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2024

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers’ trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system. metadata Alam, Shadab and Farooq, Muhammad Shoaib and Ansari, Zain Khalid and Alvi, Atif and Rustam, Furqan and Díez, Isabel De La Torre and Vidal Mazón, Juan Luis and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2024) Blockchain based transparent and reliable framework for wheat crop supply chain. PLOS ONE, 19 (1). e0295036. ISSN 1932-6203

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés Generative intelligence relies heavily on the integration of vision and language. Much of the research has focused on image captioning, which involves describing images with meaningful sentences. Typically, when generating sentences that describe the visual content, a language model and a vision encoder are commonly employed. Because of the incorporation of object areas, properties, multi-modal connections, attentive techniques, and early fusion approaches like bidirectional encoder representations from transformers (BERT), these components have experienced substantial advancements over the years. This research offers a reference to the body of literature, identifies emerging trends in an area that blends computer vision as well as natural language processing in order to maximize their complementary effects, and identifies the most significant technological improvements in architectures employed for image captioning. It also discusses various problem variants and open challenges. This comparison allows for an objective assessment of different techniques, architectures, and training strategies by identifying the most significant technical innovations, and offers valuable insights into the current landscape of image captioning research. metadata Jamil, Azhar and Rehman, Saif Ur and Mahmood, Khalid and Gracia Villar, Mónica and Prola, Thomas and Diez, Isabel De La Torre and Samad, Md Abdus and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, thomas.prola@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Engineering
Subjects > Psychology
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances. metadata Rizwan, Muhammad and Mushtaq, Muhammad Faheem and Rafiq, Maryam and Mehmood, Arif and Diez, Isabel de la Torre and Gracia Villar, Mónica and Garay, Helena and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, helena.garay@uneatlantico.es, UNSPECIFIED (2024) Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble. Computers, Materials & Continua, 78 (2). pp. 2047-2066. ISSN 1546-2226

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate. metadata Islam, Md. Milon and Shafi, Imran and Din, Sadia and Farooq, Siddique and Díez, Isabel de la Torre and Breñosa, Jose and Martínez Espinosa, Julio César and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, UNSPECIFIED (2024) Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing. PLOS ONE, 19 (3). e0298582. ISSN 1932-6203

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Retinitis pigmentosa (RP) is a group of genetic retinal disorders characterized by progressive vision loss, culminating in blindness. Identifying pigment signs (PS) linked with RP is crucial for monitoring and possibly slowing the disease’s degenerative course. However, the segmentation and detection of PS are challenging due to the difficulty of distinguishing between PS and blood vessels and the variability in size, shape, and color of PS. Recently, advances in deep learning techniques have shown impressive results in medical image analysis, especially in ophthalmology. This study presents an approach for classifying pigment marks in color fundus images of RP using a modified squeeze-and-excitation ResNet (SE-ResNet) architecture. This variant synergizes the efficiency of residual skip connections with the robust attention mechanism of the SE block to amplify feature representation. The SE-ResNet model was fine-tuned to determine the optimal layer configuration that balances performance metrics and computational costs. We trained the proposed model on the RIPS dataset, which comprises images from patients diagnosed at various RP stages. Experimental results confirm the efficacy of the proposed model in classifying different types of pigment signs associated with RP. The model yielded performance metrics, such as accuracy, sensitivity, specificity, and f-measure of 99.16%, 97.70%, 96.93%, 90.47%, 99.37%, 97.80%, 97.44%, and 90.60% on the testing set, based on GT1 & GT2 respectively. Given its performance, this model is an excellent candidate for integration into computer-aided diagnostic systems for RP, aiming to enhance patient care and vision-related healthcare services. metadata Rashid, Rubina and Aslam, Waqar and Mehmood, Arif and Ramírez-Vargas, Debora L. and Diez, Isabel De La Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, debora.ramirez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2024) A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images. IEEE Access, 12. pp. 28297-28309. ISSN 2169-3536

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Federated learning is a distributed machine-learning technique that enables multiple devices to learn a shared model while keeping their local data private. The approach poses security challenges, such as model integrity, that must be addressed to ensure the reliability of the learned models. In this context, software-defined networking (SDN) can play a crucial role in improving the security of federated learning systems; indeed, it can provide centralized control and management of network resources, enforcement of security policies, and detection and mitigation of network-level threats. The integration of SDN with federated learning can help achieve a secure and efficient distributed learning environment. In this paper, an architecture is proposed to detect attacks on Federated Learning using SDN; furthermore, the machine learning model is deployed on a number of devices for training. The simulation results are carried out using the N-BaIoT dataset and training models such as Random Forest achieves 99.6%, Decision Tree achieves 99.8%, and K-Nearest Neighbor achieves 99.3% with 20 features. metadata Babbar, Himanshi and Rani, Shalli and Singh, Aman and Gianini, Gabriele mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED (2024) Detecting Cyberattacks to Federated Learning on Software-Defined Networks. Communications in Computer and Information Science, 2022. pp. 120-132. ISSN 1865-0929

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Requirements specifications written in natural language enable us to understand a program’s intended functionality, which we can then translate into operational software. At varying stages of requirement specification, multiple ambiguities emerge. Ambiguities may appear at several levels including the syntactic, semantic, domain, lexical, and pragmatic levels. The primary objective of this study is to identify requirements’ pragmatic ambiguity. Pragmatic ambiguity occurs when the same set of circumstances can be interpreted in multiple ways. It requires consideration of the context statement of the requirements. Prior research has developed methods for obtaining concepts based on individual nodes, so there is room for improvement in the requirements interpretation procedure. This research aims to develop a more effective model for identifying pragmatic ambiguity in requirement definition. To better interpret requirements, we introduced the Concept Maximum Matching (CMM) technique, which extracts concepts based on edges. The CMM technique significantly improves precision because it permits a more accurate interpretation of requirements based on the relative weight of their edges. Obtaining an F-measure score of 0.754 as opposed to 0.563 in existing models, the evaluation results demonstrate that CMM is a substantial improvement over the previous method. metadata Aslam, Khadija and Iqbal, Faiza and Altaf, Ayesha and Hussain, Naveed and Gracia Villar, Mónica and Soriano Flores, Emmanuel and Diez, Isabel De La Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Biomedicine
Subjects > Engineering
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning. Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data. Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID. Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections. metadata Sumbul, X. and Sultana, Arshiya and Heyat, Md Belal Bin and Rahman, Khaleequr and Akhtar, Faijan and Parveen, Saba and Briones Urbano, Mercedes and Lipari, Vivian and De la Torre Díez, Isabel and Khan, Azmat Ali and Malik, Abdul mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, mercedes.briones@uneatlantico.es, vivian.lipari@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning. Frontiers in Chemistry, 12. ISSN 2296-2646

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
University of La Romana > Research > Scientific Production
Abierto Inglés Objective: This study aims to determine the efficacy of the Acacia arabica (Lam.) Willd. and Cinnamomum camphora (L.) J. Presl. vaginal suppository in addressing heavy menstrual bleeding (HMB) and their impact on participants' health-related quality of life (HRQoL) analyzed using machine learning algorithms. Method: A total of 62 participants were enrolled in a double-dummy, single-center study. They were randomly assigned to either the suppository group (SG), receiving a formulation prepared with Acacia arabica gum (Gond Babul) and camphor from Cinnamomum camphora (Kafoor) through two vaginal suppositories (each weighing 3,500 mg) for 7 days at bedtime along with oral placebo capsules, or the tranexamic group (TG), receiving oral tranexamic acid (500 mg) twice a day for 5 days and two placebo vaginal suppositories during menstruation at bedtime for three consecutive menstrual cycles. The primary outcome was the pictorial blood loss assessment chart (PBLAC) for HMB, and secondary outcomes included hemoglobin level and SF-36 HRQoL questionnaire scores. Additionally, machine learning algorithms such as k-nearest neighbor (KNN), AdaBoost (AB), naive Bayes (NB), and random forest (RF) classifiers were employed for analysis. Results: In the SG and TG, the mean PBLAC score decreased from 635.322 ± 504.23 to 67.70 ± 22.37 and 512.93 ± 283.57 to 97.96 ± 39.25, respectively, at post-intervention (TF3), demonstrating a statistically significant difference (p < 0.001). A higher percentage of participants in the SG achieved normal menstrual blood loss compared to the TG (93.5% vs 74.2%). The SG showed a considerable improvement in total SF-36 scores (73.56%) compared to the TG (65.65%), with a statistically significant difference (p < 0.001). Additionally, no serious adverse events were reported in either group. Notably, machine learning algorithms, particularly AB and KNN, demonstrated the highest accuracy within cross-validation models for both primary and secondary outcomes. Conclusion: The A. arabica and C. camphora vaginal suppository is effective, cost-effective, and safe in controlling HMB. This botanical vaginal suppository provides a novel and innovative alternative to traditional interventions, demonstrating promise as an effective management approach for HMB. metadata Fazmiya, Mohamed Joonus Aynul and Sultana, Arshiya and Heyat, Md Belal Bin and Parveen, Saba and Rahman, Khaleequr and Akhtar, Faijan and Khan, Azmat Ali and Alanazi, Amer M. and Ahmed, Zaheer and Díez, Isabel de la Torre and Brito Ballester, Julién and Saripalli, Tirumala Santhosh Kumar mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, julien.brito@uneatlantico.es, UNSPECIFIED (2024) Efficacy of a vaginal suppository formulation prepared with Acacia arabica (Lam.) Willd. gum and Cinnamomum camphora (L.) J. Presl. in heavy menstrual bleeding analyzed using a machine learning technique. Frontiers in Pharmacology, 15. ISSN 1663-9812

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff. metadata Mujahid, Muhammad and Rustam, Furqan and Shafique, Rahman and Caro Montero, Elizabeth and Silva Alvarado, Eduardo René and de la Torre Diez, Isabel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED, UNSPECIFIED (2024) Efficient deep learning-based approach for malaria detection using red blood cell smears. Scientific Reports, 14 (1). ISSN 2045-2322

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research. metadata Shaha, Tumpa Rani and Begum, Momotaz and Uddin, Jia and Yélamos Torres, Vanessa and Alemany Iturriaga, Josep and Ashraf, Imran and Samad, Md. Abdus mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vanessa.yelamos@funiber.org, josep.alemany@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms. BMC Medical Research Methodology, 24 (1). ISSN 1471-2288

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Much of nutrition research has been conventionally based on the use of simplistic in vitro systems or animal models, which have been extensively employed in an effort to better understand the relationships between diet and complex diseases as well as to evaluate food safety. Although these models have undeniably contributed to increase our mechanistic understanding of basic biological processes, they do not adequately model complex human physiopathological phenomena, creating concerns about the translatability to humans. During the last decade, extraordinary advancement in stem cell culturing, three-dimensional cell cultures, sequencing technologies, and computer science has occurred, which has originated a wealth of novel human-based and more physiologically relevant tools. These tools, also known as “new approach methodologies,” which comprise patient-derived organoids, organs-on-chip, multi-omics approach, along with computational models and analysis, represent innovative and exciting tools to forward nutrition research from a human-biology-oriented perspective. After considering some shortcomings of conventional in vitro and vivo approaches, here we describe the main novel available and emerging tools that are appropriate for designing a more human-relevant nutrition research. Our aim is to encourage discussion on the opportunity to explore innovative paths in nutrition research and to promote a paradigm-change toward a more human biology-focused approach to better understand human nutritional pathophysiology, to evaluate novel food products, and to develop more effective targeted preventive or therapeutic strategies while helping in reducing the number and replacing animals employed in nutrition research. metadata Cassotta, Manuela and Cianciosi, Danila and Elexpuru Zabaleta, Maria and Elío Pascual, Iñaki and Sumalla Cano, Sandra and Giampieri, Francesca and Battino, Maurizio mail manucassotta@gmail.com, UNSPECIFIED, maria.elexpuru@uneatlantico.es, inaki.elio@uneatlantico.es, sandra.sumalla@uneatlantico.es, francesca.giampieri@uneatlantico.es, maurizio.battino@uneatlantico.es (2024) Human‐based new approach methodologies to accelerate advances in nutrition research. Food Frontiers. pp. 1-32. ISSN 2643-8429

Article Subjects > Teaching Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria. metadata Alemany Iturriaga, Josep and Velarde-Sotres, Álvaro and Jorge, Javier and Giglio, Kamil mail josep.alemany@uneatlantico.es, alvaro.velarde@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria. Cogent Education, 11 (1). ISSN 2331-186X

Article Subjects > Biomedicine
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés Isoflavones are a group of (poly)phenols, also defined as phytoestrogens, with chemical structures comparable with estrogen, that exert weak estrogenic effects. These phytochemical compounds have been targeted for their proven antioxidant and protective effects. Recognizing the increasing prevalence of cardiovascular diseases (CVD), there is a growing interest in understanding the potential cardiovascular benefits associated with these phytochemical compounds. Gut microbiota may play a key role in mediating the effects of isoflavones on vascular and endothelial functions, as it is directly implicated in isoflavones metabolism. The findings from randomized clinical trials indicate that isoflavone supplementation may exert putative effects on vascular biomarkers among healthy individuals, but not among patients affected by cardiometabolic disorders. These results might be explained by the enzymatic transformation to which isoflavones are subjected by the gut microbiota, suggesting that a diverse composition of the microbiota may determine the diverse bioavailability of these compounds. Specifically, the conversion of isoflavones in equol—a microbiota-derived metabolite—seems to differ between individuals. Further studies are needed to clarify the intricate molecular mechanisms behind these contrasting results. metadata Laudani, Samuele and Godos, Justyna and Romano, Giovanni Luca and Gozzo, Lucia and Di Domenico, Federica Martina and Dominguez Azpíroz, Irma and Martínez Díaz, Raquel and Giampieri, Francesca and Quiles, José L. and Battino, Maurizio and Drago, Filippo and Galvano, Fabio and Grosso, Giuseppe mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, maurizio.battino@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved? Pharmaceuticals, 17 (2). p. 236. ISSN 1424-8247

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés The prevalence of sleep disorders, characterized by issues with quality, timing, and sleep duration is increasing globally. Among modifiable risk factors, diet quality has been suggested to influence sleep features. The Mediterranean diet is considered a landmark dietary pattern in terms of quality and effects on human health. However, dietary habits characterized by this cultural heritage should also be considered in the context of overall lifestyle behaviors, including sleep habits. This study aimed to systematically revise the literature relating to adherence to the Mediterranean diet and sleep features in observational studies. The systematic review comprised 23 reports describing the relation between adherence to the Mediterranean diet and different sleep features, including sleep quality, sleep duration, daytime sleepiness, and insomnia symptoms. The majority of the included studies were conducted in the Mediterranean basin and reported a significant association between a higher adherence to the Mediterranean diet and a lower likelihood of having poor sleep quality, inadequate sleep duration, excessive daytime sleepiness or symptoms of insomnia. Interestingly, additional studies conducted outside the Mediterranean basin showed a relationship between the adoption of a Mediterranean-type diet and sleep quality, suggesting that biological mechanisms sustaining such an association may exist. In conclusion, current evidence suggests a relationship between adhering to the Mediterranean diet and overall sleep quality and different sleep parameters. The plausible bidirectional association should be further investigated to understand whether the promotion of a healthy diet could be used as a tool to improve sleep quality. metadata Godos, Justyna and Ferri, Raffaele and Lanza, Giuseppe and Caraci, Filippo and Rojas Vistorte, Angel Olider and Yélamos Torres, Vanessa and Grosso, Giuseppe and Castellano, Sabrina mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, angel.rojas@uneatlantico.es, vanessa.yelamos@funiber.org, UNSPECIFIED, UNSPECIFIED (2024) Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence. Nutrients, 16 (2). p. 282. ISSN 2072-6643

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Pneumonia is a potentially life-threatening infectious disease that is typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds or lung biopsies. Accurate diagnosis is crucial as wrong diagnosis, inadequate treatment or lack of treatment can cause serious consequences for patients and may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts in diagnosing pneumonia by assisting in their decision-making process. By leveraging deep learning models, healthcare professionals can enhance diagnostic accuracy and make informed treatment decisions for patients suspected of having pneumonia. In this study, six deep learning models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L are implemented and evaluated. The study also incorporates the Adam optimizer, which effectively adjusts the epoch for all the models. The models are trained on a dataset of 5856 chest X-ray images and show 87.78%, 88.94%, 90.7%, 91.66%, 87.98% and 94.02% accuracy for CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates the highest accuracy and proves its robustness for pneumonia detection. These findings highlight the potential of deep learning models in accurately detecting and predicting pneumonia based on chest X-ray images, providing valuable support in clinical decision-making and improving patient treatment. metadata Ali, Mudasir and Shahroz, Mobeen and Akram, Urooj and Mushtaq, Muhammad Faheem and Carvajal-Altamiranda, Stefanía and Aparicio Obregón, Silvia and Díez, Isabel De La Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, stefania.carvajal@uneatlantico.es, silvia.aparicio@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model. IEEE Access, 12. pp. 34691-34707. ISSN 2169-3536

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés The purpose of the study is to assess the risk of developing general eating disorders (ED), anorexia nervosa (AN), and bulimia nervosa (BN), as well as to examine the effects of gender, academic year, place of residence, faculty, and diet quality on that risk. Over two academic years, 129 first- and fourth-year Uneatlántico students were included in an observational descriptive study. The self-administered tests SCOFF, EAT-26, and BITE were used to determine the participants’ risk of developing ED. The degree of adherence to the Mediterranean diet (MD) was used to evaluate the quality of the diet. Data were collected at the beginning (T1) and at the end (T2) of the academic year. The main results were that at T1, 34.9% of participants were at risk of developing general ED, AN 3.9%, and BN 16.3%. At T2, these percentages were 37.2%, 14.7%, and 8.5%, respectively. At T2, the frequency of general ED in the female group was 2.5 times higher (OR: 2.55, 95% CI: 1.22–5.32, p = 0.012). The low-moderate adherence to the MD students’ group was 0.92 times less frequent than general ED at T2 (OR: 0.921, 95%CI: 0.385–2.20, p < 0.001). The most significant risk factor for developing ED is being a female in the first year of university. Moreover, it appears that the likelihood of developing ED generally increases during the academic year. metadata Eguren García, Imanol and Sumalla Cano, Sandra and Conde González, Sandra and Vila-Martí, Anna and Briones Urbano, Mercedes and Martínez Díaz, Raquel and Elío Pascual, Iñaki mail imanol.eguren@uneatlantico.es, sandra.sumalla@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, mercedes.briones@uneatlantico.es, raquel.martinez@uneatlantico.es, inaki.elio@uneatlantico.es (2024) Risk Factors for Eating Disorders in University Students: The RUNEAT Study. Healthcare, 12 (9). p. 942. ISSN 2227-9032

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Over the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet. metadata Godos, Justyna and Scazzina, Francesca and Paternò Castello, Corrado and Giampieri, Francesca and Quiles, José L. and Briones Urbano, Mercedes and Battino, Maurizio and Galvano, Fabio and Iacoviello, Licia and de Gaetano, Giovanni and Bonaccio, Marialaura and Grosso, Giuseppe mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, mercedes.briones@uneatlantico.es, maurizio.battino@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet. Journal of Translational Medicine, 22 (1). ISSN 1479-5876

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies. metadata Khan, Hikmat Ullah and Anam, Rimsha and Anwar, Muhammad Waqas and Jamal, Muhammad Hasan and Bajwa, Usama Ijaz and Diez, Isabel de la Torre and Silva Alvarado, Eduardo René and Soriano Flores, Emmanuel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.silva@funiber.org, emmanuel.soriano@uneatlantico.es, UNSPECIFIED (2024) A deep learning approach for Named Entity Recognition in Urdu language. PLOS ONE, 19 (3). e0300725. ISSN 1932-6203

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages. metadata Kader, Mohammed Abdul and Ullah, Muhammad Ahsan and Islam, Md Saiful and Ferriol Sánchez, Fermín and Samad, Md Abdus and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, fermin.ferriol@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2024) A real-time air-writing model to recognize Bengali characters. AIMS Mathematics, 9 (3). pp. 6668-6698. ISSN 2473-6988

2023

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO. metadata Khan, Arooj and Shafi, Imran and Khawaja, Sajid Gul and de la Torre Díez, Isabel and López Flores, Miguel Ángel and Castanedo Galán, Juan and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, miguelangel.lopez@uneatlantico.es, juan.castanedo@uneatlantico.es, UNSPECIFIED (2023) Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants. Sensors, 23 (18). p. 7710. ISSN 1424-8220

Article Subjects > Engineering
Subjects > Comunication
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people’s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author’s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%. metadata R, Sudheesh and Mujahid, Muhammad and Rustam, Furqan and Shafique, Rahman and Chunduri, Venkata and Gracia Villar, Mónica and Brito Ballester, Julién and Diez, Isabel de la Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, julien.brito@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach. Information, 14 (9). p. 474. ISSN 2078-2489

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Diets enriched in plant-based foods are associated with the maintenance of a good well-being and with the prevention of many non-communicable diseases. The health effects of fruits and vegetables consumption are mainly due to the presence of micronutrients, including vitamins and minerals, and polyphenols, plant secondary metabolites. One of the most important classes of phenolic compounds are anthocyanins, that confer the typical purple-red color to many foods, such as berries, peaches, plums, red onions, purple corn, eggplants, as well as purple carrots, sweet potatoes and red cabbages, among others. This commentary aims to briefly highlight the progress made by science in the last years, focusing on some unexpected aspects related with anthocyanins, such as their bioavailability, their health effects and their relationship with gut microbiota metadata Giampieri, Francesca and Cianciosi, Danila and Alvarez-Suarez, José M. and Quiles, José L. and Forbes-Hernández, Tamara Y. and Navarro-Hortal, María D. and Machì, Michele and Pali-Casanova, Ramón and Martínez Espinosa, Julio César and Chen, Xiumin and Zhang, Di and Bai, Weibin and Lingmin, Tian and Mezzetti, Bruno and Battino, Maurizio and Diaz, Yasmany Armas mail francesca.giampieri@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, jose.quiles@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ramon.pali@unini.edu.mx, ulio.martinez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, maurizio.battino@uneatlantico.es, UNSPECIFIED (2023) Anthocyanins: what do we know until now? Journal of Berry Research. pp. 1-6. ISSN 18785093

Article Subjects > Teaching Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés, Español Los programas educativos cada vez más se inclinan a la potenciación de valores que favorezcan el desarrollo integral de los educandos, para ello se implementan diversas fórmulas que pretenden desde lo metodológico ajustarse a las exigencias sociales, educativas y curriculares. En este acercamiento a la formación del Bachiller Ecuatoriano, se analizan sus principios legales, lineamientos curriculares y estándares de calidad educativa enfocado al cumplimiento del perfil de salida del bachillerato, así como la percepción de estos por parte de estudiantes y docentes de la Unidad Educativa del Milenio Manuel J. Calle de la ciudad de Cuenca, a partir de aquí se propone una estrategia de mejora con el uso del Método de Aprendizaje Basado en Proyectos (ABP), aplicada en una muestra de 92 estudiantes del 2do año del Bachillerato General Unificado (BGU), quienes cursaron el Programa de Participación Estudiantil (PPE), específicamente el PPE (2017-2018), cuyos resultados evidencian que el Método ABP empleado en el PPE caso de estudio contribuye significativamente a elevar la calidad del Perfil de Salida del Bachiller (PSB) por medio del desarrollo de habilidades para la vida. El Método de Aprendizaje Basado en Proyectos ABP es una alternativa adecuada para elevar el proceso formativo del país, a la vez facilita la convivencia armónica en el marco escolar para quienes la utilizan directa e indirectamente. metadata Orúe López, Amalia Beatriz and Martínez Sierra, Ricel and Jara Quito, Daysi Margoth mail UNSPECIFIED, ricel.martinez@unini.org, daysi.jara@doctorado.unini.edu.mx (2023) Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos. MLS Educational Research, 7 (1). ISSN 2603-5820

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented. metadata Salinari, Alessia and Machì, Michele and Armas Diaz, Yasmany and Cianciosi, Danila and Qi, Zexiu and Yang, Bei and Ferreiro Cotorruelo, Maria Soledad and Gracia Villar, Santos and Dzul López, Luis Alonso and Battino, Maurizio and Giampieri, Francesca mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2023) The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases, 11 (3). p. 97. ISSN 2079-9721

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage. metadata Siddiqui, Hafeez-Ur-Rehman and Raza, Ali and Saleem, Adil Ali and Rustam, Furqan and Díez, Isabel de la Torre and Gavilanes Aray, Daniel and Lipari, Vivian and Ashraf, Imran and Dudley, Sandra mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, vivian.lipari@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features. Diagnostics, 13 (6). p. 1096. ISSN 2075-4418

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation. metadata Shafi, Imran and Khan, Harris and Farooq, Muhammad Siddique and Diez, Isabel de la Torre and Miró Vera, Yini Airet and Castanedo Galán, Juan and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, yini.miro@uneatlantico.es, juan.castanedo@uneatlantico.es, UNSPECIFIED (2023) An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation. Energies, 16 (10). p. 4171. ISSN 1996-1073

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Project-based organizations need to procure different commodities, and the failure/success of a project depends heavily on procurement management. Companies must refine and develop methods to simplify and optimize the procurement process in a highly competitive environment. This paper presents a methodology to help managers of project-based organizations analyze procurement processes to determine the optimal framework for simultaneously addressing multiple objectives. These goals include minimizing the time between the generation and required approval for a purchase, identifying unnamed activities, and allocating the budget efficiently. In this paper, we apply process mining algorithms to a dataset consisting of event logs on Oracle Financials-based enterprise resource planning (ERP) procurement processes in ERP systems and demonstrate interesting results leading to project procurement intelligence (PPI). The provided log data is the real-life data consisting of 180,462 events referring to seven activities within 43,101 cases. The logged procurement processes are filtered and analyzed using the open-source process mining frameworks PrOM and Disco. As a result of the process mining activities, a simulation of the discovered process model derived from the event log of the entire procurement process is presented, and the most frequent potential behaviors are identified. This analysis and extraction of frequent processes from corporate event logs help organizations understand, adapt, and redesign procurement operations and, most importantly, make them more efficient and of higher quality. This study shows that after the successful formulation of guiding principles, data refinement, and process structure optimization, the case study results are considered significant by the organization’s management. metadata Butt, Naveed Anwer and Mahmood, Zafar and Sana, Muhammad Usman and Díez, Isabel de la Torre and Castanedo Galán, Juan and Brie, Santiago and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juan.castanedo@uneatlantico.es, santiago.brie@uneatlantico.es, UNSPECIFIED (2023) Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach. Applied Sciences, 13 (7). p. 4145. ISSN 2076-3417

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Betalains are water-soluble, nitrogen-containing vacuolar pigment and can be divided into two subclasses: the yellow – orange betaxanthins and the red – violet betacyanin. These pigments can be found mainly in Latin America, but also in some parts of Asia, Africa, Australia and in the Mediterranean area. In this work an overview related with the status of research about betalains extracted from Opuntia spp and the enforces made to evaluate their positive incidence in the human body is provided. Several studies enhance their anticancer, anti-inflammatory and antioxidant properties. They also exhibit antimicrobial and antidiabetic effect. Taking into account these properties, betalains seem to be a promising natural alternative as a colorant to replace the synthetic ones in the food additive industry. In addition, the use of Opuntia spp fruits as possible colorant sources in the Food Industry, may contribute positively to the sustainable development in semi-arid regions. metadata Armas Diaz, Yasmany and Qi, Zexiu and Yang, Bei and Martínez López, Nohora Milena and Briones Urbano, Mercedes and Cianciosi, Danila mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, nohora.martinez@uneatlantico.es, mercedes.briones@uneatlantico.es, UNSPECIFIED (2023) Betalains: The main bioactive compounds of Opuntia spp and their possible health benefits in the Mediterranean diet. Mediterranean Journal of Nutrition and Metabolism, 16 (3). pp. 181-190. ISSN 1973798X

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes. metadata Iqbal, Faiza and Altaf, Ayesha and Waris, Zeest and Gavilanes Aray, Daniel and López Flores, Miguel Ángel and Díez, Isabel de la Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, miguelangel.lopez@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction. Sensors, 23 (11). p. 5263. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Breast cancer is prevalent in women and the second leading cause of death. Conventional breast cancer detection methods require several laboratory tests and medical experts. Automated breast cancer detection is thus very important for timely treatment. This study explores the influence of various feature selection technique to increase the performance of machine learning methods for breast cancer detection. Experimental results shows that use of appropriate features tend to show highly accurate prediction metadata Shafique, Rahman and Rustam, Furqan and Choi, Gyu Sang and Díez, Isabel de la Torre and Mahmood, Arif and Lipari, Vivian and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2023) Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning. Cancers, 15 (3). p. 681. ISSN 2072-6694

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Alpha-linolenic acid (ALA) is a long-chain polyunsaturated essential fatty acid of the Ω3 series found mainly in vegetables, especially in the fatty part of oilseeds, dried fruit, berries, and legumes. It is very popular for its preventive use in several diseases: It seems to reduce the risk of the onset or decrease some phenomena related to inflammation, oxidative stress, and conditions of dysregulation of the immune response. Recent studies have confirmed these unhealthy situations also in patients with severe coronavirus disease 2019 (COVID-19). Different findings (in vitro, in vivo, and clinical ones), summarized and analyzed in this review, have showed an important role of ALA in other various non-COVID physiological and pathological situations against “cytokines storm,” chemokines secretion, oxidative stress, and dysregulation of immune cells that are also involved in the infection of the 2019 novel coronavirus. According to the effects of ALA against all the aforementioned situations (also present in patients with a severe clinical picture of severe acute respiratory syndrome-(CoV-2) infection), there may be the biologic plausibility of a prophylactic effect of this compound against COVID-19 symptoms and fatality. metadata Cianciosi, Danila and Diaz, Yasmany Armas and Gaddi, Antonio Vittorino and Capello, Fabio and Savo, Maria Teresa and Pali-Casanova, Ramón and Martínez Espinosa, Julio César and Pascual Barrera, Alina Eugenia and Navarro‐Hortal, Maria‐Dolores and Tian, Lingmin and Bai, Weibin and Giampieri, Francesca and Battino, Maurizio mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ramon.pali@unini.edu.mx, ulio.martinez@unini.edu.mx, alina.pascual@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, francesca.giampieri@uneatlantico.es, UNSPECIFIED (2023) Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection? Food Frontiers. ISSN 2643-8429

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Manuka honey, which is rich in pinocembrin, quercetin, naringenin, salicylic, p-coumaric, ferulic, syringic and 3,4-dihydroxybenzoic acids, has been shown to have pleiotropic effects against colon cancer cells. In this study, potential chemosensitizing effects of Manuka honey against 5-Fluorouracil were investigated in colonspheres enriched with cancer stem cells (CSCs), which are responsible for chemoresistance. Results showed that 5-Fluorouracil increased when it was combined with Manuka honey by downregulating the gene expression of both ATP-binding cassette sub-family G member 2, an efflux pump and thymidylate synthase, the main target of 5-Fluorouracil which regulates the ex novo DNA synthesis. Manuka honey was associated with decreased self-renewal ability by CSCs, regulating expression of several genes in Wnt/β-catenin, Hedgehog and Notch pathways. This preliminary study opens new areas of research into the effects of natural compounds in combination with pharmaceuticals and, potentially, increase efficacy or reduce adverse effects. metadata Cianciosi, Danila and Armas Diaz, Yasmany and Alvarez-Suarez, José M. and Chen, Xiumin and Zhang, Di and Martínez López, Nohora Milena and Briones Urbano, Mercedes and Quiles, José L. and Amici, Adolfo and Battino, Maurizio and Giampieri, Francesca mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, nohora.martinez@uneatlantico.es, mercedes.briones@uneatlantico.es, jose.quiles@uneatlantico.es, UNSPECIFIED, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2023) Can the phenolic compounds of Manuka honey chemosensitize colon cancer stem cells? A deep insight into the effect on chemoresistance and self-renewal. Food Chemistry, 427. p. 136684. ISSN 03088146

Article Subjects > Biomedicine Ibero-american International University > Research > Articles and books Abierto Inglés Background The negative effects of COVID-19 infections during pregnancy have been amply described, however, the persistent sequels of this infection have not been explored so far. Objective The aim of this study was to describe persisting symptoms after COVID-19 infection in pregnant and non-pregnant women in Ecuador. Methods A cross-sectional analysis based on an online, self-reporting questionnaire was conducted in Ecuador from April to July 2022. Participants were invited by social media, radio, and TV to voluntarily participate in our study. A total of 457 surveys were included in this study. We compared risk factor variables and long-term persisting symptoms of pregnant and non-pregnant women in Ecuador. Results Overall, 247 (54.1 %) responders claimed to have long-term symptoms after SARS-CoV-2 infection. Most of these symptoms were reported by non-pregnant women (94.0 %). The most common Long-COVID symptoms in pregnant women were fatigue (10.6 %), hair loss (9.6 %), and difficulty concentrating (6.2 %). We found that pregnant women who smoked had a higher risk of suffering fatigue. Conclusions The most frequent Long-COVID symptoms in pregnant women were fatigue, hair loss, and difficulty concentrating. Apparently, the patterns of presentation of long-term sequelae of SARS-CoV-2 infection in pregnant women do not differ significantly from reports available from studies in the general population. metadata Vásconez-González, Jorge and Fernandez-Naranjo, Raul and Izquierdo Condoy, Juan Sebastian and Delgado-Moreira, Karen and Cordovez, Simone and Tello-De-la-Torre, Andrea and Paz, Clara and Castillo, Diana and Izquierdo-Condoy, Nathaly and Carrington, Sarah J. and Ortiz-Prado, Esteban mail UNSPECIFIED (2023) Comparative analysis of long-term self-reported COVID-19 symptoms among pregnant women. Journal of Infection and Public Health, 16 (3). pp. 430-440. ISSN 18760341

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés UNSPECIFIED metadata Ali, Omer and Abbas, Qamar and Mahmood, Khalid and Bautista Thompson, Ernesto and Arambarri, Jon and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ernesto.bautista@unini.edu.mx, jon.arambarri@uneatlantico.es, UNSPECIFIED (2023) Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems. Mathematics, 11 (21). p. 4406. ISSN 2227-7390

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues. metadata Shafi, Imran and Fatima, Anum and Afzal, Hammad and Díez, Isabel de la Torre and Lipari, Vivian and Breñosa, Jose and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, UNSPECIFIED (2023) A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics, 13 (13). p. 2196. ISSN 2075-4418

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models metadata Hafeez, Rabab and Anwar, Muhammad Waqas and Jamal, Muhammad Hasan and Fatima, Tayyaba and Martínez Espinosa, Julio César and Dzul López, Luis Alonso and Bautista Thompson, Ernesto and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ulio.martinez@unini.edu.mx, luis.dzul@uneatlantico.es, ernesto.bautista@unini.edu.mx, UNSPECIFIED (2023) Contextual Urdu Lemmatization Using Recurrent Neural Network Models. Mathematics, 11 (2). p. 435. ISSN 2227-7390

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés This paper presents a current- and voltage-driven protection scheme for transmission lines based on a hybrid mix of Stockwell transform (ST) and Hilbert transform (HT). Use of both current and voltage waveforms to detect and categorize faults, improves the reliability of this protection scheme and avoids false tripping. Current and voltage waveforms captured during a period of fault are analyzed using ST to compute a median intermediate fault index (MIFI), a maximum value intermediate fault index (MVFI), and a summation intermediate fault index (SIFI). Current and voltage signals are analyzed via applying HT to compute a Hilbert fault index (HFI). The proposed hybrid current and voltage fault index (HCVFI) is obtained from the MIFI, MVFI, SIFI, and HFI. A threshold magnitude for this hybrid current and voltage fault index (HCVFITH) is set to 500 to identify the faulty phase. The HCVFIT is selected after testing the method for various conditions of different fault locations, different fault impedances, different fault occurrence angles, and reverse flows of power. Fault classification is performed using the number of faulty phases and an index for ground detection (IGD). The ground involved in a fault is detected by comparison of peak IGD magnitude with a threshold for ground detection (THGD). THGD is considered equal to 1000 in this study. The study is carried out using a two-terminal transmission line modeled in MATLAB software. The performance of the proposed technique is better compared to a discrete wavelet transform (DWT)-based technique, a time–frequency approach, and an alienation method. Our algorithm effectively detected an AG fault, observed on a practical transmission line. metadata Tang, Ligang and Mahela, Om Prakash and Khan, Baseem and Miró Vera, Yini Airet mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, yini.miro@uneatlantico.es (2023) Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques. Sustainability, 15 (7). p. 5715. ISSN 2071-1050

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region-based network (RPN) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches metadata Fatima, Anum and Shafi, Imran and Afzal, Hammad and Mahmood, Khawar and Díez, Isabel de la Torre and Lipari, Vivian and Brito Ballester, Julién and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, julien.brito@uneatlantico.es, UNSPECIFIED (2023) Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection. Healthcare, 11 (3). p. 347. ISSN 2227-9032

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively. metadata Shafi, Imran and Mazhar, Muhammad Fawad and Fatima, Anum and Álvarez, Roberto Marcelo and Miró Vera, Yini Airet and Martínez Espinosa, Julio César and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, ulio.martinez@unini.edu.mx, UNSPECIFIED (2023) Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance. Drones, 7 (1). p. 31. ISSN 2504-446X

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Given that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants which can occasionally cause crop losses ranging from 30 to 60%. This manuscript proposed a Convolutional Neural Network (CNN) and Visual Geometry Group (VGG)19 i.e. CNN-VGG19 model with a transfer learning-based method for the precise identification and classification of rice leaf diseases. This scheme employs a transfer learning technique based on the VGG19 which can identify the brown spot class. The accuracy is 93.0% in the deployment of the dataset of rice leaf disease. The other parameters are sensitivity, specificity, precision and F1-score with 89.9%, 94.7%, 92.4% and 90.5% respectively. The developed technique obtained better results as compared to the existing baseline models. metadata Dogra, Roopali and Rani, Shalli and Singh, Aman and Albahar, Marwan Ali and Pascual Barrera, Alina Eugenia and Alkhayyat, Ahmed mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, alina.pascual@unini.edu.mx, UNSPECIFIED (2023) Deep learning model for detection of brown spot rice leaf disease with smart agriculture. Computers and Electrical Engineering, 109. p. 108659. ISSN 00457906

Article Subjects > Social Sciences Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés This research aims to gather opinions from experts in the European tourism sector regarding training needs to address severe crises, such as Covid, in Small and Medium-Sized Enterprises (SMEs) across five countries: Spain, Iceland, Ireland, Scotland, and Germany. This study was conducted within the scope of the European TC-NAV project, which is funded by the European Union. The ultimate goal of this project is to develop training solutions for European SMEs Most existing literature on tourism crises primarily examines the impact on destinations as a whole rather than on individual tourism enterprises. Thus, this research is both relevant and timely The methodology employed was qualitative, and data being collected using a 9-question interview guide. This guide underwent validation by experts, achieving a Cronbach's Alpha value of 0.7. In total, 30 individuals were interviewed: 5 civil servants, 9 company directors, 5 university professors, 6 researchers, and 5 entrepreneurs. Some notable findings include the importance of innovation for change, promoting sustainable tourism, fostering informal partnerships among regional companies, the essential role of government support, the benefits of flexible planning and service digitisation, and the ongoing need for training and upskilling. metadata Soriano Flores, Emmanuel and Prola, Thomas and Halldórsdóttir, Íris Hrund Halldórsdóttir and Taylor, Steve mail emmanuel.soriano@uneatlantico.es, thomas.prola@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Diagnosing Training Needs in European Tourism SMEs: The TC-NAV Project for Managing and Overcoming Virulent Crises. Kurdish Studies, 11 (2). pp. 2011-2022. ISSN 2051-4883

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM. metadata Ramzan, Mahrukh and Shoaib, Muhammad and Altaf, Ayesha and Arshad, Shazia and Iqbal, Faiza and Kuc Castilla, Ángel Gabriel and Ashraf, Imran mail UNSPECIFIED (2023) Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm. Sensors, 23 (20). p. 8642. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver’s impairment level by monitoring the driver’s blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model. metadata Farooq, Hamza and Altaf, Ayesha and Iqbal, Faiza and Castanedo Galán, Juan and Gavilanes Aray, Daniel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juan.castanedo@uneatlantico.es, daniel.gavilanes@uneatlantico.es, UNSPECIFIED (2023) DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents. Sensors, 23 (12). p. 5388. ISSN 1424-8220

Article Subjects > Teaching
Subjects > Comunication
Subjects > Psychology
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Communication professionals are experiencing a growing level of exposure to traumatic events as a result of their involvement in the coverage of various tragedies, including accidents, climatic disasters, rights violations, and acts of terrorism. However, it is worth noting that journalism and communication university courses often lack comprehensive instruction on effectively managing emotional challenges, anxiety, trauma, self-care, and the prevention of vicarious trauma. The objective of this study is to assess the inclusion of emotional management within the curricula of Journalism and Communication programmes offered by two universities in Catalonia, namely the University of Barcelona and the Autonomous University of Barcelona. In order to accomplish this objective, a series of semi-structured interviews were carried out with a total of twelve (12) professors who specialise in the fields of Journalism and Communication. Additionally, a thorough analysis was conducted on a set of 97 study plan guides. The results indicate that none of the participants in the interviews possess knowledge regarding any existing training programmes focused on emotional management. Furthermore, they unanimously agree on the importance of implementing such courses. The study plans did not include any subjects that were specifically dedicated to the topic of emotional management. This study presents a set of strategies aimed at creating a cross-disciplinary teaching-learning model that offers a comprehensive educational experience for students. This entails integrating precise subject matter on the previously mentioned topics, fostering critical contemplation and discourse regarding emotions within the educational setting, and advocating for ethical and sound professional behaviours. metadata Escudero, Carolina and Prola, Thomas and Fraga, Leticia and Soriano Flores, Emmanuel mail UNSPECIFIED, thomas.prola@uneatlantico.es, leticia.fraga@uneatlantico.es, emmanuel.soriano@uneatlantico.es (2023) Emotional Management in Journalism and Communication Studies. Social Space, 23 (2). pp. 507-534.

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions. metadata Siddiqui, Hafeez Ur Rehman and Saleem, Adil Ali and Raza, Muhammad Amjad and Gracia Villar, Santos and Dzul Lopez, Luis and Diez, Isabel de la Torre and Rustam, Furqan and Dudley, Sandra mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence. Diagnostics, 13 (18). p. 2881. ISSN 2075-4418

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality. metadata Siddiqui, Hafeez Ur Rehman and Younas, Faizan and Rustam, Furqan and Soriano Flores, Emmanuel and Brito Ballester, Julién and Diez, Isabel de la Torre and Dudley, Sandra and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, emmanuel.soriano@uneatlantico.es, julien.brito@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning. Sensors, 23 (15). p. 6839. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Classification is a commonly used technique in data mining and is applied in various fields such as sentiment analysis, fraud detection, and fault diagnosis. Multiclass classification, which involves more than two classes, is more complex than binary classification. There are mainly two ways to approach multiclass classification, one is to expand the binary classifier into a multiclass classifier through various strategies and the other is to divide the multiclass classification problem into multiple binary problems (binarization). Two popular approaches for binarization are One vs One (OvO) and One vs All (OvA). It is simpler to aggregate the outputs of all binary classifiers as the number of classifiers decreases. However, it causes an imbalance of positive and negative sample numbers, which affects the classification effect of each binary classifier. In this article, we contribute to the field of ensemble learning and multi-class classification by proposing a new method called Ensemble Partition Sampling (EPS). This article presents a new approach to multiclass classification using an "Ensemble Partition Sampling" method within the "one-vs-all" (OvA) framework. The primary goal of this method is to tackle the problem of data imbalance by incorporating ensemble learning and preprocessing techniques into each binary dataset. The study found that Ensemble Partition Sampling (EPS) is the most effective method for imbalanced and multiclass imbalanced classification, outperforming other methods including OvA, SMOTE, k-means-SMOTE, Bagging-RB, DES-MI, OvO-EASY, and OvO-SMB. The study used CART, Random Forest, and SVM as classifiers, and the results consistently showed that EPS outperformed all other algorithms. The findings suggest that EPS is a highly effective method for improving classification performance in imbalanced and multiclass imbalanced datasets. metadata Jabir, Brahim and Díez, Isabel De la Torre and Bautista Thompson, Ernesto and Ramírez-Vargas, Debora L. and Kuc Castilla, Ángel Gabriel mail UNSPECIFIED (2023) Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Engineering
Subjects > Teaching
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The purpose of this research article was to contrast the benefits of the optimal probability threshold adjustment technique with other imbalanced data processing techniques, in its application to the prediction of post-graduate students’ late dropout from distance learning courses in two universities in the Ibero-American space. In this context, the optimization of the Logistic Regression, Random Forest, and Neural Network classifiers, together with different techniques, attributes, and algorithms (Hyperparameters, SMOTE, SMOTE_SVM, and ADASYN) resulted in a set of metrics for decision-making, prioritizing the reduction of false negatives. The best model was the Neural Network model in combination with SMOTE_SVM, obtaining a recall index of 0.75 and an f1-Score of 0.60. Likewise, the robustness of the Random Forest classifier for imbalanced data was demonstrated by achieving, with an optimal threshold of 0.427, very similar metrics to those obtained by the consensus of the three best models found. This demonstrates that, for Random Forest, the optimal prediction probability threshold is an excellent alternative to resampling techniques with different optimal thresholds. Finally, it is hoped that this research paper will contribute to boost the application of this simple but powerful technique, which is highly underrated with respect to data resampling techniques for imbalanced data. metadata Rodríguez Velasco, Carmen Lilí and García Villena, Eduardo and Brito Ballester, Julién and Durántez Prados, Frigdiano Álvaro and Silva Alvarado, Eduardo René and Crespo Álvarez, Jorge mail carmen.rodriguez@uneatlantico.es, eduardo.garcia@uneatlantico.es, julien.brito@uneatlantico.es, durantez@uneatlantico.es, eduardo.silva@funiber.org, jorge.crespo@uneatlantico.es (2023) Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data. International Journal of Emerging Technologies in Learning (iJET), 18 (04). pp. 120-155. ISSN 1863-0383

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés In the Internet of things (IoT), data packets are accumulated and disseminated across IoT devices without human intervention, therefore the privacy and security of sensitive data during transmission are crucial. For this purpose, multiple routing techniques exist to ensure security and privacy in IoT Systems. One such technique is the routing protocol for low power and lossy networks (RPL) which is an IPv6 protocol commonly used for routing in IoT systems. Formal modeling of an IoT system can validate the reliability, accuracy, and consistency of the system. This paper presents the formal modeling of RPL protocol and the analysis of its security schemes using colored Petri nets that applies formal validation and verification for both the secure and non-secure modes of RPL protocol. The proposed approach can also be useful for formal modeling-based verification of the security of the other communication protocols. metadata Balfaqih, Mohammed and Ahmad, Farooq and Chaudhry, Muhammad Tayyab and Jamal, Muhammad Hasan and Sohail, Muhammad Amar and Gavilanes Aray, Daniel and Masías Vergara, Manuel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, manuel.masias@uneatlantico.es, UNSPECIFIED (2023) Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets. PLOS ONE, 18 (8). e0285700. ISSN 1932-6203

Article Subjects > Teaching Ibero-american International University > Research > Articles and books Abierto Inglés Background Radiology is a useful tool for diagnosis and intervention in medical practice, and all the components within the teaching-learning process of this subject during undergraduate studies influence successful knowledge application. Objective This study aimed to describe the level of knowledge in radiology of students in the last two years of medical school and curricular characteristics of their courses in seven Latin American countries. Methods A multicenter cross-sectional study was carried out on medical students of 7 Latin American countries (Bolivia, Brazil, Colombia, Ecuador, Mexico, Paraguay, and Peru) in their final two years of medical school, using an online questionnaire validated by experts and adapted for each country that assessed knowledge and curricular characteristics in radiology subject. Scores were assigned according to the number of correct answers for the knowledge test. The T-test, and regression analysis with one-way ANOVA were used to search for relationships between the level of knowledge and other variables. Results A total of 1514 medical students participated in this study. All countries had similar participation (n > 200); most participants were women 57.8%. The country with the highest knowledge score was Brazil. Male, sixth year (internship) and from public universities students had higher knowledge score (n < 0.05). Participants, who considered radiology more important, and who reported higher compliance with teaching staff with the proposed syllabus, and programmed classes, obtained better scores (n < 0.05). Conclusions Latin American medical students included in this study have a regular overall level of knowledge of Radiology, apparently influenced by curricular differences such as class and academic program compliance. Efforts to better understand and improve academic training are indispensable. Limitations The study was subject to selection bias determined by non-probability convenience sampling. The questionnaire assessed only theoretical knowledge and the evaluation system was designed by the investigators. metadata Izquierdo Condoy, Juan Sebastian and Simbaña-Rivera, Katherine and Nati-Castillo, Humberto Alejandro and Cassa Macedo, Arthur and Cardozo Espínola, Claudia Diana and Vidal Barazorda, Gabriela M. and Palazuelos-Guzmán, Ideli and Trejo García, Brayan and Carrington, Sarah J. and Ortiz-Prado, Esteban mail UNSPECIFIED (2023) How much do Latin American medical students know about radiology? Latin-American multicenter cross-sectional study. Medical Education Online, 28 (1). ISSN 1087-2981

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Forecasting of sediment load (SL) is essential for reservoir operations, design of water resource structures, risk management, water resource planning and for preventing natural disasters in the river basin systems. Direct measurement of SL is difficult, labour intensive, and expensive. The development of an accurate and reliable model for forecasting the SL is required. Sediment transport is highly non-linear and is influenced by a variety of factors. Forecasting of the SL using various conventional methods is not highly accurate because of the association of various complex phenomena. In this study, major key factors such as rock type (RT), relief (R), rainfall (RF), water discharge (WD), temperature (T), catchment area (CA), and SL are recognized in developing the one-step-ahead SL forecasting model in the Mahanadi River (MR), which is among India’s largest rivers. Artificial neural networks (ANN) in conjunction with multi-objective genetic algorithm (ANN-MOGA)-based forecasting models were developed for forecasting the SL in the MR. The ANN-MOGA model was employed to optimize the two competing objective functions (bias and error variance) with simultaneous optimization of all associated ANN parameters. The performances of the proposed novel model were finally compared to other existing methods to verify the forecasting capability of the model. The ANN-MOGA model improved the performance by 12.81% and 10.19% compared to traditional AR and MAR regression models, respectively. The results suggested that hybrid ANN-MOGA models outperform traditional autoregressive and multivariate autoregressive forecasting models. Overall, hybrid ANN-MOGA intelligent techniques are recommended for the forecasting of SL in rivers metadata Yadav, Arvind and Ali Albahar, Marwan and Chithaluru, Premkumar and Singh, Aman and Alammari, Abdullah and Kumar, Gogulamudi Vijay and Miró Vera, Yini Airet mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, yini.miro@uneatlantico.es (2023) Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization. Water, 15 (3). p. 522. ISSN 2073-4441

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks metadata Faheem, Zaid Bin and Ishaq, Abid and Rustam, Furqan and de la Torre Díez, Isabel and Gavilanes, Daniel and Masías Vergara, Manuel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, manuel.masias@uneatlantico.es, UNSPECIFIED (2023) Image Watermarking Using Least Significant Bit and Canny Edge Detection. Sensors, 23 (3). p. 1210. ISSN 1424-8220

Article Subjects > Engineering Universidad Internacional do Cuanza > Research > Scientific Production
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés This research paper aims to examine the impact of innovative HRM practices, including employee participation, performance appraisal, reward and compensation, recruitment and selection, and redeployment–retraining on firm performance. For this purpose, four different models are utilized to examine the impact of innovative HRM department practices on the performance of small and medium enterprises (SMEs) in a country. The dependent variable, firm performance, is proxified by different variables such as labor productivity, product innovation, process innovation, and marketing innovation. For empirical analysis, primary data are collected using a questionnaire. Estimation is conducted using ordinary least squares (OLS) and logit regression techniques. The estimated results indicate that most innovative HRM practices have a statistically significant impact on firm performance in terms of labor productivity, product, process, and marketing innovations. These results imply that SMEs in a country may observe the benefits of devoting greater attention to innovative HRM practices to achieve their future growth potential. metadata Aslam, Mahvish and Shafi, Imran and Ahmed, Jamil and Garat de Marin, Mirtha Silvana and Soriano Flores, Emmanuel and Rojo Gutiérrez, Marco Antonio and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvana.marin@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, UNSPECIFIED (2023) Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance. Sustainability, 15 (7). p. 6273. ISSN 2071-1050

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés There is growing evidence that Alzheimer’s disease (AD) can be prevented by reducing risk factors involved in its pathophysiology. Food-derived bioactive molecules can help in the prevention and reduction of the progression of AD. Honey, a good source of antioxidants and bioactive molecules, has been tied to many health benefits, including those from neurological origin. Monofloral avocado honey (AH) has recently been characterized but its biomedical properties are still unknown. The aim of this study is to further its characterization, focusing on the phenolic profile. Moreover, its antioxidant capacity was assayed both in vitro and in vivo. Finally, a deep analysis on the pathophysiological features of AD such as oxidative stress, amyloid-β aggregation, and protein-tau-induced neurotoxicity were evaluated by using the experimental model C. elegans. AH exerted a high antioxidant capacity in vitro and in vivo. No toxicity was found in C. elegans at the dosages used. AH prevented ROS accumulation under AAPH-induced oxidative stress. Additionally, AH exerted a great anti-amyloidogenic capacity, which is relevant from the point of view of AD prevention. AH exacerbated the locomotive impairment in a C. elegans model of tauopathy, although the real contribution of AH remains unclear. The mechanisms under the observed effects might be attributed to an upregulation of daf-16 as well as to a strong ROS scavenging activity. These results increase the interest to study the biomedical applications of AH; however, more research is needed to deepen the mechanisms under the observed effects metadata Romero-Márquez, Jose M. and Navarro-Hortal, María D. and Orantes, Francisco J. and Esteban-Muñoz, Adelaida and Mazas Pérez-Oleaga, Cristina and Battino, Maurizio and Sánchez-González, Cristina and Rivas-García, Lorenzo and Giampieri, Francesca and Quiles, José L. and Forbes-Hernandez, Tamara Y. mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, cristina.mazas@uneatlantico.es, maurizio.battino@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, tamara.forbes@unini.edu.mx (2023) In Vivo Anti-Alzheimer and Antioxidant Properties of Avocado (Persea americana Mill.) Honey from Southern Spain. Antioxidants, 12 (2). p. 404. ISSN 2076-3921

Article Subjects > Teaching Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Regulatory dispersion and a utilitarian use of sustainability deepen the gap within the teaching–learning process and limit the introduction of sustainable criteria in organizations through projects. The objective of this research consisted in developing a sustainable and holistic educational proposal for an online postgraduate program belonging to the Universidad Europea del Atlántico (UNEATLANTICO) within the field of projects. The proposal was based on the instrumentalization of a model comprised of national and international bibliographic references, resulting in a sustainability guide with significant improvements in relation to the reference standard par excellence: ISO 26000:2010. This guide formed the basis of a sustainability management plan, which was key in the project methodology and during the development of sustainable objectives and descriptors for each of the subjects. Lastly, the entities, attributes, and cardinal relationships were established for the development of a physical model used to facilitate the management of all this information within a SQL database. The rigor when determining the educational program, as well as the subsequent analysis of results as supported by the literature review, presupposes the application of this methodology toward other multidisciplinary programs contributing to the adoption of good sustainability practices within the educational field metadata Gracia Villar, Mónica and Álvarez, Roberto Marcelo and Brie, Santiago and Miró Vera, Yini Airet and García Villena, Eduardo mail monica.gracia@uneatlantico.es, roberto.alvarez@uneatlantico.es, santiago.brie@uneatlantico.es, yini.miro@uneatlantico.es, eduardo.garcia@uneatlantico.es (2023) Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area. Education Sciences, 13 (1). p. 97. ISSN 2227-7102

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved. metadata Kanwal, Tabassum and Rehman, Saif Ur and Ali, Tariq and Mahmood, Khalid and Gracia Villar, Santos and Dzul Lopez, Luis and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, UNSPECIFIED (2023) An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field. Agriculture, 13 (8). p. 1600. ISSN 2077-0472

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés An Internet of Things (IoT) network is prone to many ways of threatening individuals. IoT sensors are lightweight, lack complicated security protocols, and face threats to privacy and confidentiality. Hackers can attack the IoT network and access personal information and confidential data for blackmailing, and negatively manipulate data. This study aims to propose an IoT threat protection system (IoTTPS) to protect the IoT network from threats using an ensemble model RKSVM, comprising a random forest (RF), K nearest neighbor (KNN), and support vector machine (SVM) model. The software-defined networks (SDN)-based IoT network datasets such as KDD cup 99, NSL-KDD, and CICIDS are used for threat detection based on machine learning. The experimental phase is conducted by using a decision tree (DT), logistic regression (LR), Naive Bayes (NB), RF, SVM, gradient boosting machine (GBM), KNN, and the proposed ensemble RKSVM model. Furthermore, performance is optimized by adding a grid search hyperparameter optimization technique with K-Fold cross-validation. As well as the NSL-KDD dataset, two other datasets, KDD and CIC-IDS 2017, are used to validate the performance. Classification accuracies of 99.7%, 99.3%, 99.7%, and 97.8% are obtained for DoS, Probe, U2R, and R2L attacks using the proposed ensemble RKSVM model using grid search and cross-fold validation. Experimental results demonstrate the superior performance of the proposed model for IoT threat detection. metadata Akram, Urooj and Sharif, Wareesa and Shahroz, Mobeen and Mushtaq, Muhammad Faheem and Gavilanes Aray, Daniel and Bautista Thompson, Ernesto and Diez, Isabel de la Torre and Djuraev, Sirojiddin and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, ernesto.bautista@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System. Sensors, 23 (14). p. 6379. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés 5G has been launched in a few countries of the world, so now all focus shifted towards the development of future 6G networks. 5G has connected all aspects of society. Ubiquitous connectivity has opened the doors for more data sharing. Although 5G is providing low latency, higher data rates, and high-speed yet there are some security-related vulnerabilities. Those security issues need to be mitigated for securing 6G networks from existing challenges. Classical cryptography will not remain enough for securing the 6G network. As all classical cryptography can be disabled with the help of quantum mechanics. Therefore, in the place of traditional security solutions, in this article, we have reviewed all the existing quantum solutions of 5G existing security issues to mitigate them and secure 6G in a Future Quantum World. metadata Mangla, Cherry and Rani, Shalli and Faseeh Qureshi, Nawab Muhammad and Singh, Aman mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es (2023) Mitigating 5G security challenges for next-gen industry using quantum computing. Journal of King Saud University - Computer and Information Sciences. ISSN 13191578

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Society and the environment are severely impacted by catastrophic events, specifically floods. Inadequate emergency preparedness and response are frequently the result of the absence of a comprehensive plan for flood management. This article proposes a novel flood disaster management (FDM) system using the full lifecycle disaster event model (FLCNDEM), an abstract model based on the function super object. The proposed FDM system integrates data from existing flood protocols, languages, and patterns and analyzes viewing requests at various phases of an event to enhance preparedness and response. The construction of a task library and knowledge base to initialize FLCNDEM results in FLCDEM flooding response. The proposed FDM system improves the emergency response by offering a comprehensive framework for flood management, including pre-disaster planning, real-time monitoring, and post-disaster evaluation. The proposed system can be modified to accommodate various flood scenarios and enhance global flood management. metadata Khan, Saad Mazhar and Shafi, Imran and Butt, Wasi Haider and Díez, Isabel de la Torre and López Flores, Miguel Ángel and Castanedo Galán, Juan and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, miguelangel.lopez@uneatlantico.es, juan.castanedo@uneatlantico.es, UNSPECIFIED (2023) Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support. Land, 12 (8). p. 1538. ISSN 2073-445X

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Background and Aims The 2022-mpox outbreak has spread worldwide in a short time. Integrated knowledge of the epidemiology, clinical characteristics, and transmission of mpox are limited. This systematic review of peer-reviewed articles and gray literature was conducted to shed light on the epidemiology, clinical features, and transmission of 2022-mpox outbreak. Methods We identified 45 peer-reviewed manuscripts for data analysis. The standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement and Cochrane Collaboration were followed for conducting the study. Results The case number of mpox has increased about 100 times worldwide. About 99% of the cases in 2022 outbreak was from non-endemic regions. Men (70%–98% cases) were mostly infected with homosexual and bisexual behavior (30%–60%). The ages of the infected people ranged between 30 and 40 years. The presence of HIV and sexually transmitted infections among 30%–60% of cases were reported. Human-to-human transmission via direct contact and different body fluids were involved in the majority of the cases (90%–100%). Lesions in genitals, perianal, and anogenital areas were more prevalent. Unusually, pharyngitis (15%–40%) and proctitis (20%–40%) were more common during 2022 outbreak than pre-2022 outbreaks. Brincidofovir is approved for the treatment of smallpox by FDA (USA). Two vaccines, including JYNNEOSTM and ACAM2000®, are approved and used for pre- and post-prophylaxis in cases. About 100% of the cases in non-endemic regions were associated with isolates of IIb clade with a divergence of 0.0018–0.0035. Isolates from B.1 lineage were the most predominant followed by B.1.2 and B.1.10. Conclusion This study will add integrated knowledge of the epidemiology, clinical features, and transmission of mpox. metadata Sharif, Nadim and Sharif, Nazmul and Alzahrani, Khalid J. and Halawani, Ibrahim F. and Alzahrani, Fuad M. and Díez, Isabel De la Torre and Lipari, Vivian and López Flores, Miguel Ángel and Parvez, Anowar K. and Dey, Shuvra K. mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, miguelangel.lopez@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review. Health Science Reports, 6 (10). ISSN 2398-8835

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model. metadata Chakraborty, Gouri Shankar and Batra, Salil and Singh, Aman and Muhammad, Ghulam and Yélamos Torres, Vanessa and Mahajan, Makul mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, vanessa.yelamos@funiber.org, UNSPECIFIED (2023) A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics, 13 (10). p. 1806. ISSN 2075-4418

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs. The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity. To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences. This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations. The next step is to link the polarity to the review’s sentence-level features. Products are prioritized following user needs by assigning relative importance to each of the polarities. The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated. Experimental evaluation of PRUS utilizes rank score ( RS ) and normalized discounted cumulative gain ( nDCG ) score. Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products. metadata Hussain, Naveed and Mirza, Hamid Turab and Iqbal, Faiza and Altaf, Ayesha and Shoukat, Ahtsham and Gracia Villar, Mónica and Soriano Flores, Emmanuel and Rojo Gutiérrez, Marco Antonio and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, UNSPECIFIED (2023) PRUS: Product Recommender System Based on User Specifications and Customers Reviews. IEEE Access, 11. pp. 81289-81297. ISSN 2169-3536

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases. metadata Aggarwal, Meenakshi and Khullar, Vikas and Goyal, Nitin and Singh, Aman and Tolba, Amr and Bautista Thompson, Ernesto and Kumar, Sushil mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, ernesto.bautista@unini.edu.mx, UNSPECIFIED (2023) Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification. Agriculture, 13 (5). p. 936. ISSN 2077-0472

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés Objective The aim was to explore the association of demographic and prehospital parameters with short-term and long-term mortality in acute life-threatening cardiovascular disease by using a hazard model, focusing on elderly individuals, by comparing patients under 75 years versus patients over 75 years of age. Design Prospective, multicentre, observational study. Setting Emergency medical services (EMS) delivery study gathering data from two back-to-back studies between 1 October 2019 and 30 November 2021. Six advanced life support (ALS), 43 basic life support and five hospitals in Spain were considered. Participants Adult patients suffering from acute life-threatening cardiovascular disease attended by the EMS. Primary and secondary outcome measures The primary outcome was in-hospital mortality from any cause within the first to the 365 days following EMS attendance. The main measures included prehospital demographics, biochemical variables, prehospital ALS techniques used and syndromic suspected conditions. Results A total of 1744 patients fulfilled the inclusion criteria. The 365-day cumulative mortality in the elderly amounted to 26.1% (229 cases) versus 11.6% (11.6%) in patients under 75 years old. Elderly patients (≥75 years) presented a twofold risk of mortality compared with patients ≤74 years. Life-threatening interventions (mechanical ventilation, cardioversion and defibrillation) were also related to a twofold increased risk of mortality. Importantly, patients suffering from acute heart failure presented a more than twofold increased risk of mortality. Conclusions This study revealed the prehospital variables associated with the long-term mortality of patients suffering from acute cardiovascular disease. Our results provide important insights for the development of specific codes or scores for cardiovascular diseases to facilitate the risk of mortality characterisation. metadata del Pozo Vegas, Carlos and Zalama-Sánchez, Daniel and Sanz-Garcia, Ancor and López-Izquierdo, Raúl and Sáez-Belloso, Silvia and Mazas Pérez-Oleaga, Cristina and Dominguez Azpíroz, Irma and Elío Pascual, Iñaki and Martín-Rodríguez, Francisco mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, cristina.mazas@uneatlantico.es, irma.dominguez@unini.edu.mx, inaki.elio@uneatlantico.es, UNSPECIFIED (2023) Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study. BMJ Open, 13 (11). e078815. ISSN 2044-6055

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Background: Nowadays, there is no gold standard score for prehospital sepsis and sepsis-related mortality identification. The aim of the present study was to analyze the performance of qSOFA, NEWS2 and mSOFA as sepsis predictors in patients with infection-suspected in prehospital care. The second objective is to study the predictive ability of the aforementioned scores in septic-shock and in-hospital mortality. Methods: Prospective, ambulance-based, and multicenter cohort study, developed by the emergency medical services, among patients (n = 535) with suspected infection transferred by ambulance with high-priority to the emergency department (ED). The study enrolled 40 ambulances and 4 ED in Spain between 1 January 2020, and 30 September 2021. All the variables used in the scores, in addition to socio-demographic data, standard vital signs, prehospital analytical parameters (glucose, lactate, and creatinine) were collected. For the evaluation of the scores, the discriminative power, calibration curve and decision curve analysis (DCA) were used. Results: The mSOFA outperformed the other two scores for mortality, presenting the following AUCs: 0.877 (95%CI 0.841–0.913), 0.761 (95%CI 0.706–0.816), 0.731 (95%CI 0.674–0.788), for mSOFA, NEWS, and qSOFA, respectively. No differences were found for sepsis nor septic shock, but mSOFA’s AUCs was higher than the one of the other two scores. The calibration curve and DCA presented similar results. Conclusion: The use of mSOFA could provide and extra insight regarding the short-term mortality and sepsis diagnostic, backing its recommendation in the prehospital scenario. metadata Melero-Guijarro, Laura and Sanz-García, Ancor and Martín-Rodríguez, Francisco and Lipari, Vivian and Mazas Pérez-Oleaga, Cristina and Carvajal-Altamiranda, Stefanía and Martínez López, Nohora Milena and Dominguez Azpíroz, Irma and Castro Villamor, Miguel A. and Sánchez Soberón, Irene and López-Izquierdo, Raúl mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, cristina.mazas@uneatlantico.es, stefania.carvajal@uneatlantico.es, nohora.martinez@uneatlantico.es, irma.dominguez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study. Frontiers in Medicine, 10. ISSN 2296-858X

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés Introduction: Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh. Methods: This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023. Results: This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001). Discussion: This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh. metadata Sharif, Nadim and Sharif, Nazmul and Khan, Afsana and Dominguez Azpíroz, Irma and Martínez Díaz, Raquel and Díez, Isabel De la Torre and Parvez, Anowar Khasru and Dey, Shuvra Kanti mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis. Frontiers in Immunology, 14. ISSN 1664-3224

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Introduction: Co-prevalence of long-COVID-19, cardiovascular diseases and diabetes is one of the major health challenges of the pandemic worldwide. Studies on long-COVID-19 and associated health outcomes are absent in Bangladesh. The main aim of this study was to determine the prevalence and impact of long-COVID-19 on preexisting diabetes and cardiovascular diseases (CVD) on health outcomes among patients in Bangladesh. Methods: We collected data from 3,250 participants in Bangladesh, retrospectively. Multivariable logistic regression model was used to determine the odds ratio between independent and dependent variables. Kaplan-Meier survival curve was used to determine the cumulative survival. Results: COVID-19 was detected among 73.4% (2,385 of 3,250) participants. Acute long-COVID-19 was detected among 28.4% (678 of 2,385) and chronic long-COVID-19 among 71.6% (1,707 of 2,385) patients. CVD and diabetes were found among 32%, and 24% patients, respectively. Mortality rate was 18% (585 of 3,250) among the participants. Co-prevalence of CVD, diabetes and COVID-19 was involved in majority of fatality (95%). Fever (97%), dry cough (87%) and loss of taste and smell (85%) were the most prevalent symptoms. Patients with co-prevalence of CVD, diabetes and COVID-19 had higher risk of fatality (OR: 3.65, 95% CI, 2.79–4.24). Co-prevalence of CVD, diabetes and chronic long-COVID-19 were detected among 11.9% patients. Discussion: Risk of hospitalization and fatality reduced significantly among the vaccinated. This is one of the early studies on long-COVID-19 in Bangladesh. metadata Sharif, Nadim and Sharif, Nazmul and Khan, Afsana and Halawani, Ibrahim F. and Alzahrani, Fuad M. and Alzahrani, Khalid J. and Díez, Isabel De la Torre and Ramírez-Vargas, Debora L. and Kuc Castilla, Ángel Gabriel and Parvez, Anowar Khasru and Dey, Shuvra Kanti mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, debora.ramirez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh. Frontiers in Public Health, 11. ISSN 2296-2565

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings. metadata Rustam, Furqan and Ishaq, Abid and Hashmi, Muhammad Shadab Alam and Siddiqui, Hafeez Ur Rehman and Dzul Lopez, Luis and Castanedo Galán, Juan and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, luis.dzul@unini.edu.mx, juan.castanedo@uneatlantico.es, UNSPECIFIED (2023) Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data. Sensors, 23 (16). p. 7018. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%. metadata Aziz, Romila and Anwar, Muhammad Waqas and Jamal, Muhammad Hasan and Bajwa, Usama Ijaz and Kuc Castilla, Ángel Gabriel and Uc-Rios, Carlos and Bautista Thompson, Ernesto and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.uc@unini.edu.mx, ernesto.bautista@unini.edu.mx, UNSPECIFIED (2023) Real Word Spelling Error Detection and Correction for Urdu Language. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor’s algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The National Institute of Standards and Technology (NIST) has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two post-quantum cryptography (PQC) algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing. metadata Farooq, Sana and Altaf, Ayesha and Iqbal, Faiza and Bautista Thompson, Ernesto and Ramírez-Vargas, Debora L. and Díez, Isabel de la Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ernesto.bautista@unini.edu.mx, debora.ramirez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2023) Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms. Sensors, 23 (12). p. 5379. ISSN 1424-8220

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Mutations allow viruses to continuously evolve by changing their genetic code to adapt to the hosts they infect. It is an adaptive and evolutionary mechanism that helps viruses acquire characteristics favoring their survival and propagation. The COVID-19 pandemic declared by the WHO in March 2020 is caused by the SARS-CoV-2 virus. The non-stop adaptive mutations of this virus and the emergence of several variants over time with characteristics favoring their spread constitute one of the biggest obstacles that researchers face in controlling this pandemic. Understanding the mutation mechanism allows for the adoption of anticipatory measures and the proposal of strategies to control its propagation. In this study, we focus on the mutations of this virus, and we propose the SARSMutOnto ontology to model SARS-CoV-2 mutations reported by Pango researchers. A detailed description is given for each mutation. The genes where the mutations occur and the genomic structure of this virus are also included. The sub-lineages and the recombinant sub-lineages resulting from these mutations are additionally represented while maintaining their hierarchy. We developed a Python-based tool to automatically generate this ontology from various published Pango source files. At the end of this paper, we provide some examples of SPARQL queries that can be used to exploit this ontology. SARSMutOnto might become a ‘wet bench’ machine learning tool for predicting likely future mutations based on previous mutations. metadata Bakkas, Jamal and Hanine, Mohamed and Chekry, Abderrahman and Gounane, Said and de la Torre Díez, Isabel and Lipari, Vivian and Martínez López, Nohora Milena and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, nohora.martinez@uneatlantico.es, UNSPECIFIED (2023) SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations. Viruses, 15 (2). p. 505. ISSN 1999-4915

Article Subjects > Social Sciences Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés In the last two decades, there is an increasingly broad line of studies that warn about the emotional health of journalists and the challenges that it poses for communication professionals to be able to separate work issues from their personal lives. The coverage of COVID-19 exposed many journalists to situations of frustration, discomfort and stress for various reasons: long working hours, not having the appropriate technological material, added to an environment of uncertainty caused by the pandemic. This study aims to examine the possible scope of technostress –in some cases associated to digital divide– in journalists and analyze if they are aware of the uses of care of the self as a way to deal with stressful situations. For this, our research is based on documentary analysis, a survey answered by (50) fifty Argentinean journalists, and twelve (12) in-depth interviews to experienced journalists. Our findings suggest that constant exposure to computers and smartphones during the lockdown together with difficulties to connect to Internet or to have the adequate materials and the lack of coping strategies –as the care of the self– confirms the presence of technostress. Another result that emerges from this research, it should be addressed in future studies, is that some journalists’ reactions about care of the self could respond to the third person effect theory to maintain high self-esteem and not demonstrate vulnerability. metadata Escudero, Carolina and Prola, Thomas and Soriano Flores, Emmanuel and Silva Alvarado, Eduardo René mail UNSPECIFIED, thomas.prola@uneatlantico.es, emmanuel.soriano@uneatlantico.es, eduardo.silva@funiber.org (2023) The Scope of Technostress and Care of The Self on Journalists During the Pandemic. Przestrzeń Społeczna (Social Space), 23 (4). pp. 20-43. ISSN 20841558

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The rapid generation of data from various sources by the public sector, private corporations, business associations, and local communities is referred to as big data. This large and complex dataset is often regarded as the ‘new oil’ by public administrations (PAs), and data-driven approaches are employed to transform it into valuable insights that can improve governance, transparency, digital services, and public engagement. The government’s big-data ecosystem (GBDE) is a result of this initiative. Effective data management is the first step towards large-scale data analysis, which yields insights that benefit your work and your customers. However, managing big data throughout its life cycle is a daunting challenge for public agencies. Despite its widespread use, big data management is still a significant obstacle. To address this issue, this study proposes a hybrid approach to secure the data management life cycle for GBDE. Specifically, we use a combination of the ECC algorithm with AES 128 BITS encryption to ensure that the data remain confidential and secure. We identified and analyzed various data life cycle models through a systematic literature review to create a data management life cycle for data-driven governments. This approach enhances the security and privacy of data management and addresses the challenges faced by public agencies. metadata Zahid, Reeba and Altaf, Ayesha and Ahmad, Tauqir and Iqbal, Faiza and Miró Vera, Yini Airet and López Flores, Miguel Ángel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, yini.miro@uneatlantico.es, miguelangel.lopez@uneatlantico.es, UNSPECIFIED (2023) Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective. Systems, 11 (8). p. 380. ISSN 2079-8954

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
Abierto Inglés Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations. metadata Rashid, Chaudhary Hamza and Shafi, Imran and Ahmad, Jamil and Bautista Thompson, Ernesto and Masías Vergara, Manuel and Diez, Isabel De La Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ernesto.bautista@unini.edu.mx, manuel.masias@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Software Cost and Effort Estimation: Current Approaches and Future Trends. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Safety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such parts becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe the recent trends of error surface and responds efficiently to the local gradient for precise spare prediction results marked by lumpiness. Introduction of the momentum term allows the proposed ANN model to ignore small variations in the error surface and to behave like a low-pass filter and thus to avoid local minima. Using the whole collection of aviation spare parts having the highest demand activity, an ANN model is built to predict the failure of aircraft installed parts. The proposed model is first optimized for its topology and is later trained and validated with known historical demand datasets. The testing phase includes introducing input vector comprising influential factors that dictate sporadic demand. The proposed approach is found to provide superior results due to its simple architecture and fast converging training algorithm once evaluated against some other state-of-the-art models from the literature using related benchmark performance criteria. The experimental results demonstrate the effectiveness of the proposed approach. The accurate prediction of the cost-heavy and critical spare parts is expected to result in huge cost savings, reduce downtime, and improve the operational readiness of drones, fixed wing aircraft and helicopters. This also resolves the dead inventory issue as a result of wrong demands of fast moving spares due to human error. metadata Shafi, Imran and Sohail, Amir and Ahmad, Jamil and Martínez Espinosa, Julio César and Dzul Lopez, Luis Alonso and Bautista Thompson, Ernesto and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, luis.dzul@unini.edu.mx, ernesto.bautista@unini.edu.mx, UNSPECIFIED (2023) Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety. Applied Sciences, 13 (9). p. 5475. ISSN 2076-3417

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques. metadata Khan, Saad Mazhar and Shafi, Imran and Butt, Wasi Haider and Diez, Isabel de la Torre and López Flores, Miguel Ángel and Castanedo Galán, Juan and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, miguelangel.lopez@uneatlantico.es, juan.castanedo@uneatlantico.es, UNSPECIFIED (2023) A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions. Land, 12 (8). p. 1514. ISSN 2073-445X

Article Subjects > Biomedicine
Subjects > Engineering
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics. metadata Ferreras, Antonio and Sumalla Cano, Sandra and Martínez-Licort, Rosmeri and Elío Pascual, Iñaki and Tutusaus, Kilian and Prola, Thomas and Vidal Mazón, Juan Luis and Sahelices, Benjamín and de la Torre Díez, Isabel mail UNSPECIFIED, sandra.sumalla@uneatlantico.es, UNSPECIFIED, inaki.elio@uneatlantico.es, kilian.tutusaus@uneatlantico.es, thomas.prola@uneatlantico.es, juanluis.vidal@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight. Journal of Medical Systems, 47 (1). ISSN 1573-689X

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach. metadata Shafi, Imran and Sajad, Muhammad and Fatima, Anum and Gavilanes Aray, Daniel and Lipari, Vivian and Diez, Isabel de la Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, vivian.lipari@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19. Sensors, 23 (15). p. 6837. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés In this paper, a novel ultra-wideband UWB antenna element with triple-band notches is proposed. The proposed UWB radiator element operates from 2.03 GHz up to 15.04 GHz with triple rejected bands at the WiMAX band (3.28–3.8 GHz), WLAN band (5.05–5.9 GHz), and X-band (7.78–8.51 GHz). In addition, the radiator supports the Bluetooth band (2.4–2.483 GHz). Three different techniques were utilized to obtain the triple-band notches. An alpha-shaped coupled line with a stub-loaded resonator (SLR) band stop filter was inserted along the main feeding line before the radiator to obtain a WiMAX band notch characteristic. Two identical U-shaped slots were etched on the proposed UWB radiator to achieve WLAN band notch characteristics with a very high degree of selectivity. Two identical metallic frames of an octagon-shaped electromagnetic band gap structure (EBG) were placed along the main feeding line to achieve the notch characteristic with X-band satellite communication with high sharpness edges. A novel UWB multiple-input multiple-output (MIMO) radiator is proposed. The proposed UWB-MIMO radiator was fabricated on FR-4 substrate material and measured. The isolation between every two adjacent ports was below −20 dB over the FCC-UWB spectrum and the Bluetooth band for the four MIMO antennas. The envelope correlation coefficient (ECC) between the proposed antennas in MIMO does not exceed 0.05. The diversity gains (DG) for all the radiators are greater than 9.98 dB. metadata El-Gendy, Mohamed S. and Ali, Mohamed Mamdouh M. and Bautista Thompson, Ernesto and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, ernesto.bautista@unini.edu.mx, UNSPECIFIED (2023) Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band. Sensors, 23 (9). p. 4475. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Blockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML. metadata Akrami, Nouhaila El and Hanine, Mohamed and Flores, Emmanuel Soriano and Aray, Daniel Gavilanes and Ashraf, Imran mail UNSPECIFIED (2023) Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis. IEEE Access, 11. pp. 78879-78903. ISSN 2169-3536

Article Subjects > Social Sciences Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The primary objectives of this research article were twofold. Firstly, to categorise a total of 294 individuals who aspired to three distinct competency profiles associated with the supervision of international car sales (SPV). Secondly, to prioritise the criteria used for measurement and assess the level of satisfaction attained following the provision of targeted online training for each respective position. Segmentation was performed using the K-Means algorithm on a Likert scale importance questionnaire. Satisfaction indicators were derived by applying fuzzy set methods to the results of a satisfaction questionnaire, also using a Likert scale. The measurement criteria did not show any clear negative perceptions. The overall satisfaction index was 0.7, which was supported by classic statistics and placed in a high category. Additionally, a variable analysis revealed that candidates from the Euro-Asian region exhibited significantly low levels of satisfaction. However, no significant associations were observed between satisfaction levels and gender, income profile, completed training action, or age groups. The researchers rigorously employed a methodology that included assessing the validity and reliability of the instrument. A review of relevant literature also supported the analysis of the results. These findings suggest that the method could be applied to other multidisciplinary programmes to make informed decisions in the field of training. metadata Brito Ballester, Julién and Gracia Villar, Mónica and Soriano Flores, Emmanuel and García Villena, Eduardo mail julien.brito@uneatlantico.es, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, eduardo.garcia@uneatlantico.es (2023) Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive. International Journal of Operations and Quantitative Management, 29 (2). pp. 223-251.

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés This study sought to investigate how different brain regions are affected by Alzheimer’s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer’s disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer’s disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each. metadata Shahzadi, Samra and Butt, Naveed Anwer and Sana, Muhammad Usman and Elío Pascual, Iñaki and Briones Urbano, Mercedes and Díez, Isabel de la Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, inaki.elio@uneatlantico.es, mercedes.briones@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches. Diagnostics, 13 (18). p. 2871. ISSN 2075-4418

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance metadata Chithaluru, Premkumar and Singh, Aman and Mahmoud, Mahmoud Shuker and Kumar, Sunil and Vidal Mazón, Juan Luis and Alkhayyat, Ahmed and Anand, Divya mail UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, UNSPECIFIED, divya.anand@uneatlantico.es (2023) An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks. Sustainable Energy Technologies and Assessments, 56. p. 103079. ISSN 22131388

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Objective This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. Method The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture. Results The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. Conclusion Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance. metadata Alvi, Sohaib Bin Khalid and Nayyer, Muhammad Ziad and Jamal, Muhammad Hasan and Raza, Imran and de la Torre Diez, Isabel and Rodríguez Velasco, Carmen Lilí and Breñosa, Jose and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carmen.rodriguez@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, UNSPECIFIED (2023) A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation. DIGITAL HEALTH, 9. ISSN 2055-2076

2022

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés The present project corresponds to a course design employing CLIL methodology to teach Science in English as the target language to a group of students in an elementary school in the city of Guayaquil, Ecuador. This course has been planned to take into consideration learners’ active participation, including hands-on activities that will lead to a better understanding and acquisition of the contents of the materials as body movement plays an important role in the cognitive and memorization process, and curriculum connection with other academic areas. Experiential knowledge is a solid element to learn but also an integral classroom environment that allows expressiveness, cooperation, and interaction between peers without any judgment. metadata Coello Franco, Gianella Alexandra mail gianellacoello11@hotmail.es (2022) A 6th Grade A2 EFL Communicative CLIL Course Design Implementing Hands-on Activities and Cross-Curricular Contents in A Monolingual Classroom in A Primary School in Ecuador. Masters thesis, UNSPECIFIED.

Thesis Subjects > Social Sciences Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This project aims to design EFL communicative and task-based teaching material for an A1 level adult whose native language (L1) is Spanish. In the teaching material proposed in this project student’s L1 background as well as Bloom’s taxonomy of educational objectives is analyzed theoretically and considered when designing activities in the second part of this project. Withing this project an A1 EFL material that develops lexical, grammatical, notion-functional and sociocultural contents as well as communicative tasks while incorporating ICTs and digital tools such as: Kahoot, Canvas, Miro, Prezi videos, Edpuzzle, Youtube, Padlet, Bloom, Mentimeter, Adobe Spark, Genially and Picktochart.-This Material Design Project can serve as a guideline for EFL teachers whose A1 adult students’ native language is Spanish who not only study English online, but also offline as the materials proposed in this project can be used in both modalities of teaching. metadata Robalino Lopesa, Zanna mail zanna.aniscenko@gmail.com (2022) An A1 EFL Communicative and Task-Based Material Design for Adults, with Spanish as their L1, Incorporating ICTs. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This Action Research Project is aimed to contribute to the production of English Speaking and Writing skills in a group of six-grade students by employing CLIL and Task-Based Science activities to catch children's interes to learn and produce the second language in an autonomous and meaningful way.This project shows four stages: 1) Diagnostic of learning needs and motivations for productive skills, 2) Designing of CLIL and task-based Science activities to reach productive skills, 3) Implementation of CLIL and task-based Science activities for online, face-to-face and hybrid classes, and 4) Evaluation of the impact and meaningful outcomes.For this Action-Research project, explanatory and exploratory methodologies were applied. The instruments to collect quantitative and qualitative informaction were designed with online applications to receive the answers of all the educative community, face-to-face, online and hybrid modalities.Furthermore, a proved compilation of Science lessons used with six-grders to improve the mentioned skills is going to be shared, rubrics and gamification for assessment samples tht have motivated students to learn and produce a second language while they are exploring their world. metadata Méndez Guevara, Verónica Del Carmen mail vero_anahi2000@yahoo.com (2022) An Action Reseach for Implementing CLIL and Task-Based Science Activities for Promoting the Englsih Productive Skills in a Group of Six-Grade Students at a Private International Bilingual School in Cumbayá, Ecuador. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Nowadays, the use of strategies represent significant roll in English language teaching process, because these help learners to improve their skills. The Implementation of the European Language Portfolio is a strategy that develops the writing process through the communication tasks. This action research, carried out in learners from seventh grade in a public school in Colombia, aimed to understand the implementation of portfolio as learning strategy maximizes the writing skill. The data was gathered through participant observations, interviews, journals and the analysis of ELP through the communicative tasks worked in class. The information was interpreted under a qualitative and quantitative analysis. Finally, the results present the main contributions that implementation of the ELP is an excellent strategy to improve writing skill, as well as increase their vocabulary, allowing students to use it while writing in the foreign language. metadata Torres Florez, Maria Alexandra mail torresalexandra1989@gmail.com (2022) An Action Research Study on How to Implement the European Language Portfolio as a Learning Strategy for Developing Writing Skill through communicative tasks in a Public High School. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This paper is the process of a material design and action research that took place in Cartagena, Colombia at the Navy school "ESCUELA NAVAL ALMIRANTE PADILLA". It was developed in three main phases that included, evaluation, design, and implementation of an EAP (English for Academic Purposes) or ESP (English for Specific purposes) material for fully beginner English language learners. metadata Castillo Payares, Maria Teresa mail mayecastillo35@gmail.com (2022) An Action Research for Designing and Implementing a Beginners English for Specific Purposes material for naval students at Escuela Naval “Almirante Padilla” in Cartagena, Colombia". Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Scientific-technological progress has had a massive impact on society, occupying a fundamental place in the development and daily life of people. The main objective of the Project was to develop an action-research for the design and implementation of a communicative material for English as a foreign language for sixth grade that incorporates ICT, collaborative, active and significant learning in the Milenio Lumbaquí Educational Unit. The research was quantitative since it will process numerical results derived from the surveys applied to teachers and students. We worked with 164 students and 8 teachers belonging to the English Area whose ages range between 27 and 44 years and whose teaching experience ranges between 3 and 14 years of experience. 52% of the students state that the computer is sometimes used to teach English classes, while 43% declare that the computer is not used and only 5% express that they always use it for the development of the classes, with this it should be noted that teachers do not make good use of the computer in English classrooms, although in this time of pandemic teachers have had to resort to technology to teach their classes. The design and implementation of a communicative material of English as a foreign language for sixth grade that incorporates ICT, collaborative, active and significant learning in the Millennium Educational Unit Lumbaquí, allows improving verbal fluency, lexicon, syntax, vocabulary and reading comprehension. metadata Iñiguez Vallejo, Paulo César mail paulo2378@hotmail.com (2022) An Action Research for Designing and Implementing a Communicative 6th Grade EFL Material Incorporating ICTs, collaborative, active and meaningful learning in the Millennium Educational Unit Lumbaqui. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This project reflects the influence of approaches in the English classroom to promote speaking skills on students from one group of 8th graders in a private institution thrhough the implementation of Active and Cooperative learning strategies. metadata Azofeifa Vanegas, Ana Elena mail ae1804-@hotmail.com (2022) An Action Research for Implementing Active and Cooperative Learning Strategies that Develop the EFL Speaking Skills of 8-A Graders at Centro Educativo Santa Inés, Costa Rica. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés The objective of this research study is to develop an action-research project to collect information on the impact of the implementation of communicative learning strategies based on online tasks and activities in a group of 8th grade students of English as a foreign language at the "San Juan Pablo II" Institute in Erandique, Lempira. For the development of the research we worked with students, teachers and parents. The results show that the educational center should propose that various teaching strategies be used so that students can develop their competencies in the four skills. Therefore, the research study investigates in a general way the criteria involved in the activities for the practice of a second language. metadata López Sánchez, Marcelo Otoniel mail otonielsanchez83@yahoo.com (2022) An Action Research for Implementing Communicative and Task-Based Learning strategies and online activities in a group of EFL 8th grade students at “San Juan Pablo II” Institute in Erandique, Lempira. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This final project proposes an action research suggesting the implementation of didactic strategies to reduce students' anxiety and promote the development of communicative skills in a group of Bachelor in Teaching English as a Foreign Language students at the National Pedagogical University “Francisco Morazán” in San Pedro Sula, Honduras. This proposal is based on the communicative and task-based approaches, and it follows the guidelines of the Common European Framework of Reference for Languages (CEFR). It proposes motivating and engaging students with didactic activities articulated to the development of the speaking, listening, writing, and reading skills. This action research and material design proposal aims to offer students communicative tasks that enable them to work the communicative competence, along with the speaking, writing, reading, and listening skills, while also working on grammatical, lexical, notion-functional and sociocultural contents. metadata Vega Martinez, Ronny Jareth mail ronnyareth@gmail.com (2022) An Action Research for Implementing Didactic Strategies Reducing Students' Anxiety and Promoting Communicative Skills Development in a group of Bachelor in Teaching English as a Foreign Language students at the National Pedagogic University “Francisco Morazán” in San Pedro Sula, Honduras. Masters thesis, UNSPECIFIED.

Thesis Subjects > Social Sciences Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Students Learning Assessment is one of the most challenging aspects of teaching, mainly because it places a great deal of responsibility on the shoulders of the teacher. They must choose tools and tasks that accurately assess students' learning and achievement while motivating them to improve their performance and continue learning. According to Stassen (2001, p. 5), assessments are "the systematic gathering and analysis of information to improve student learning." "Assessment theories and academics laud the relevance of feedback on performance assessment tasks for enabling development and progress in student learning success," says Orell (2006, pp. 441-456).All three aspects of assessment, teaching, and learning are interwoven. Teaching activities should not be divided; they should consider the students' needs and abilities. As a result, effective assessment strategies and activities must be implemented to involve students in their learning process with meaningful real-life scenarios to build their competencies. Professors must provide adequate assessment and feedback for students to have better learning opportunities and develop expertise to tailor their teaching experience. This is because when teachers assess and give feedback to students, they see it as a punishment or a simple grade rather than an opportunity to learn and improve their learning process. metadata Torres Valladares, Carlos Roberto mail ctorres@unah.edu.hn (2022) An Action Research for Implementing Effective Assessment Strategies and Activities in the Foreign Language program Psychopedagogy class at the National Autonomous University of Honduras. Masters thesis, Universidad Europea del Atlántico.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This research project had as main aim that of identifying the effectiveness of asynchronous corrective feedback (CF) on the oral production of university students taking an online integrated English course as part of their program. All research instruments and tested CF method were applied in the speaking section of the course that had a duration of sixteen weeks. The study followed a mixed-methods design, where qualitative data was gathered by means of a leaners’ speech analysis scale, while quantitative data was gathered through a questionnaire addressing language learners´ preferences in relation to CF in oral English classes. A first oral test and final oral test were also used as pre-test and post-test to measure the level of improvement after the intervention phase. Opposed to Nunan’s (1991) position towards the use of positive feedback instead of CF, the present research project was underlain by the conception that teachers’ reactions and reflections on learners’ deviant forms of the target language (TL) are a crucial part of the whole language learning spectrum. Very interesting findings were shed, first, for the two main types of errors analyzed in the study (Grammar and Vocabulary, and Pronunciation), there was a diminution in quantity when comparing the results from the first exam to those of the final exam, representing that learners committed 10% less errors in the final oral exam. Unsurprisingly, students unanimously favored the use of CF in their language classes. However, preferences on synchronous and asynchronous CF showed no major difference. metadata Ugalde Ramos, Oscar Abel mail abelcr8@hotmail.com (2022) An Action Research for Implementing Effective Online Asynchronous Corrective Feedback on the Oral Production of English as a Foreign Language Learners in University Virtual Speaking Classes. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés El presente trabajo muestra un action research sobre como ayudar a estudiantes respondan en inglés de forma oral a través de estrategias metacognitivas. metadata Salazar Aguirre, Imer Alejandro mail imer07alejandro@gmail.com (2022) An Action Research for Implementing Metacognitive Strategies for Increasing EFL Beginner Learners' Awareness on English Structure in Oral Production in a group of 14 students from Ingles Integrado III at Universidad Técnica Nacional Sede Regional de Guanacaste. Masters thesis, UNSPECIFIED.

Thesis Subjects > Comunication
Subjects > Teaching
Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés The rise of communicative language teaching approaches paved the way for a radical revision of teaching methodology. With introduction and the practical application of the principles of communicative competence, traditional models based on memorization were discarded and greater emphasis was placed on the importance of using the target language purposefully. Moreover, speaking and writing came to the foreground in ELT as both skills foster student-production and may also help to raise awareness on language related issues. Nevertheless, the excessive and almost exclusive focus on the sole development of speaking is evident. Despite its importance for the development of communicative competence, writing is not treated fairly in the classroom. In fact, writing is highly beneficial for teachers and students. For educators, compositions are the visible realization of learners´ interlanguage. As such, writing enables teachers to identify problematic areas and find practical solutions to them. For students, writing tasks trigger critical thinking processes and allow them to plan, organize and express their ideas visually. At this point, it is undeniable that the role and the approach to writing must be reconsidered. In this paper, a group of second-year students from Profesorado Superior de Lenguas Vivas participated in a series of communicative activities hoped to bring understanding on the importance of regarding writing as an active process. To this end, listening, speaking and reading materials were used as sources of pre-writing techniques that could help learners discover and unleash their potential in writing tasks. metadata Roldan, Germán Jose Maria mail joseroldan.769@gmail.com (2022) An Action Research for Implementing Pre-Reading Tasks as a Learning Strategy to increase superstructure, macrostructure and microstructure awareness in written productions. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This article discusses the importance of including English instruction in the curricular contents of learning institutions in Ecuador. Of particular interest for this study is how beginner-level English language learners at a public university in Guayaquil - Ecuador perform when exposed to lessons taught using two different methods – the TPRS method versus the traditional teaching method (using textbooks, workbooks and audio recordings). metadata Zea Vallejo, Daniel Arturo mail dzea2012@hotmail.com (2022) An Action Research for Implementing Teaching Proficiency through Reading and Storytelling (TPRS) for developing vocabulary in a group of EFL students from the Faculty of Chemistry at University of Guayaquil. Masters thesis, UNSPECIFIED.

Thesis Subjects > Comunication
Subjects > Teaching
Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés The study was planned as an Action Research. Thus, from our role as teacher-researchers, we examine the strengths, limitations, and difficulties of the initial reading and writing teaching process. Then, we implement strategies in order to facilitate and motivate students to develop the reading and writing process in the English language. metadata Padilla Membreño, Mirta Isabel mail mirtaphn@yahoo.com (2022) An Action Research for Implementing the Use of short stories in the classroom to develop reading comprehension in a group of EFL students at The National Autonomous University of Honduras. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés The research project analyses the communicative processes that students of ages ranging from 15 to 47, that are part of a mixed-age class, experience as part of their learning process. Also, the work investigates the communicative strategies that the students employ in order to enhance the oral skills of the A2 level students. This work analyzes aspects such as the role of the mixed-age class composition and its effect on the development of the class, the relationship between the implementation of the oral communication strategies and peer interaction, and the effect of oral communication strategies as a possible enhancer of the class participation process. As well, it provides an overview of the class dynamics and offers an integrative strategy that teachers and students can implement in class to improve group/peer interaction and oral production. metadata Jimenez Martinez, Orlando Jesus mail orjimenez27@gmail.com (2022) An Action Research for Improving Class Interaction through Oral Communicative Strategies in a Mixed-Age Group in the Fifth Level of EFL Courses of the National University of Costa Rica. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Teaching listening comprehension is one of the most difficult tasks for English teachers and for students, because there are no rules as in grammar teaching. This study explored to what extent students enhance listening comprehension through movie segments and what their attitudes are towards this teaching tool. A total of 64 students and two English teachers were surveyed. Interviews were used to know student's positive and negative opinions towards the strategy. Clearly, using movies proved to be an effective way for students to improve their listening ability. Most learners improved listening skills and gained more than vocabulary, understood more foreign culture, felt relaxed and had fun while learning in class. metadata Lara Ramirez, Juan Pablo mail juanlararamirez04@gmail.com (2022) An Action Research for improving listening skills by implementing the use of movie segments in a 2nd EFL Class with BGU students at “Réplica Guayaquil” high school. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This research has attempted to ascertain and examine the effect that cognitive reading strategies have on the academic reading skills. The report of this research is organized as follows: the title is presented at the very beginning of the research. Indeed, it is contextualized. The introduction is an essential part of this research. It explains briefly why this research tries to accomplish and numerous facts of the Ecuadorian educational context are cited. Then, the justification of academic and personal interest are developed as well as the guidelines of the research, certain theories that support it, and my desires regards to working on it. It is compulsory to have the research question and the objectives, too. These are presented right after the justification and personal interests. The theoretical background is all about the theories the research is grounded, which provides the theoretical framework of the research to foster reading comprehension skills. The methodology of the research is explained in this part. The description comprises an explanation of the research, methods, level, and type. In addition, the population of the research is identified and described, and the operationalization of the variables is carried out, and the data collection and analysis procedures are described. metadata Robelli Coto, Stefania Melisa mail stefytar@hotmail.com (2022) An Action Research: Fostering Reading Comprehension through the implementation of cognitive strategies in an11th grade EFL class at Babahoyo high school in Ecuador. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés This research investigates the difference between inductive and deductive methods in teaching grammar, applied to adult learners, in terms of effectiveness. In addition, it also analyzes the relationship between students’ preferences and their performance in both methods.How can the implementation of communicative strategies for teaching grammar inductively and deductively benefit and engage a group of adult A1~A2 English Business learners? Objective: To develop action research for implementing communicative strategies for teaching grammar both from an inductive or deductive approach while engaging a group of adult English business students.Specific aims:– To analyze which approach (inductive or deductive) works best;– To identify the learning needs of a group of adult English business students in order to define which approach best motivates them to practice grammar;– To design and implement a set of communicative strategies and activities for teaching grammar both from an inductive and a deductive approach while motivating them to practice it;– To assess the implementation of the communicative strategies and activities and the way they affect students’ learning;– To offer alternative activities for the classes, instead of those commonly used from the Oxford books. For instance, short grammar rules and explanation charts, are used in the warming phase when applying the deductive approach. metadata Torres da Silva, Silvino mail silvino.torres@gmail.com (2022) Action research for implementing communicative strategies for teaching grammar from both an inductive and deductive approach in a group of business English adult learners. Masters thesis, Universidad Internacional Iberoamericana Puerto Rico.

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009–2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care metadata Fatima, Anum and Shafi, Imran and Afzal, Hammad and Díez, Isabel De La Torre and Lourdes, Del Rio-Solá M. and Breñosa, Jose and Martínez Espinosa, Julio César and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, UNSPECIFIED (2022) Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives. Healthcare, 10 (11). p. 2188. ISSN 2227-9032

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users’ mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access control) address, providing an in-depth analysis of similarity behaviors on a daily basis. We analyzed the similarity distributions of users on different days of the week for different locations based on their real movements. The results show similar characteristics of users with common mobility behaviors based on location and time to showcase the efficacy. The results show that the proposed SAA approach is 33% more accurate in terms of recognizing the user’s similarity as compared to the existing similarity approach. metadata Memon, Ambreen and Kilby, Jeff and Breñosa, Jose and Martínez Espinosa, Julio César and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, UNSPECIFIED (2022) Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix. Sensors, 22 (24). p. 9898. ISSN 1424-8220

Article Subjects > Social Sciences
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Innovation plays a pivotal role in the progress and goodwill of an organization, and its ability to thrive. Consequently, the impact analysis of innovation on the performance of an organization holds great importance. This paper presents a two-stage analytical framework to examine the impact of business innovation on a firm’s performance, especially firms from the manufacturing sector. The prime objective is to identify the factors that have an impact on firm-level innovation, and to examine the impact of firm-level innovation on business performance. The framework and its analysis are based on the latest World Bank enterprise survey, with a sample size of 696 manufacturing firms. The first stage of the proposed framework establishes the analytical results through Bivariate Probit, which indicates that research and development (R&D) has a significantly positive impact on the product, process, marketing, and organizational innovations. It thus highlights the important role of the allocation of lump-sum amounts for R&D activities. The statistical analysis shows that innovation does not depend on the size of the firms. Moreover, the older firms are found to be wiser at conducting R&D than newer firms that are reluctant to take risks. The second stage of the proposed framework separately analyzes the impacts of the product and organizational innovation, and the process and marketing innovation on the firm performance, and finds them to be statistically significant and insignificant, respectively. metadata Aslam, Mahrukh and Shafi, Imran and Ahmad, Jamil and Álvarez, Roberto Marcelo and Miró Vera, Yini Airet and Soriano Flores, Emmanuel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, emmanuel.soriano@uneatlantico.es, UNSPECIFIED (2022) An Analytical Framework for Innovation Determinants and Their Impact on Business Performance. Sustainability, 15 (1). p. 458. ISSN 2071-1050

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Industries need solutions that can automatically monitor oil leakage from deployed underwater pipelines and to rapidly report any damage. The location prediction of mineral reservoirs like oil, gas, or metals in deep water is a challenge during the extraction of these resources. Moreover, the problem of ores and mineral deposits on the seafloor comes into play. The abovementioned challenges necessitate for the deployment of underwater wireless sensor networks (UWSNs). Anchor-based localization techniques are segregated into range-free and range-based processes. Range-based schemes depend on various techniques like angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and received signal strength indicator (RSSI). In this article, the localization of these leakages is performed by using range-based metrics for calculating the distance among anchor nodes (ANs) and target nodes (TNs). This estimated distance is further optimized to minimize the estimation error. A multilateralism procedure is used to estimate the optimal position of each TN. The results exhibit that the proposed algorithm shows a high performance when compared to previous works, in terms of minimum energy consumption, lower packet loss, rapid location estimation, and lowest localization error. The benefit of using the proposed methodology greatly impacts on identifying the leakage area in mobility-assisted UWSN, where rapid reporting helps to lower the loss of resources. metadata Goyal, Nitin and Nain, Mamta and Singh, Aman and Abualsaud, Khalid and Alsubhi, Khalid and Ortega-Mansilla, Arturo and Zorba, Nizar mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, arturo.ortega@uneatlantico.es, UNSPECIFIED (2022) An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring. IEEE Canadian Journal of Electrical and Computer Engineering, 45 (4). pp. 466-474. ISSN 2694-1783

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected. metadata Mir, Tahira Sarwar and Liaqat, Hannan Bin and Kiren, Tayybah and Sana, Muhammad Usman and Álvarez, Roberto Marcelo and Miró Vera, Yini Airet and Pascual Barrera, Alina Eugenia and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, alina.pascual@unini.edu.mx, UNSPECIFIED (2022) Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing. Sensors, 22 (22). p. 8778. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés With rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches. metadata Rustam, Furqan and Ishaq, Abid and Kokab, Sayyida Tabinda and de la Torre Diez, Isabel and Vidal Mazón, Juan Luis and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) An Artificial Neural Network Model for Water Quality and Water Consumption Prediction. Water, 14 (21). p. 3359. ISSN 2073-4441

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes metadata Chaganti, Rajasekhar and Rustam, Furqan and Daghriri, Talal and Díez, Isabel de la Torre and Vidal Mazón, Juan Luis and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model. Sensors, 22 (19). p. 7692. ISSN 1424-8220

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions metadata Farooq, Muhammad Shoaib and Suhail, Maryam and Qureshi, Junaid Nasir and Rustam, Furqan and de la Torre Díez, Isabel and Vidal Mazón, Juan Luis and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics. Sensors, 22 (21). p. 8582. ISSN 1424-8220

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This FP was elaborated to help teachers to improve the teaching-learning process of ADHD in an Ecuadorian public school. It contains a material design with some engaging activities for teachers in order to foster motivation and avoid the indiscipline of ADHD students inside the classroom. metadata López Cárdenas, Nancy Viviana mail geminis_nlc_@hotmail.com (2022) Designing and Implementing a 3rd grade EFL A1 Communicative and Task-Based Booklet with Engaging Activities for 7-year-old Students with ADHD in an Ecuadorian Public School. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Developing and Implementing Effective Classroom Techniques through Communicative Language Teaching (CLT) for Acquiring Oral Proficiency among Adult ESL Learners Coursing a Hybrid ProgramAdult ESL learners are continuously coming to language schools in order to learn the new language. Their interests in learning to speak English include a variety of reasons. In short, to adjust to a new culture and to acquire the skills to survive and thrive in that new culture. It is well known that many approaches to teaching have arisen from researchers’ studies to ensure to development of oral competence. Communicative Language Teaching is an approach to teaching that focuses on developing speaking skills among learners. Both educators and adult learners admit that developing speaking skills in English is not an easy task. Adult learners usually struggle to maintain a conversation in English. Many cognitive, social, and personal factors are involved in adult language teaching. The topic aims to analyze those factors that interfere with the development of oral proficiency among adult ESL learners who take classes partially online under Communicative Language teaching methodology at a language school in Newark, New Jersey. It also aims to design classroom techniques that ensure the development of this competency. It collects data regarding students’ thoughts on the CLT methodology, the social barriers they face while learning a new language, and the learning strategies they use to develop oral competence. Also, the work seeks to shed some light on teachers’ techniques to help students overcome the obstacles that prevent them from developing oral competence. A quantitative, descriptive research approach was carried out for the completion of this project. We describe the situation and the nature of its existence at the time of the study. We give details regarding the type of students at the institution, and we explain in full detail the way classes are carried out. We did in-depth interviews with students and teachers as well, to find out the cause of the problem. A qualitative approach was also taken into consideration. We used qualitative research tools such as surveys and readily data from the institution. Results show that students are overall satisfied with the efficiency of the CLT methodology for promoting oral competence. On the other hand, one of the main red flag aspects shown in the results is that students are not practicing English outside of the classroom context. They lack the real-life context to practice or they are too shy to use the language that they have already acquired. Also, the learning strategies they use to learn and practice English are not effective enough. They mainly rely on translation to their mother tongue when it comes to learning vocabulary or grammar. The techniques used by teachers at the center are efficient in developing speaking skills, however, the institution provides the teaching methodology for teachers and requires them to stick to it when instructing students. This leaves teachers with a narrow frame to use and implement their teaching style and to broadly reach students’ oral competence needs. Keywords: CLT Methodology, Learning Cognitive Factors, Oral Proficiency, Teaching Techniques, Blended Learning. metadata Uceta De Rodríguez, Gidelca Mabel mail cutemabe@hotmail.es (2022) Developing and Implementing Effective Classroom Techniques through Communicative Language Teaching (CLT) for Acquiring Oral Proficiency among Adult ESL Learners Coursing a Hybrid Program. Masters thesis, UNSPECIFIED.

Article Subjects > Social Sciences Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés This article proposes a discussion on the form of coexistence of local Development Agencies in Uruguay, with local governments in the face of the new scenarios marked by the decentralization process, initiated in the country with the Constitutional Reform of 1996 and culminating in February 2009, with the Law of Political Decentralization and Citizen Participation. The discussion applies in particular to the local development agency of the city of Rivera (ADR), located in the northeast of the country. A descriptive, mixed, bibliographic, documentary investigation was carried out with primary data collection to internal and external references to ADR. The results show that the coexistence of both institutions has been difficult, without defining clear roles. Promoting dialogue to define the role of each seems to be the great challenge facing the sustainability of the agency metadata Garat de Marin, Mirtha Silvana and Soriano Flores, Emmanuel and Rodríguez Velasco, Carmen Lilí and Silva Alvarado, Eduardo and Calderón Iglesias, Rubén and Álvarez, Roberto Marcelo and Gracia Villar, Santos mail silvana.marin@uneatlantico.es, emmanuel.soriano@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED, ruben.calderon@uneatlantico.es, roberto.alvarez@uneatlantico.es, santos.gracia@uneatlantico.es (2022) Development Agencies and Local Governments—Coexistence within the Same Territory. Social Sciences, 11 (9). p. 398. ISSN 2076-0760

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés The Internet of Things (IoT) is a revolutionary technique of sharing data for smart devices that generates huge amounts of data from smart healthcare systems. Therefore, healthcare systems utilize the convergence power and traffic analysis of sensors that cannot be satisfactorily handled by the IoT. In this article, a novel mutation operator is devised and incorporated with the differential evolution (DE) algorithm. Two tests have been conducted in the validation process. Firstly, the newly dual adaption-based operators incorporated with the differential evolution algorithm are being proposed. The proposed approach provides sufficient diversity and enhances the search speed of nature’s local and global search environments in the problem. The proposed method incorporates the application of IoT-based smart healthcare. Second, an application-based test has been conducted, in which the proposed approach is applied to the application in the smart healthcare system. Therefore, IoT sensor deployment is an optimization problem to minimize service time, delay, and energy loss by considering the communication constraint between sensors(objects). The proposed algorithm is applied in this article to solve this optimization problem. Further, in the experimentation and comparative study, the proposed method is superior to the standard evolutionary algorithms in IoT applications concerning the minimum number of function evaluations and minimization of traffic services. The proposed approach also achieves efficiency in the minimum loss of energy in each service and reduces load and delay metadata Singh, Shailendra Pratap and Viriyasitavat, Wattana and Juneja, Sapna and Alshahrani, Hani and Shaikh, Asadullah and Dhiman, Gaurav and Singh, Aman and Kaur, Amandeep mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED (2022) Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city. Physical Communication, 55. p. 101893. ISSN 18744907

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners’ preferences. metadata Kaur, Ramneet and Gupta, Deepali and Madhukar, Mani and Singh, Aman and Abdelhaq, Maha and Alsaqour, Raed and Breñosa, Jose and Goyal, Nitin mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, UNSPECIFIED (2022) E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation. Electronics, 11 (20). p. 3354. ISSN 2079-9292

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés This work offers a material design that contemplates tasks and activities with detailed explanations to a group of students in a bilingual scenario undergoing a CLIL approach as the language being a conductor to content learning, with the intention of teaching poetry as part of a Canadian Curriculum. metadata dos Santos Correia de Lima, Ana Rita mail ana.rita22@gmail.com (2022) An EFL Material Design Integrating Poetry in a Didactic Communicative, Task-Based and CLIL Unit. Masters thesis, UNSPECIFIED.

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients. metadata Shafi, Imran and Din, Sadia and Khan, Asim and Díez, Isabel De La Torre and Pali-Casanova, Ramón and Tutusaus, Kilian and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ramon.pali@unini.edu.mx, kilian.tutusaus@uneatlantico.es, UNSPECIFIED (2022) An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network. Cancers, 14 (21). p. 5457. ISSN 2072-6694

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Facial emotion recognition (FER) is an important and developing topic of research in the field of pattern recognition. The effective application of facial emotion analysis is gaining popularity in surveillance footage, expression analysis, activity recognition, home automation, computer games, stress treatment, patient observation, depression, psychoanalysis, and robotics. Robot interfaces, emotion-aware smart agent systems, and efficient human–computer interaction all benefit greatly from facial expression recognition. This has garnered attention as a key prospect in recent years. However, due to shortcomings in the presence of occlusions, fluctuations in lighting, and changes in physical appearance, research on emotion recognition has to be improved. This paper proposes a new architecture design of a convolutional neural network (CNN) for the FER system and contains five convolution layers, one fully connected layer with rectified linear unit activation function, and a SoftMax layer. Additionally, the feature map enhancement is applied to accomplish a higher detection rate and higher precision. Lastly, an application is developed that mitigates the effects of the aforementioned problems and can identify the basic expressions of human emotions, such as joy, grief, surprise, fear, contempt, anger, etc. Results indicate that the proposed CNN achieves 92.66% accuracy with mixed datasets, while the accuracy for the cross dataset is 94.94%. metadata Qazi, Awais Salman and Farooq, Muhammad Shoaib and Rustam, Furqan and Gracia Villar, Mónica and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) Emotion Detection Using Facial Expression Involving Occlusions and Tilt. Applied Sciences, 12 (22). p. 11797. ISSN 2076-3417

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés The demand for digitization has inspired organizations to move towards cloud computing, which has increased the challenge for cloud service providers to provide quality service. One of the challenges is energy consumption, which can shoot up the cost of using computing resources and has raised the carbon footprint in the atmosphere; therefore, it is an issue that it is imperative to address. Virtualization, bin-packing, and live VM migration techniques are the key resolvers that have been found to be efficacious in presenting sound solutions. Thus, in this paper, a new live VM migration algorithm, live migration with efficient ballooning (LMEB), is proposed; LMEB focuses on decreasing the size of the data that need to be shifted from the source to the destination server so that the total energy consumption of migration can be reduced. A simulation was performed with a specific configuration of virtual machines and servers, and the results proved that the proposed algorithm could trim down energy usage by 18%, migration time by 20%, and downtime by 20% in comparison with the existing approach of live migration with ballooning (LMB) metadata Gupta, Neha and Gupta, Kamali and Qahtani, Abdulrahman M. and Gupta, Deepali and Alharithi, Fahd S. and Singh, Aman and Goyal, Nitin mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED (2022) Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center. Electronics, 11 (23). p. 3932. ISSN 2079-9292

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Learning a second language entails much more than remembering lists of words, rules, and grammar explanations. In fact, learning a language involves knowing how to express one's ideas clearly and comprehend input (either oral or written) in an effective way. Hence, in order for students to actually learn a language, teachers of English as a foreign language should provide students with plenty of opportunities to engage in real-life communicative situations. Those ‘opportunities’ can also be referred to as 'tasks', which have similar characteristics, such as being situations in real-life communication, focusing primarily on fluency to accomplish accuracy, placing the learner as an autonomous being, relying on others to negotiate meaning, repurposing language, and so on. Hence, tasks represent an approach to language learning that involves different steps or stages, which range from vocabulary-related activities to engage students in the topic of the lesson to more complex tasks that require learners using the target language to solve a problem, choose the best option out of a set, and so on.Accordingly, this "materials design" final project has been composed in order to provide a complete learning unit for 10th grade students. Additionally, the materials with opportunities to interact with peers in the English language through a set of communicative tasks. metadata Ríos Hernández, Paulina Alejandra mail rioshernandez.pauli@gmail.com (2022) English Learning: B1 EFL Digital Materials for Homeschooling 10-Grade Students in Chile. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés In the following paper, it has been designed an English syllabus for third grade students of a secondary school from Uruguay. It has also been included the detailed lesson plans for two units, with their corresponding materials. metadata Plada Sequeira, Leticia Daicy mail leticiaplada88@gmail.com (2022) English syllabus for 3rd Grade Students in Secondary School in Uruguay. Masters thesis, UNSPECIFIED.

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects. metadata García Villena, Eduardo and Pascual Barrera, Alina Eugenia and Álvarez, Roberto Marcelo and Dzul López, Luis Alonso and Tutusaus, Kilian and Vidal Mazón, Juan Luis and Miró Vera, Yini Airet and Brie, Santiago and López Flores, Miguel A. mail eduardo.garcia@uneatlantico.es, alina.pascual@unini.edu.mx, roberto.alvarez@uneatlantico.es, luis.dzul@uneatlantico.es, kilian.tutusaus@uneatlantico.es, juanluis.vidal@uneatlantico.es, yini.miro@uneatlantico.es, santiago.brie@uneatlantico.es, miguelangel.lopez@uneatlantico.es (2022) Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean. Applied Sciences, 12 (21). p. 11188. ISSN 2076-3417

Article Subjects > Biomedicine Ibero-american International University > Research > Articles and books Abierto Inglés Background: The rapid spread of the SARS-CoV-2 virus, especially during the first year of the COVID-19 pandemic, has caused an unprecedented health crisis worldwide. Fear of getting infected, and the high mortality rates in some places, prompted the general population to engage in self-medication practices. Methods: We conducted a cross-sectional analysis of Ecuador's prescription and self-medication consumption trends during the first two years COVID-19 pandemic. Data came from an integrated countrywide database of the physician prescribing trends, the use of over-the-counter medicines, (OTC) and the medicine-related spending levels through the COVID-19 pandemic in Ecuador. We compared the absolute difference in monthly and yearly demand and calculated excessive expenditure from previous years. Findings: We found that in Ecuador, the pre-pandemic (2017-2019) yearly expenditure among these ATC groups was, on average, $150’646,206 while during 2020 and 2021, the same groups represented $228.327.210, a significant 52% increase. Of this amount, 13% were OTC Medicines, and 87% required a formal prescription. The most remarkable growth in drug sales came from ivermectin with 2,057%, followed by hydroxychloroquine with 171%. Interpretation: Our study shows that people consumed large quantities of medicines during the first two years of the pandemic in Ecuador, including drugs with no proven benefit to treat or reduce the risk of progression due to COVID-19. We suggest that the lack of local prescription guidelines and prescription control, as well as generalized fear and misinformation led doctors and patients to prescribe and consume vast amounts of unnecessary medicines. metadata Ortiz-Prado, Esteban and Izquierdo Condoy, Juan Sebastian and Mora, Carla and Vásconez-González, Jorge and Fernández, Raúl mail UNSPECIFIED (2022) Excessive Sales of Pharmaceutical Drugs in a Low and Middle-Income Country During the COVID-19 Pandemic: The Case of Ecuador. SSRN Electronic Journal. ISSN 1556-5068 (Submitted)

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5 G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice Quality of Service (QoS) for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5 G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot sub-problems, realizing a trade-off between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve non-convex fair sub-problems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme. metadata Lin, Xi and Wu, Jun and Bashir, Ali Kashif and Yang, Wu and Singh, Aman and AlZubi, Ahmad Ali mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED (2022) FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things. IEEE Transactions on Industrial Informatics. pp. 1-10. ISSN 1551-3203

Article Subjects > Biomedicine Ibero-american International University > Research > Articles and books Abierto Inglés Vaccination coverage in Ecuador has decreased since 2013, falling short of the World Health Organization’s vaccination goal. There are several causes for this deficiency in coverage, one of these are lost vaccination opportunities, which are caused when a patient without contraindications postpones, or for other reasons fails to receive a recommended immunization. The objective of this study was to determine the state of knowledge regarding vaccination contraindications among the Metropolitan District of Quito health personnel to assess missed vaccination opportunities. Through this cross-sectional descriptive study, health personnel were surveyed online and asked 18 clinical scenarios which were created to evaluate their knowledge of the true contraindications of vaccination, and measure missed opportunities. A total of 273 surveys were collected; 74% belonged to the public health system, and the rest represented by private practitioners. Of those surveyed, 98.2% of health personnel had improperly denied vaccination at least once. We specifically found vaccinations were incorrectly denied more frequently in cases where the hypothetical patient presented mild or moderate fever cases. The use of corticosteroids, autoimmune diseases, and egg allergy were also incorrectly denied (89%, 71.4%, 72.9%, and 58.6%, respectively). Among the health personnel surveyed, there is an apparent lack of knowledge of the true contraindications of vaccination and differences in knowledge about contraindications according to personnel in charge of administering immunization to children. Our preliminary results suggest that lack of education related to side effects could be biasing medical professionals’ decisions, causing them to unnecessarily delay or deny vaccinations, which likely contributes to explaining low overall vaccination coverage in Quito, the capital city of Ecuador. metadata Andrade-Guerrero, Felipe and Tapia, Adriana and Andrade, Vinicio and Vásconez-González, Jorge and Andrade-Guerrero, José and Noroña-Calvachi, Carlos and Izquierdo Condoy, Juan Sebastian and Yeager, Justin and Ortiz-Prado, Esteban mail UNSPECIFIED (2022) False Contraindications for Vaccinations Result in Sub-Optimal Vaccination Coverage in Quito, Ecuador: A Cross-Sectional Study. Vaccines, 11 (1). p. 60. ISSN 2076-393X

Article Subjects > Engineering Ibero-american International University > Research > Articles and books Abierto Inglés, Portugués Objective: The general objective of this article is to study the implementation of free geotechnologies, based on free software, in basic sanitation management. Theoretical benchmark: The research brings perspectives aiming to demonstrate that the implementation of geotechnologies, based on free and free software, in the management of basic sanitation can reduce the costs of implementing information technology, as well as assist the process of combating water losses and waste. Method: This study consisted of exploratory research with application in a case study at the Bahia Water and Sanitation Company. To assess the hypotheses raised by the survey, data from the company's information systems were used, as well as the answers from the online questionnaire applied to professionals in the area of geotechnologies. Results and conclusion: The results pointed out that the free geotechnologies, based on free and free software, implemented in the basic sanitation company allowed a better management of basic sanitation, being important for the specific process of combating water losses. Research Implications: The research contributes with the literature review and practical application of free and free geotechnologies applied in the management of basic sanitation, which allow the economicity and scalability of technological projects of this nature. Originality/value: The results obtained in the present study are unprecedented, innovative and relevant for the scientific community, in the context of the use of free and free geotechnologies, in the management of basic sanitation and its process of combating water losses. metadata Guimarães Aragão, Helder and Pereira, Vilmar Alves and Florencio da Silva, Rodrigo mail UNSPECIFIED, vilmar.alves@unini.edu.mx, UNSPECIFIED (2022) Free Geotechnologies Applied to Basic Sanitation Management: a Case Study at the Empresa Baiana de Águas e Saneamento. RGSA –Revista de Gestão Social e Ambiental, 16 (2). pp. 1-16. ISSN 1981-982X

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés This study intends to explore the impact of social media on the foreign languageacquisition of higher education students. The objectives of the research wereaccomplished by conducting a survey with these students to discover how social mediacould be a useful tool not only to learn English, but to reinforce knowledge and developthe language skills of speaking, listening, writing, and reading throughout students’virtual interaction among themselves and their online contexts. metadata Julio Blanco, Liliana mail lilojuliob@gmail.com (2022) The Impact of Social Media on the Foreign Language Acquisition of Higher Education Students. Masters thesis, UNSPECIFIED.

Article Subjects > Social Sciences
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Financial management is a critical aspect of firms, and entails the strategic planning, direction, and control of financial endeavors. Risk assessment, fraud detection, wealth management, online transactions, customized bond scheme, customer retention, virtual assistant and so on, are a few of the critical areas where Industry 4.0 technologies intervention are highly required for managing firms' finance. It has been identified from the previous studies that they are limited studies that have addressed the significance and application of integrating of Industry 4.0 technologies such as Internet of Things (IoT), cloud computing, big data, robotic process automation (RPA), artificial intelligence (AI), Blockchain, Digital twin, and Metaverse. With the motivation from the above aspects, this study aims to discuss the role of these technologies in the area of financial management of a firm. Based up on the analysis, it has been concluded that these technologies assist to credit risk management based on real-time data; financial data analytics of risk assessment, digital finance, digital auditing, fraud detection, and AI- and IoT- based virtual assistants. This study recommended that digital technologies be deeply integrated into the financial sector to improve service quality and accessibility, as well as the creation of innovative rules that allow for healthy competition among market participants. metadata Bisht, Deepa and Singh, Rajesh and Gehlot, Anita and Akram, Shaik Vaseem and Singh, Aman and Caro Montero, Elisabeth and Priyadarshi, Neeraj and Twala, Bhekisipho mail UNSPECIFIED (2022) Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective. Electronics, 11 (19). p. 3252. ISSN 2079-9292

Article Subjects > Social Sciences
Subjects > Engineering
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Energy is a crucial element for human needs today. Traditional systems of energy generation have represented a problem in terms of their costs, their impact on the environment, and their impact on community life. Therefore, the search for clean and renewable energy sources that meet the needs of contemporary society becomes increasingly essential in the search for alternatives related to energy sources. The photovoltaic energy generation system explores the solar irradiation, making it possible to generate and store energy. This system finds good conditions for implementation in Brazil in terms of climatic characteristics, but investments and public policies that encourage and favor this process are still needed. This study aimed to identify how the deployment of photovoltaic mini-generation power plant in a federal university, the Federal University of Paraná (UFPR), can contribute to the university community in relation to cost reduction and environmental preservation. The methodology used was descriptive-exploratory, qualitative, through which an open questionnaire and a semi-structured interview were carried out, guided by the theme. After analyzing the data, the conclusion was that the system can bring benefits in the long term and that most of the interviewees consider Brazil's great potential in expanding the exploration of other sources of energy, besides hydroelectric, which, besides being costly, brings fewer advantages related to the environmental and social contexts. metadata Miura, Augusto Takashi and Pereira, Vilmar Alves and Florencio da Silva, Rodrigo mail UNSPECIFIED, vilmar.alves@unini.edu.mx, UNSPECIFIED (2022) Implementation of photovoltaic energy, sustainability, economic and social development in a Higher Education Institution in Brazil. Latin American Journal of Development, 4 (4). pp. 1514-1532. ISSN 2674-9297

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés In Smart Cities’ applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node’s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of α=1.5 and γ=2.5 with the varying number of nodes. metadata Rani, Shalli and Babbar, Himanshi and Shah, Syed Hassan Ahmed and Singh, Aman mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es (2022) Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities. Scientific Reports, 12 (1). ISSN 2045-2322

Article Subjects > Social Sciences Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Angola, as with many countries on the African continent, has great inequalities or asymmetries between its provinces. At the economic, financial, and technological level, there is a great disparity between them, where it is observed that the province of Luanda is the largest financial business center to the detriment of others, such as Moxico, Zaire, and Cabinda. In the latter, despite the advantages of high oil production, from a regional point of view, they remain almost stagnant in time, in a social dysfunction where the population lives on extractivism and artisanal fishing. This article analyzes the most important events in contemporary regional history, the Portuguese occupation that was the Portuguese colonial rule over Angola (1890–1930) and the civil war that was a struggle between Angolans for control of the country (1975–2002), in the consolidation of the asymmetries between provinces. For this work, a theoretical-reflective study was conducted based on the reading of books, articles, and previous investigations on the phenomenon studied. Considering the interpretation and analysis of the theoretical content obtained through the bibliographic research conducted, this theoretical construction approaches the qualitative approach. We conclude that the deep inequalities between regions and within them, between the provinces studied, originated historically in the form of exploitation of the regions and from the consequences of the war. The asymmetries, observed through the variables studied show that the provinces historically explored and considered object regions present a lower growth compared to those that were considered subject regions in which the applied geopolitical strategy, as they are centers of primary production flows, was different. We also observe that, due to the conflicts of the civil war in the less developed regions, the inequalities have deepened, contributing seriously to a higher level of poverty and a lower development of the provinces where these conflicts took place. metadata Catoto Capitango, João Adolfo and Garat de Marin, Mirtha Silvana and Soriano Flores, Emmanuel and Rojo Gutiérrez, Marco Antonio and Gracia Villar, Mónica and Durántez Prados, Frigdiano Álvaro mail UNSPECIFIED, silvana.marin@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, monica.gracia@uneatlantico.es, durantez@uneatlantico.es (2022) Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War. Social Sciences, 11 (8). p. 334. ISSN 2076-0760

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés This paper presents the design, development, and testing of an IoT-enabled smart stick for visually impaired people to navigate the outside environment with the ability to detect and warn about obstacles. The proposed design employs ultrasonic sensors for obstacle detection, a water sensor for sensing the puddles and wet surfaces in the user’s path, and a high-definition video camera integrated with object recognition. Furthermore, the user is signaled about various hindrances and objects using voice feedback through earphones after accurately detecting and identifying objects. The proposed smart stick has two modes; one uses ultrasonic sensors for detection and feedback through vibration motors to inform about the direction of the obstacle, and the second mode is the detection and recognition of obstacles and providing voice feedback. The proposed system allows for switching between the two modes depending on the environment and personal preference. Moreover, the latitude/longitude values of the user are captured and uploaded to the IoT platform for effective tracking via global positioning system (GPS)/global system for mobile communication (GSM) modules, which enable the live location of the user/stick to be monitored on the IoT dashboard. A panic button is also provided for emergency assistance by generating a request signal in the form of an SMS containing a Google maps link generated with latitude and longitude coordinates and sent through an IoT-enabled environment. The smart stick has been designed to be lightweight, waterproof, size adjustable, and has long battery life. The overall design ensures energy efficiency, portability, stability, ease of access, and robust features. metadata Farooq, Muhammad Siddique and Shafi, Imran and Khan, Harris and Díez, Isabel De La Torre and Breñosa, Jose and Martínez Espinosa, Julio César and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, UNSPECIFIED (2022) IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition. Sensors, 22 (22). p. 8914. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead. metadata Alsukayti, Ibrahim S. and Singh, Aman mail UNSPECIFIED, aman.singh@uneatlantico.es (2022) A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks. IEEE Access, 10. pp. 111115-111133. ISSN 2169-3536

Article Subjects > Biomedicine Ibero-american International University > Research > Articles and books Abierto Inglés Background: Several reports from around the world have reported that some patients who have recovered from COVID-19 have experienced a range of persistent or new clinical symptoms after a SARS-CoV-2 infection. These symptoms can last from weeks to months, impacting everyday functioning to a significant number of patients. Methods: A cross-sectional analysis based on an online, self-reporting questionnaire was conducted in Ecuador from April to July 2022. Participants were invited by social media, radio, and TV to voluntarily participate in our study. A total of 2103 surveys were included in this study. We compared socio-demographic variables and long-term persisting symptoms at low (<2500 m) and high altitude (>2500 m). Results: Overall, 1100 (52.3%) responders claimed to have Long-COVID symptoms after SARS-CoV-2 infection. Most of these were reported by women (64.0%); the most affected group was young adults between 21 to 40 years (68.5%), and most long-haulers were mestizos (91.6%). We found that high altitude residents were more likely to report persisting symptoms (71.7%) versus those living at lower altitudes (29.3%). The most common symptoms were fatigue or tiredness (8.4%), hair loss (5.1%) and difficulty concentrating (5.0%). The highest proportion of symptoms was observed in the group that received less than 2 doses. Conclusions: This is the first study describing post-COVID symptoms’ persistence in low and high-altitude residents. Our findings demonstrate that women, especially those aging between 21–40, are more likely to describe Long-COVID. We also found that living at a high altitude was associated with higher reports of mood changes, tachycardia, decreased libido, insomnia, and palpitations compared to lowlanders. Finally, we found a greater risk to report Long-COVID symptoms among women, those with previous comorbidities and those who had a severer acute SARS-CoV-2 infection. metadata Izquierdo Condoy, Juan Sebastian and Fernandez-Naranjo, Raul and Vasconez-González, Eduardo and Cordovez, Simone and Tello-De-la-Torre, Andrea and Paz, Clara and Delgado-Moreira, Karen and Carrington, Sarah and Viscor, Ginés and Ortiz-Prado, Esteban mail UNSPECIFIED (2022) Long COVID at Different Altitudes: A Countrywide Epidemiological Analysis. International Journal of Environmental Research and Public Health, 19 (22). p. 14673. ISSN 1660-4601

Revista Subjects > Nutrition Europe University of Atlantic > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Universidad Internacional do Cuanza > Research > Scientific Magazines
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Abierto Inglés La revista MLS Health and Nutrition Research nace como una publicación semestral con el objetivo de publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución científica al progreso de cualquier ámbito de la salud y nutrición como objetivo principal. Los artículos incluidos en esta revista se publican en español, portugués e inglés. La vocación internacional de esta revista promueve la difusión del conocimiento en sus diferentes áreas. metadata UNSPECIFIED mail mls@devnull.funiber.org (2022) MLS Health and Nutrition Research. [Revista]

Revista Subjects > Teaching Europe University of Atlantic > Research > Scientific Magazines
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Universidad Internacional do Cuanza > Research > Scientific Magazines
Abierto Inglés Antigua Revista internacional de apoyo a la inclusión, logopedia, sociedad y multiculturalidad La revista MLS Inclusion and Society Journal es la continuación de la Revista internacional de apoyo a la inclusión, logopedia, sociedad y multiculturalidad (RIAI), revista heredera de la revista RIALAIM con mayor antigüedad, pero de la cual se independizó para tomar las directrices de las revistas actuales con indicadores de impacto. La revista MLS Inclusion and Society Journal cuenta actualmente con artículos de investigación y teóricos, tanto internacionales como nacionales, que están arbitrados por pares ciegos externos a la revista, en un proceso riguroso de selección. Los ejes temáticos son: educación inclusiva, logopedia, sociedad y multiculturalidad. La MLS Inclusion and Society Journal tiene una periodicidad de dos números al año (junio y diciembre) metadata UNSPECIFIED mail mls@devnull.funiber.org (2022) MLS Inclusion and Society Journal. [Revista]

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés This research project aims to analyze the awareness and usage of the student-centered approach at a bilingual high school of Honduras. It first, focuses in a deep revision of the textbook used by the ELA professor. Then, it directs its attention to determine the awareness and accountability of the students towards their own learning. Proceeding by the put into action a professional development for the teachers of this institution. Finalizing by contrasting the results of the students´ performance after being exposed to a classroom using student-centered techniques. metadata Ramirez Rivera, Roberto mail mrbetoramirez@gmail.com (2022) A Material Analysis on the Application of Learner-Centered Teaching Strategies used in an EFL Secondary Honduran Class: Pearson series “My Perspective”. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés A recent skyrocket growth in demand for bilingual education in Brazil is currently taking place. Young learners' families are no longer willing to afford both a regular education school and a language institution.Since the teacher market is not prepared to attend to this new context, schools are reaching out for solutions, within the current Brazilian legislation BNCC and the Guidelines for a Plurilingual Education. The modality being called "Bilingual Program" is arising; in it, regular EFL and bilingual teaching is being "mixed", in an attempt to cater for a diversity of learners' necessities, as well as adjust to the workforce available.Renowned publishing houses have been developing materials and offering "solutions", pursuing partners and material consumers. In exchange, some teacher training is offered.Marist, a regular educational institution in Federal District, Brazil, adopted Super Minds, a seven-level program for elementary learners, by Cambridge Press, for its bilingual program. Considering Super Minds is an EFL material, this present work aims at analyzing its viability in a bilingual setting, and the role of the teachers in this adaptation. metadata Souza Silveira, Carla mail carlaenglishteacher@outlook.com (2022) Material Analysis: Super MInds 3. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés In the educational field, English is considered as a communicative system. It is named as an essential patron to discuss science, art, knowledge and commerce internationally. In Colombia, students were ranked in last place throughout Latin-American countries in the use of the second language (English). The purpose of this proposal was to maximize students oral skills through virtual teaching tips in an innovative platform to support role play in the classroom. There were 35 students per classroom between ages 13-19 and they were taken as main participants of this research. Furthermore, research design was applied as a prior part of this proposal. Data was analyzed through the following instruments; classroom observation and journal, videotaping lessons, lesson plans, and digital platforms. Findings revealed a maximization of high school students’ oral skills and an increase level of autonomy, collaboration, and interaction in their educational language process developed in a virtual platform. metadata Vega Corzo, Millyndy Katherine Alexandra mail millyndy.vegacorzo@gmail.com (2022) Maximizing students oral skills through an english teacher’s work in a virtual platform. Masters thesis, UNSPECIFIED.

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Mobility and low energy consumption are considered the main requirements for wireless body area sensor networks (WBASN) used in healthcare monitoring systems (HMS). In HMS, battery-powered sensor nodes with limited energy are used to obtain vital statistics about the body. Hence, energy-efficient schemes are desired to maintain long-term and steady connectivity of the sensor nodes. A sheer amount of energy is consumed in activities such as idle listening, excessive transmission and reception of control messages, packet collisions and retransmission of packets, and poor path selection, that may lead to more energy consumption. A combination of adaptive scheduling with an energy-efficient protocol can help select an appropriate path at a suitable time to minimize the control overhead, energy consumption, packet collision, and excessive idle listening. This paper proposes a region-based energy-efficient multipath routing (REMR) approach that divides the entire sensor network into clusters with preferably multiple candidates to represent each cluster. The cluster representatives (CRs) route packets through various clusters. For routing, the energy requirement of each route is considered, and the path with minimum energy requirements is selected. Similarly, end-to-end delay, higher throughput, and packet-delivery ratio are considered for packet routing. metadata Akbar, Shuja and Mehdi, Muhammad Mohsin and Jamal, M. Hasan and Raza, Imran and Hussain, Syed Asad and Breñosa, Jose and Martínez Espinosa, Julio César and Pascual Barrera, Alina Eugenia and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, ulio.martinez@unini.edu.mx, alina.pascual@unini.edu.mx, UNSPECIFIED (2022) Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring. Healthcare, 10 (11). p. 2297. ISSN 2227-9032

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés This is an effort to analyze the reaction of stock prices of Indian public and private banks listed in NSE and BSE to the announcement of seven best case news events. Several recent studies have analyzed the correlation between stock prices and news announcements; however, there is no evidence on how private and public sector Indian bank stocks react to important news events independently. We examine these features by concentrating on a sample of banking and government news events. We classify these news events to create a group of negative and a group of positive tone of announcements (sentiments). The statistical results show that the negative banking news announcements had a one-month impact on private banks, with statistically significant negative mean CARs. However, with highly statistically substantial negative mean CARs, the influence of the negative banking news announcements on public banks was observed for two months after the news was published. Furthermore, the influence of the positive banking news on private banks persisted a month after the news was published. Positive banking news events had an influence on public banks for five days after they were published. The study concludes that public bank stocks react more to negative news announcements than positive news announcements in the same manner as the sentimental polarity of the news announcements as compared to private bank stocks. First, we retrieved the news articles published in prominent online financial news portals between 2017 and 2020, and the seven major news events were extracted and classified using multi-class text classification. The Random Forest classifier produced a significant accuracy of 94% with pre-trained embeddings of DistilBERT, a neural network model, which outperformed the traditional feature representation technique, TF-IDF. The training data for the classifier were balanced using the SMOTE sampling technique metadata Dogra, Varun and Alharithi, Fahd S. and Álvarez, Roberto Marcelo and Singh, Aman and Qahtani, Abdulrahman M. mail UNSPECIFIED, UNSPECIFIED, roberto.alvarez@uneatlantico.es, aman.singh@uneatlantico.es, UNSPECIFIED (2022) NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange. Systems, 10 (6). p. 233. ISSN 2079-8954

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés The standard optimization of open-pit mine design and production scheduling, which is impacted by a variety of factors, is an essential part of mining activities. The metal uncertainty, which is connected to supply uncertainty, is a crucial component in optimization. To address uncertainties regarding the economic value of mining blocks and the general problem of mine design optimization, a minimum-cut network flow algorithm is employed to give the optimal ultimate pit limits and pushback designs under uncertainty. A structure that is computationally effective and can manage the joint presentation and treatment of the economic values of mining blocks under various circumstances is created by the push re-label minimum-cut technique. In this study, the algorithm is put to the test using a copper deposit and shows similarities to other stochastic optimizers for mine planning that have already been created. Higher possibilities of reaching predicted production targets are created by the algorithm’s earlier selection of more certain blocks with blocks of high value. Results show that, in comparison to a conventional approach using the same algorithm, the cumulative metal output is larger when the uncertainty in the metal content is taken into consideration. There is also an additional 10% gain in net present value. metadata Joshi, Devendra and Ali Albahar, Marwan and Chithaluru, Premkumar and Singh, Aman and Yadav, Arvind and Miró Vera, Yini Airet mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, yini.miro@uneatlantico.es (2022) A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization. Mathematics, 10 (24). p. 4803. ISSN 2227-7390

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing plays a vital role in managing ever increasing processing demands from diverse IoT-based applications. Smart healthcare systems work with the assistance of sensor-based devices and automatic data collection and processing can speed up overall system functionality. In the proposed work, a novel framework for smart health care is presented where a series of activities are performed with prime objective of reducing latency and execution time. Principal component analysis is used for feature reduction and support vector machines with radial basis function kernel is used for classification purpose. Workload optimization on the fog nodes is implemented using genetic algorithm. Data collection process also involves preprocessing as a leading step for generating cleaner data. Amalgamation of intelligent and optimization techniques in the presented framework certainly improves the efficiency of the overall system. Experimental results reveal that proposed work outperforms the existing fog-assisted smart healthcare systems in terms of latency, execution time, overall system accuracy, and system stability. metadata Abdellatif, Ahmed A. H. and Singh, Aman and Aldribi, Abdulaziz and Ortega-Mansilla, Arturo and Ibrahim, Muhammad and Rehman, Ateeq Ur mail UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, arturo.ortega@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization. Computational Intelligence and Neuroscience, 2022. pp. 1-12. ISSN 1687-5265

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory. metadata Pal, Rishi and Adhikari, Deepak and Heyat, Md Belal Bin and Guragai, Bishal and Lipari, Vivian and Brito Ballester, Julién and De la Torre Díez, Isabel and Abbas, Zia and Lai, Dakun mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vivian.lipari@uneatlantico.es, julien.brito@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2022) A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students’ Cardiac Signal and MSY. Bioengineering, 9 (12). p. 793. ISSN 2306-5354

Article Subjects > Biomedicine Ibero-american International University > Research > Articles and books Abierto Inglés Objective To compare the predictive value of the quick COVID-19 Severity Index (qCSI) and the National Early Warning Score (NEWS) for 90-day mortality amongst COVID-19 patients. Methods Multicenter retrospective cohort study conducted in adult patients transferred by ambulance to an emergency department (ED) with suspected COVID-19 infection subsequently confirmed by a SARS-CoV-2 test (polymerase chain reaction). We collected epidemiological data, clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and use of supplemental oxygen) and hospital variables. The primary outcome was cumulative all-cause mortality during a 90-day follow-up, with mortality assessment monitoring time points at 1, 2, 7, 14, 30 and 90 days from ED attendance. Comparison of performances for 90-day mortality between both scores was carried out by univariate analysis. Results From March to November 2020, we included 2,961 SARS-CoV-2 positive patients (median age 79 years, IQR 66–88), with 49.2% females. The qCSI score provided an AUC ranging from 0.769 (1-day mortality) to 0.749 (90-day mortality), whereas AUCs for NEWS ranging from 0.825 for 1-day mortality to 0.777 for 90-day mortality. At all-time points studied, differences between both scores were statistically significant (p < .001). Conclusion Patients with SARS-CoV-2 can rapidly develop bilateral pneumonias with multiorgan disease; in these cases, in which an evacuation by the EMS is required, reliable scores for an early identification of patients with risk of clinical deterioration are critical. The NEWS score provides not only better prognostic results than those offered by qCSI at all the analyzed time points, but it is also better suited for COVID-19 patients. metadata Martín-Rodríguez, Francisco and Sanz-García, Ancor and Ortega, Guillermo J. and Delgado-Benito, Juan F. and Garcia Villena, Eduardo and Mazas Pérez-Oleaga, Cristina and López-Izquierdo, Raúl and Castro Villamor, Miguel A. mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.garcia@uneatlantico.es, cristina.mazas@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19. Annals of Medicine, 54 (1). pp. 646-654. ISSN 0785-3890

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés This study involves a working limestone mine that supplies limestone to the cement factory. The two main goals of this paper are to (a) determine how long an operating mine can continue to provide the cement plant with the quality and quantity of materials it needs, and (b) explore the viability of combining some limestone from a nearby mine with the study mine limestone to meet the cement plant’s quality and quantity goals. These objectives are accomplished by figuring out the maximum net profit for the ultimate pit limit and production sequencing of the mining blocks. The issues were resolved using a branch-and-cut based sequential integer and mixed integer programming problem. The study mine can exclusively feed the cement plant for up to 15 years, according to the data. However, it was also noted that the addition of the limestone from the neighboring mine substantially increased the mine’s life (85 years). The findings also showed that, when compared with the production planning formulation that the company is now using, the proposed approach creates 10% more profit. The suggested method also aids in determining the desired desirable quality of the limestone that will be transported from the nearby mine throughout each production stage. metadata Joshi, Devendra and Chithaluru, Premkumar and Singh, Aman and Yadav, Arvind and Elkamchouchi, Dalia H. and Breñosa, Jose and Anand, Divya mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, josemanuel.brenosa@uneatlantico.es, divya.anand@uneatlantico.es (2022) An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value. Mathematics, 10 (21). p. 4140. ISSN 2227-7390

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés β-Thalassemia is one of the dangerous causes of the high mortality rate in the Mediterranean countries. Substantial resources are required to save a β-Thalassemia carriers’ life and early detection of thalassemia patients can help appropriate treatment to increase the carrier’s life expectancy. Being a genetic disease, it can not be prevented however the analysis of several indicators in parents’ blood can be used to detect disorders causing Thalassemia. Laboratory tests for Thalassemia are time-consuming and expensive like high-performance liquid chromatography, Complete Blood Count (CBC) with peripheral smear, genetic test, etc. Red blood indices from CBC can be used with machine learning models for the same task. Despite the available approaches for Thalassemia carriers from CBC data, gaps exist between the desired and achieved accuracy. Moreover, the data imbalance problem is studied well which makes the models less generalizable. This study proposes a highly accurate approach for β-Thalassemia detection using red blood indices from CBC augmented by supervised machine learning. In view of the fact that all the features do not carry predictive information regarding the target variable, this study employs a unified framework of two features selection techniques including Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD). The data imbalance between β-Thalassemia carrier and non-carriers is handled by Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN). Extensive experiments are performed using many state-of-the-art machine learning models and deep learning models. Experimental results indicate the superiority of the proposed approach over existing approaches with an accuracy score of 0.96. metadata Rustam, Furqan and Ashraf, Imran and Jabbar, Shehbaz and Tutusaus, Kilian and Mazas Pérez-Oleaga, Cristina and Pascual Barrera, Alina Eugenia and de la Torre Diez, Isabel mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, kilian.tutusaus@uneatlantico.es, cristina.mazas@uneatlantico.es, alina.pascual@unini.edu.mx, UNSPECIFIED (2022) Prediction β-Thalassemia carriers using complete blood count features. Scientific Reports, 12 (1). ISSN 2045-2322

Article Subjects > Biomedicine
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Cactus has been used in traditional folk medicine because of its role in treating a number of diseases and conditions. Prickly pear fruit is an excellent source of secondary metabolites (i.e., betalains, flavonoids, and ascorbic acid) with health-promoting properties against many common human diseases, including diabetes, hypertension, hypercholesterolemia, rheumatic pain, gastric mucosa diseases and asthma. In addition, prickly pears are potential candidates for the development of low-cost functional foods because they grow with low water requirements in arid regions of the world. This review describes the main bioactive compounds found in this fruit and shows the in vitro and some clinical studies about the fruit of most important cactus (Opuntia ficus-indica) and its relationship with some chronic diseases. Even though a lot of effort have been done to study the relationship between this fruit and the human health, more studies on Opuntia ficus-indica could help better understand its pharmacological mechanism of action to provide clear scientific evidence to explain its traditional uses, and to identify its therapeutic potential in other diseases. metadata Armas Diaz, Yasmany and Machì, Michele and Salinari, Alessia and Mazas Pérez-Oleaga, Cristina and Martínez López, Nohora Milena and Briones Urbano, Mercedes and Cianciosi, Danila mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, cristina.mazas@uneatlantico.es, nohora.martinez@uneatlantico.es, mercedes.briones@uneatlantico.es, UNSPECIFIED (2022) Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet. Mediterranean Journal of Nutrition and Metabolism, 15 (4). pp. 581-592. ISSN 1973798X

Article Subjects > Teaching Ibero-american International University > Research > Articles and books Abierto Inglés The objective of this article is to do bibliographic research lined up with the worldwide COVID-19 pandemic in which the main focus will be inclusion, Pygmalion effect as well as teaching and learning remotely. The basis is to gather different perspectives to build knowledge through exhaustive reading of books, doctoral dissertations, masters programs dissertations, current news, reliable sources of information, scientific papers, and own experience amongst others. The analysis of this bibliographic research will be useful for future research on topics related. Emotional and affective relations are important and have impact when facing English learning difficulties and when working to have inclusion. In the context of Covid-19, the learning and teaching conditions got complex when migrating to virtual classes. Remote teaching is not a solution to face learning and teaching difficulties. In this way, affection is the best tool for inclusive learning in virtual environments. It is said that one of the biggest barriers is teacher’s formation whose career is based on traditional pedagogy not easily adaptable to digital environments. This situation creates a deep feeling of frustration in teachers, which can be reflected in the low academic performance of English students. Several reflections will be made to encourage the actors involved in the teaching-learning process and in the educational system to ask themselves: Which are the challenges of remote teaching and learning during the Covid-19 pandemic being inclusive through the Pygmalion effect? metadata Rizo Peñafort, Andrea María and Pereira, Vilmar Alves mail UNSPECIFIED, vilmar.alves@unini.edu.mx (2022) Remote English Teaching during Covid-19 Pandemic: Challenges and Lessons in Higher Education Teaching in Colombia. Research, Society and Development, 11 (2). e44111226000. ISSN 2525-3409

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés This is a research study focused on searching didactic motivating strategies for supporting the EFL learning and teaching to Spanish speaking students from the Dominican Republic. The main objectives are to identify the influence of motivation in the learning of the English language and also to find new strategies that can be helpful for students to fulfill their wish of learning English fluently. metadata Méndez, Natividad mail maestranatividadm@outlook.es (2022) Research On Didactic Motivating Strategies for EFL Students from Dominican Republic. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés This project purposes to demonstrate that using active learning methodologies and having the students involved in doing things and thinking about the things they are doing in an effective way, it will foster understanding, autonomy, greater involvement and control over their learning and giving them skills to foster lifelong learning in the future. On the other hand, some difficulties which are commonly confronted by teachers and students in speaking activities and the proposed strategies along communicative activities will be discussed further in this paper. The project also pretends to show that using active learning activities, students experience learning by engaging in authentic learning activities, that is, ones that replicate situations or problems they might encounter in real life or in a work situation. metadata García Morales, Sara Eunise mail eunigarcimorales@gmail.com (2022) A Research about the Development of the Speaking Skills in Advanced EFL Students with Active Learning Methodologies. Masters thesis, UNSPECIFIED.

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction. metadata Anand, Divya and Singh, Aman and Alsubhi, Khalid and Goyal, Nitin and Abdrabou, Atef and Vidyarthi, Ankit and Rodrigues, Joel J. P. C. mail divya.anand@uneatlantico.es, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2022) A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction. IEEE Transactions on Intelligent Transportation Systems. pp. 1-9. ISSN 1524-9050

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly metadata Peter, Geno and Stonier, Albert Alexander and Gupta, Punit and Gavilanes, Daniel and Masías Vergara, Manuel and Lung sin, Jong mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, manuel.masias@uneatlantico.es, UNSPECIFIED (2022) Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT. Energies, 15 (21). p. 8206. ISSN 1996-1073

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés AbstractThere has been major growth in the business industry. Many foreign companies have settled subsidiaries in this country and require the use of English as Lingua Franca in order to get hired, promoted, or sent to training abroad.Additionally, universities have also changed their requirements to graduate, so now all students must obtain an acceptable score in an international English exam to be able to obtain a Bachelor’s or a Master’s degree.The factors mentioned above along with pressure at work or school, family responsibilities, and tight schedules among others, help increase the level of anxiety which eventually affects their performance and outcome. To find how this project will use adult English students in the 25 - 50 age range who are currently working in companies on a regular nine to six schedule. They are professionals who are studying to obtain a specialization or their master’s or doctorate’s degree.All of these students have studied English before in a school, language institute, or with a private teacher once or several times before. All of them need the language to grow professionally and academically and are very motivated and eager to learn. Most of them expect to finally learn the language with their current course. They do not want to drop the course one more time and promise to do all that it takes to pursue their goal.The methodology of this research will be descriptive using quantitative approaches.We will conduct observation, diaries, videos, interviews, and tests to define anxious students' characteristics. A survey research design will be necessary due to the nature of this action research project as well as content analysis. metadata Andrade Romaña, Lord Leidy mail lordleidy13@gmail.com (2022) A Study for Anxiety and Effects on Performance of Second-Year Adult English Language Learners of Georgetown University. Masters thesis, UNSPECIFIED.

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation metadata Yadav, Arvind and Chithaluru, Premkumar and Singh, Aman and Albahar, Marwan Ali and Jurcut, Anca and Álvarez, Roberto Marcelo and Mojjada, Ramesh Kumar and Joshi, Devendra mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, roberto.alvarez@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models. Mathematics, 10 (22). p. 4263. ISSN 2227-7390

Article Subjects > Biomedicine Ibero-american International University > Research > Articles and books Abierto Inglés COVID-19 made its debut as a pandemic in 2020; since then, more than 607 million cases and at least 6.5 million deaths have been reported worldwide. While the burden of disease has been described, the long-term effects or chronic sequelae are still being clarified. The aim of this study was to present an overview of the information available on the sequelae of COVID-19 in people who have suffered from the infection. A systematic review was carried out in which cohort studies, case series, and clinical case reports were included, and the PubMed, Scielo, SCOPUS, and Web of Science databases were extracted. Information was published from 2020 to 1 June 2022, and we included 26 manuscripts: 9 for pulmonary, 6 for cardiac, 2 for renal, 8 for neurological and psychiatric, and 6 for cutaneous sequelae. Studies showed that the most common sequelae were those linked to the lungs, followed by skin, cutaneous, and psychiatric alterations. Women reported a higher incidence of the sequelae, as well as those with comorbidities and more severe COVID-19 history. The COVID-19 pandemic has not only caused death and disease since its appearance, but it has also sickened millions of people around the globe who potentially suffer from serious illnesses that will continue to add to the list of health problems, and further burden healthcare systems around the world. metadata Vásconez-González, Jorge and Izquierdo Condoy, Juan Sebastian and Fernandez-Naranjo, Raul and Ortiz-Prado, Esteban mail UNSPECIFIED (2022) A Systematic Review and Quality Evaluation of Studies on Long-Term Sequelae of COVID-19. Healthcare, 10 (12). p. 2364. ISSN 2227-9032

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés It is a 6 week created plan to integrate more English Language learning in the Science CLIL classroom through task-based, communicative, constructivist tasks in a hybrid learning environment. metadata Zuidervaart, Cynthia Corinne mail cynthiazuidervaart3@hotmail.com (2022) Task-based Hybrid Learning in a B1 Science CLIL classroom in Ecuador. Masters thesis, UNSPECIFIED.

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Currently, sustainability is a vital aspect for every nation and organization to accomplish Sustainable Development Goals (SDGs) by 2030. Environmental, social, and governance (ESG) metrics are used to evaluate the sustainability level of an organization. According to the statistics, 53% of respondents in the BlackRock survey are concerned about the availability of low ESG data, which is critical for determining the organization’s sustainability level. This obstacle can be overcome by implementing Industry 4.0 technologies, which enable real-time data, data authentication, prediction, transparency, authentication, and structured data. Based on the review of previous studies, it was determined that only a few studies discussed the implementation of Industry 4.0 technologies for ESG data and evaluation. The objective of the study is to discuss the significance of ESG data and report, which is used for the evaluation of the sustainability of an organization. In this regard, the assimilation of Industry 4.0 technologies (Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data for obtaining ESG data by an organization is detailed presented to study the progress of advancement of these technologies for ESG. On the basis of analysis, this study concludes that consumers are concerned about the ESG data, as most organizations develop inaccurate ESG data and suggest that these digital technologies have a crucial role in framing an accurate ESG report. After analysis a few vital conclusions are drawn such as ESG investment has benefited from AI capabilities, which previously relied on self-disclosed, annualized company information that was susceptible to inherent data issues and biases. Finally, the article discusses the vital recommendations that can be implemented for future work metadata Saxena, Archana and Singh, Rajesh and Gehlot, Anita and Akram, Shaik Vaseem and Twala, Bhekisipho and Singh, Aman and Caro Montero, Elisabeth and Priyadarshi, Neeraj mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, elizabeth.caro@uneatlantico.es, UNSPECIFIED (2022) Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape. Sustainability, 15 (1). p. 309. ISSN 2071-1050

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study. metadata Mehmood, Aneela and Farooq, Muhammad Shoaib and Naseem, Ansar and Rustam, Furqan and Gracia Villar, Mónica and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) Threatening URDU Language Detection from Tweets Using Machine Learning. Applied Sciences, 12 (20). p. 10342. ISSN 2076-3417

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach. metadata Chaganti, Rajasekhar and Rustam, Furqan and De La Torre Díez, Isabel and Vidal Mazón, Juan Luis and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques. Cancers, 14 (16). p. 3914. ISSN 2072-6694

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés Device-to-device (D2D) communication has attracted many researchers, cellular operators, and equipment makers as mobile traffic and bandwidth demands have increased. It supports direct communication within devices with no need for any intermediate node and, therefore, offers advantage in 5G network while providing wide cell coverage range and frequency reuse. However, establishing acceptable and secure mechanism for D2D communication which ensures confidentiality, integrity, and availability is an issue encountered in this situation. Furthermore, in a resource-constrained IoT environment, these security challenges are more critical and difficult to mitigate, especially during emergence of IoT with 5G network application scenarios. To address these issues, this paper proposed a security mechanism in 5G network for D2D wireless communication dependent on lightweight modified elliptic curve cryptography (LMECC). The proposed scheme follows a proactive routing protocol to discover services, managing link setup, and for data transfer with the aim to reduce communication overhead during user authentication. The proposed approach has been compared against Diffie–Hellman (DH) and ElGamal (ELG) schemes to evaluate the protocol overhead and security enhancement at network edge. Results proved the outstanding performance of the proposed LMECC for strengthening data secrecy with approximate 13% and 22.5% lower overhead than DH and ELG schemes. metadata Gupta, Divya and Rani, Shalli and Singh, Aman and Vidal Mazón, Juan Luis and Wang, Han mail UNSPECIFIED, UNSPECIFIED, aman.singh@unic.co.ao, juanluis.vidal@uneatlantico.es, UNSPECIFIED (2022) Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach. Wireless Communications and Mobile Computing, 2022. pp. 1-9. ISSN 1530-8669

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Este estudio se realizó con el propósito de analizar las percepciones de profesores y alumnos sobre la aplicación de estrategias según el enfoque comunicativo y Task-Based, teniendo en cuenta tres aspectos principales. Primero, las estrategias de evaluación que utilizan los profesores de inglés según el TBL en un concepto motivacional. En segundo lugar, el conocimiento que tienen los profesores de inglés sobre este método. Por último, las limitaciones del TBL a partir de las percepciones de profesores y alumnos. metadata Fernandez Aguero, Rosa Irene mail irenfer13@yahoo.com (2022) Una Investigación Acción para Fomentar la Motivación Basada en Estrategias Comunicativas y Task-Based para mejorar el nivel de Inglés como Lengua Extranjera A2 de los alumnos de décimo grado del Colegio Técnico de Puriscal. Masters thesis, UNSPECIFIED.

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés The development of underwater wireless sensor networks (UWSNs) has attracted great interest from many researchers and scientists to detect and monitor unfamiliar underwater domains. To achieve this goal, collecting data with an underwater network of sensors is primordial. Moreover, real-time information transmission needs to be achieved through efficient and enabling technologies for node deployment and data collection in UWSN. The Internet of Things (IoT) helps in real time data transmission, and it has great potential in UWSN, i.e., the Internet of Underwater Things (IoUT). The Internet of Underwater Things (IoUT) is a modern communication ecosystem for undersea things in marine and underwater environments. Intelligent boats and ships, automatic maritime transportation, location and navigation, undersea discovery, catastrophe forecasting and avoidance, as well as intelligent monitoring and security are all intertwined with IoUT technology. In this paper, the enabling technologies of UWSN along with several fundamental key aspects are scrupulously explained. The study aims to inquire about node deployment and data collection strategies, and then encourages researchers to lay the groundwork for new node deployment and advanced data collection techniques that enable effective underwater communication techniques. Besides different types of communication media, applications of UWSNs are also part of this paper. Various existing data collection protocols based on the deployment models are simulated using Network Simulator (NS 2.30) to analyse and compare the performance of state-of-the-art techniques. metadata Chaudhary, Monika and Goyal, Nitin and Benslimane, Abderrahim and Awasthi, Lalit Kumar and Alwadain, Ayed and Singh, Aman mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es (2022) Underwater Wireless Sensor Networks: Enabling Technologies for Node Deployment and Data Collection Challenges. IEEE Internet of Things Journal. p. 1. ISSN 2372-2541

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés At this time, efforts are being made on a worldwide scale to accomplish sustainable development objectives. It has, thus, now become essential to investigate the part of technology in the accomplishment of these Sustainable Development Goals (SDGs), as this will enable us to circumvent any potential conflicts that may arise. The importance of wastewater management in the accomplishment of these goals has been highlighted in the study. The research focuses on the role of fourth industrial revolution in meeting the Sustainable Goals for 2030. Given that water is the most important resource on the planet and since 11 of the 17 Sustainable Goals are directly related to having access to clean water, effective water management is the most fundamental need for achieving these goals. The age of Industry 4.0 has ushered in a variety of new solutions in many industrial sectors, including manufacturing, water, energy, healthcare, and electronics. This paper examines the present creative solutions in water treatment from an Industry-4.0 viewpoint, focusing on big data, the Internet of Things, artificial intelligence, and several other technologies. The study has correlated the various concepts of Industry 4.0 along with water and wastewater management and also discusses the prior work carried out in this field with help of different technologies. In addition to proposing a way for explaining the operation of I4.0 in water treatment through a systematic diagram, the paper makes suggestions for further research as well. metadata Pandey, Shivam and Twala, Bhekisipho and Singh, Rajesh and Gehlot, Anita and Singh, Aman and Caro Montero, Elisabeth and Priyadarshi, Neeraj mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, elizabeth.caro@uneatlantico.es, UNSPECIFIED (2022) Wastewater Treatment with Technical Intervention Inclination towards Smart Cities. Sustainability, 14 (18). p. 11563. ISSN 2071-1050

Article Subjects > Biomedicine
Subjects > Social Sciences
Ibero-american International University > Research > Articles and books Abierto Inglés Background Despite worldwide progress in terms of clean water supply, sanitation, and hygiene knowledge, some middle and most of low-income countries are still experiencing many diseases transmitted using unsafe water and the lack of sanitation. Methods To understand the impact of all waterborne diseases (WBD) registered in Ecuador. We performed a population-based analysis of all cases and deaths due to WBD in Ecuador based on the national public databases of hospital discharges as a proxy of incidence, in-hospital mortality, and countrywide general mortality rates from 2011 to 2020. Results In Ecuador, mestizos (mixed European and Indigenous American ancestry) had the greatest morbidity rate (141/100,000), followed by indigenous (63/100,000) and self-determined white patients (21/100,000). However, in terms of mortality, indigenous population have the greatest risk and rates, having a 790% additional mortality rate (2.6/100,000) than the reference group (self-determined white populations) at 0.29/100,000. The burden of disease analysis demonstrated that indigenous had the highest burden of disease caused by WBD with 964 YLL per every 100,000 people while mestizos have 360 YYL per 100,000 and self-determined white Ecuadorians have 109 YYL per 100,000. Conclusions In Ecuador, waterborne diseases (WBD) are still a major public health problem. We found that indigenous population had higher probability of getting sick and die due to WBD than the rest of the ethnic groups in Ecuador. We also found that younger children and the elderly are more likely to be admitted to the hospital due to a WBD. These epidemiological trends are probably associated with the lower life expectancy found among Indigenous than among the rest of the ethnic groups, who die at least, 39 years earlier than the self-determined white populations, 28 years earlier than Afro-Ecuadorians and 12 years earlier than the mestizos. metadata Ortiz-Prado, Esteban and Simbaña-Rivera, Katherine and Cevallos, Gabriel and Gómez-Barreno, Lenin and Cevallos, Domenica and Lister, Alex and Fernandez-Naranjo, Raul and Ríos-Touma, Blanca and Vásconez-González, Jorge and Izquierdo Condoy, Juan Sebastian mail UNSPECIFIED (2022) Waterborne diseases and ethnic-related disparities: A 10 years nationwide mortality and burden of disease analysis from Ecuador. Frontiers in Public Health, 10. ISSN 2296-2565

Article Subjects > Biomedicine
Subjects > Social Sciences
Ibero-american International University > Research > Articles and books Abierto Inglés Background Despite worldwide progress in terms of clean water supply, sanitation, and hygiene knowledge, some middle and most of low-income countries are still experiencing many diseases transmitted using unsafe water and the lack of sanitation. Methods To understand the impact of all waterborne diseases (WBD) registered in Ecuador, we performed an analysis of all cases and deaths related to WBD to compute incidence and mortality rates. Results We found that in Ecuador, mestizo people had the greatest morbidity rate (141/100,000) patient followed by indigenous (63/100,000) and self-determined white patients (21/100,000). However, in terms of mortality, indigenous population have a 790% increase in mortality rate (2.6 /100,000) when compared to self-determined white populations (0.29/100,000) or 176% more when compared to mestizos (0.94/100,000). This trend remains the same among children and the elderly who have higher mortality rates when compared to other ethnic groups. Conclusions In Ecuador, water borne diseases (WBD) are still a major public health problem. We found that younger children and elderly are more likely to be get sick and die due to water borne diseases. In terms of morbidity, mestizos reported the highest rate, while in terms of mortality, indigenous populations are the most affected, having the highest mortality among different ethnic groups. We hypostatize that reduced health care access is linked to fewer reporting incidence rates among indigenous populations but higher mortality rates. metadata Ortiz-Prado, Esteban and Simbaña-Rivera, Katherine and Cevallos-Sierra, Gabriel and Cevallos, Domenica and Lister, Alex and Fernandez-Naranjo, Raul and Ríos-Touma, Blanca and Vasconez, Jorge and Izquierdo Condoy, Juan Sebastian and Gomez-Barreno, Lenin mail UNSPECIFIED (2022) Waterborne diseases as an indicator of health disparities: A nationwide study of WaSH related morbidity and mortality in Ecuador from 2011-2020. Research square. (Unpublished)

Article Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity. metadata Rustam, Furqan and Aslam, Naila and De La Torre Díez, Isabel and Khan, Yaser Daanial and Vidal Mazón, Juan Luis and Rodríguez Velasco, Carmen Lilí and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, UNSPECIFIED (2022) White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. Healthcare, 10 (11). p. 2230. ISSN 2227-9032

Thesis Subjects > Teaching Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés In the present research it is analyzed the relation between the errors in writing production and the learners’ strategies to learn a language identifying the anxiety levels. Three different tests are applied along one semester to determine the students’ competence and the difficulties they may have when writing about a topic. Thus, the grade project is divided in three parts. Classes are designed to overcome difficulties in writing considering the language learning strategies increasing autonomy, motivation and decreasing anxiety levels. Students are trained in learning strategies implicitly and explicitly to become more aware of strengthens and weaknesses in writing.At the end of each exam, students apply a survey to determine what strategies they use, and which ones are more important to overcome difficulties. In the first part, students are trained implicitly in the learning strategies along with the class activities. In the second part, students are trained in explicit learning strategies so they can observe and analyze their considerations. In the third part, students are trained again in learning strategies implicitly.Before each exam, students complete pre-workshops focused on the topics studied. In this activity, behavior is observed so strategies are identified and analyzed along with the results of the SILL tests. After each pre-workshop, students complete a survey focused on testing anxiety and stress levels that may be present in the tests. metadata Zamora Valencia, Diego Fernando mail diegof_zava@hotmail.com (2022) An improvement of the written production by decreasing anxiety. Masters thesis, UNSPECIFIED.

Article Subjects > Teaching Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés, Español, Portugués Este estudo tem por objetivo apresentar reflexões a partir da experiência de estudantes em nível de doutorado sobre a modalidade da Internacionalização em Casa no contexto da Pós-graduação, através da Educação a Distância. Pesquisa de horizonte qualitativo com abordagem da Hermenêutica Filosófica, num primeiro momento, apresenta demarcações conceituais sobre a necessidade da internacionalização, suas formas e desafios no contexto da região da América Latina e Caribe. Num segundo, apresenta resultados de uma experiência com estudantes que vivenciam esta modalidade no Chile, Colômbia e Brasil. Os resultados expressam as motivações, avaliações, aprendizagens e desafios em cursar um doutorado nessa modalidade. A internacionalização em casa na Pós-graduação propicia a emergência de uma nova relação entre uma instituição internacional diretamente com o estudante. Para os estudantes, a satisfação está na realização de um curso que em outros moldes não seria possível sem perder os vínculos pessoais e profissionais. O maior desafio passa pela disciplina e gestão de espaços e tempos de estudo. metadata Pereira, Vilmar Alves mail vilmar.alves@unini.edu.mx (2022) A internacionalização em casa na pós-graduação na América Latina e Caribe na modalidade a distância. Revista Ibero-Americana de Estudos em Educação. pp. 2476-2493. ISSN 2446-8606

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés Teaching pronunciation is a very difficult task within the public schooling system in Ecuador, because Pronunciation alone has not been taken into consideration in many ways. Learners are memorizing words and phrases and teachers do not feel comfortable teaching pronunciation because they have no preparation in the field.We need to change the teaching-learning process in order to provide better opportunities to our students. metadata Rosales Villalva, Karem Victoria mail kvr9918@hotmail.com (2022) A research on Teaching Pronunciation and Evaluation in the Public Education System in Ecuador. Masters thesis, Universidad Internacional Iberoamericana México.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects Cerrado Inglés Thesis to qualify for:Master in Teaching English as a Foreign Language metadata Habitzreuter, Alice Cecilia mail alicehabit@hotmail.com (2022) A research on the benefits of learning english as a second language at an early age. Masters thesis, UNSPECIFIED.

Thesis Subjects > Teaching Ibero-american International University > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado Inglés The problematic generated by the overuse of Spanish in the English as a Foreign Language classroom focused on English as a Foreign Language students at superior sublevel from public schools in Ecuador is reviewed in this research. This work was developed by providing writing and speaking assignments to selected participants as well as by conducting surveys to students and to all English teachers from this sublevel. The present investigation process was done in order to find the causes of the overuse of Spanish by means of a correlation between a bibliographic and camp work. Thus, a better English fluency is intended to be reached by avoiding practices such as using Spanish or thinking in Spanish first instead of using English within the learning context. metadata Núñez Pesantez, Milton Armando mail armando-tom@hotmail.com (2022) A research on the overuse of Spanish in the EFL classroom focused on EFL students at superior sublevel From Humberto Vacas Gómez school in Ecuador. Masters thesis, UNSPECIFIED.

2021

Article Subjects > Psychology Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Many earlier studies conducted on sports betting and addiction have examined sports betting in the context of gambling and have not taken into account the specific motivations of sports betting. Therefore, the effects of motivational elements of sports betting on sports betting addiction risk are unknown. The aim of the present study was to examine the effects of motivation factors specific to sports betting on sports betting addiction. Accordingly, three linked studies were conducted. Firstly, to determine sports betting motivations “Sports Betting Motivation Scale (SBMS)” developed and validated. Secondly, to determine the risks of sports betting addiction “Problem Sports Betting Severity Index (PSBSI)” was adapted from Problem Gambling Severity Index (PGSI). Finally, the third study examined effects of the sports betting motivations on sports betting addiction risk. Study one (n=281), study two comprised (n=230), and the final study comprised (n=643) sports fans who bet on sports regularly for 12 months with different motivations. The findings demonstrate that the SBMS appears to be a reliable and valid instrument for assessing sports betting motivations. Also, the findings provided PSBSI validity for the use of the Turkish and sports betting adapted version of PGSI. As a result of the main research, “make money,” “socialization,” and “being in the game” motivations were found to be positive predictors of sports betting addiction risk, while “fun” motivation was a negative predictor. The motivations “recreation/escape,” “knowledge of the game,” and “interest in sport” were found not to be significant predictors of the risk of sports betting addiction. metadata Gökce Yüce, Sevda and Yüce, Arif and Katırcı, Hakan and Nogueira-López, Abel and González-Hernández, Juan mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, abel.nogueira@uneatlantico.es, UNSPECIFIED (2021) Effects of Sports Betting Motivations on Sports Betting Addiction in a Turkish Sample. International Journal of Mental Health and Addiction. ISSN 1557-1874

Article Subjects > Teaching Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto Inglés The purpose of this article was to evaluate the level of satisfaction of a sample of graduates in relation to different online postgraduate programs in the environmental area, as part of the process of continuous improvement in which the educational institution was immersed for the renewal of its accreditation before the corresponding official bodies. Based on the bibliographic review of a series of models and tools, a Likert scale measurement instrument was developed. This instrument, once applied and validated, showed a good level of reliability, with more than three quarters of the participants having a positive evaluation of satisfaction. Likewise, to facilitate the relational study, and after confirming the suitability of performing a factor analysis, four variable grouping factors were determined, which explained a good part of the variability of the instrument’s items. As a result of the analysis, it was found that there were significant values of low satisfaction in graduates from the Eurasian area, mainly in terms of organizational issues and academic expectations. On the other hand, it was observed that the methodological aspects of the “Auditing” and “Biodiversity” programs showed higher levels of dissatisfaction than the rest, with no statistically significant relationships between gender, entry profile or age groups. The methodology followed and the rigor in determining the validity and reliability of the instrument, as well as the subsequent analysis of the results, endorsed by the review of the documented information, suggest that the instrument can be applied to other multidisciplinary programs for decision making with guarantees in the educational field metadata García Villena, Eduardo and Pueyo Villa, Silvia and Delgado Noya, Irene and Tutusaus, Kilian and Ruiz Salces, Roberto and Pascual Barrera, Alina Eugenia mail eduardo.garcia@uneatlantico.es, silvia.pueyo@uneatlantico.es, irene.delgado@uneatlantico.es, kilian.tutusaus@uneatlantico.es, roberto.ruiz@uneatlantico.es, alina.pascual@unini.edu.mx (2021) Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field. Sustainability, 13 (9). p. 5112. ISSN 2071-1050

Revista Subjects > Comunication Europe University of Atlantic > Research > Scientific Magazines
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Universidad Internacional do Cuanza > Research > Scientific Magazines
Abierto Inglés El objetivo principal de Revista MLS Communication Journal es difundir obras inéditas relacionadas con los grandes retos y desafíos de la comunicación en sus diferentes ámbitos: el periodismo, la publicidad, la comunicación audiovisual, la comunicación interactiva o la comunicación en las organizaciones, entre otros. La revista tiene interés en la difusión de trabajos académicos y científicos que identifiquen, describan y divulguen hallazgos inéditos y de interés en estos campos desde la revisión teórica, la innovación metodológica, la experimentación y la apuesta por la innovación. Los estudios publicados en MLS Communication Journal se centran en reflexionar sobre los grandes hitos, las principales interrogantes y las tendencias más destacadas del escenario comunicativo, adoptando una perspectiva de estudio teórico-práctica. La revista tiene un marcado carácter iberoamericano e internacional, por lo que puede ser utilizada para su publicación en cualquier país de origen, siempre que éstos cumplan con las diferentes fases de la investigación con rigor metodológico. Constituye, por lo tanto, un medio de difusión del conocimiento derivado de diferentes entornos socioculturales. MLS Communication Journal pública trabajos en el idioma castellano, portugués e inglés, y se edita totalmente en el último idioma, manteniendo también una edición en el idioma original del manuscrito. Su estructura organizativa se compone principalmente de investigadores, ya que una revista científica, basada en principios, debe tener sus raíces en la comunidad investigadora que tiene la producción intelectual y las contribuciones relevantes en el tema dentro de sus respectivas instituciones. metadata UNSPECIFIED mail mls@devnull.funiber.org (2021) MLS Communication Journal. [Revista]

Revista Subjects > Physical Education and Sport Europe University of Atlantic > Research > Scientific Magazines
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Universidad Internacional do Cuanza > Research > Scientific Magazines
Abierto Inglés MLS Sport Research es una revista científica que tiene como objetivo publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución al progreso en el ámbito de las Ciencias de la Actividad Física y del Deporte. Los estudios publicados deben cumplir con las diferentes fases de la investigación con rigor metodológico. MLS Sport Research atenderá a diferentes ámbitos dentro de la actividad física y el deporte: salud, educación física, prevención y readaptación de lesiones, socorrismo, nuevas tecnologías, fisiología, nutrición, psicología, dirección y gestión, entrenamiento y rendimiento deportivo. metadata UNSPECIFIED mail mls@devnull.funiber.org (2021) MLS Sport Research. [Revista]

2020

Article Subjects > Psychology Ibero-american International University > Research > Articles and books Abierto Inglés, Español La trata humana, es un fenómeno que crece en las entrañas de muchos países del mundo. Puerto Rico, no es la excepción a esta situación. En la investigación que se presenta en este artículo, se analizan las repercusiones que ha tenido la ausencia de protocolos de trata humana para menores de edad, en la lucha contra este fenómeno en Puerto Rico. Para lograr ese propósito, se realizó una investigación de enfoque mixto y diseño exploratorio. Respecto a las hipótesis del estudio, a través de estas, se intentó probar que las efectos negativos que acarrea la ausencia de protocolos, en la lucha contra este fenómeno, disminuirían con la presencia de protocolos de trata humana para menores en Puerto Rico. También, se auscultó si la identificación e inclusión de los factores correctos, en un protocolo para menores, podría mejorar las estrategias de detección de casos. Se utilizó la percepción y conocimiento de expertos que ofrecen servicios a la población de menores, en algunas agencias públicas de Puerto Rico y en algunas organizaciones no gubernamentales (ONG). La muestra seleccionada, fue no aleatoria y por disponibilidad. La técnica utilizada para obtener la información, fue la entrevista por medio de un cuestionario. El cuestionario, se redactó utilizando una escala Likert, además, se realizaron preguntas abiertas. Se cumplió con los objetivos principales de crear un prototipo de plan de prevención juvenil de trata huma e identificar los factores que debe incluir un protocolo de prevención y protección de la trata para menores en Puerto Rico. metadata I Alvarez, Nydia mail UNSPECIFIED (2020) Ausencia de protocolos de prevención de trata humana para menores de edad en Puerto Rico. MLS Psychology Research, 3 (1). pp. 65-78. ISSN 26055295

Article Subjects > Nutrition Ibero-american International University > Research > Articles and books Abierto Inglés University students frequently develop unhealthy eating habits. However, it is unknown if students enrolled in academic programs related to nutrition and culinary arts have healthier eating habits. We evaluated the relationship of eating habits and nutritional status of students in academic programs with knowledge on nutrition, as well as cooking methods and techniques. A descriptive cross-sectional study was conducted in spring of 2019, while we completed a survey measuring eating habits and knowledge on nutrition, as well as cooking methods and techniques. Anthropometric measurements were collected for nutritional status estimation. The non-probabilistic convenience sample comprised 93 students pursuing degrees at Universidad Ana G. Mendez, Puerto Rico. Inadequate body mass index (BMI) was observed in 59% of the students. Eating habits, knowledge on nutrition, and knowledge on cooking methods and techniques were inadequate in 86%, 68%, and 41% of the population, respectively. Eating habits were associated with knowledge on nutrition and academic program, but not with knowledge on cooking methods and techniques. Most students reported having inadequate eating habits and BMI. Nutrition and dietetics students had the best knowledge on nutrition compared to culinary management students, a majority of whom had inadequate knowledge. We can conclude that there are other factors inherent to students’ life that may have a stronger influence on eating habits metadata Rivera Medina, Christian and Briones Urbano, Mercedes and de Jesús Espinosa, Aixa and Toledo López, Ángel mail UNSPECIFIED, mercedes.briones@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2020) Eating Habits Associated with Nutrition-Related Knowledge among University Students Enrolled in Academic Programs Related to Nutrition and Culinary Arts in Puerto Rico. Nutrients, 12 (5). p. 1408. ISSN 2072-6643

2018

Revista Subjects > Psychology Europe University of Atlantic > Research > Scientific Magazines
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Universidad Internacional do Cuanza > Research > Scientific Magazines
Abierto Inglés MLS Psychology Research es una revista científica que tiene como finalidad publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución al progreso de cualquier ámbito de la psicología científica como objetivo principal. MLSPR acogerá a artículo que analicen la conducta y procesos mentales tanto de individuos como de grupos, y que abarque aspectos de la experiencia humana. MLSPR atenderá a diferentes enfoques dentro de la psicología: Psicología clínica, Psicoterapea, Psicología educativa, Psicología del desarrollo, Neuropsicología, Psicología social, etc. metadata UNSPECIFIED mail mls@devnull.funiber.org (2018) MLS Psychology Research. [Revista]

2017

Revista Subjects > Teaching Europe University of Atlantic > Research > Scientific Magazines
Fundación Universitaria Internacional de Colombia > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Ibero-american International University > Research > Scientific Magazines
Universidad Internacional do Cuanza > Research > Scientific Magazines
Abierto Inglés La revista MLS Educational Research nace como una publicación semestral con el objetivo de contribuir al debate y mejorar la comprensión de la práctica educativa, la innovación pedagógica y la investigación en general. Los artículos incluidos en esta revista se publican en español, portugués e inglés. La vocación internacional de esta revista lo hace apto para difundir el conocimiento de los diferentes ambientes socioculturales. metadata UNSPECIFIED mail mls@devnull.funiber.org (2017) MLS Educational Research. [Revista]

Other Subjects > Teaching Europe University of Atlantic > Research > Projects I+D+I
Fundación Universitaria Internacional de Colombia > Research > Projects I+D+I
Ibero-american International University > Research > Projects I+D+I
Ibero-american International University > Research > Projects I+D+I
Universidad Internacional do Cuanza > Research > Projects I+D+I
Cerrado Inglés El e-learning como modalidad de enseñanza-aprendizaje introduce especificidades en cuanto a las funciones y competencias docentes: nuevos entornos de aprendizaje suponen nuevos enfoques para entenderlos, diseñarlos y gestionarlos. La empresa MLSJOURNALS pretende desarrollar una nueva línea de servicios para Universidades dentro del campo de las competencias docentes para la cual requiere de un profesional del campo de la psicología y la docencia. La presente actividad de I+D aporta a la empresa un conocimiento sistematizado y basado en la evidencia, para describir el perfil de competencias docentes para la formación universitaria en entornos virtuales de aprendizaje. Con ello, la empresa pretende aportar un nuevo servicio que dé respuesta a esta necesidad en el mercado universitario, enfocándose a dos objetivos principales: 1. Describir el conjunto de competencias - que integran conocimientos, habilidades y actitudes- que deben reunir los profesores universitarios para la docencia a través de Entornos Virtuales de Aprendizaje. 2. Descubrir la relación existente entre el perfil competencial de los profesores y los resultados logrados en el proceso de enseñanza – aprendizaje. metadata UNSPECIFIED mail UNSPECIFIED (2017) VIRTUALAP: Competencias docentes para la formación universitaria en un entorno virtual de aprendizaje. Repositorio de la Universidad.

2016

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Abierto Inglés It has been hypothesized that alterations in the composition of the gut microbiota might be associated with the onset of certain human pathologies, such as Alzheimer disease, a neurodegenerative syndrome associated with cerebral accumulation of amyloid-β fibrils. It has been shown that bacteria populating the gut microbiota can release significant amounts of amyloids and lipopolysaccharides, which might play a role in the modulation of signaling pathways and the production of proinflammatory cytokines related to the pathogenesis of Alzheimer disease. Additionally, nutrients have been shown to affect the composition of the gut microbiota as well as the formation and aggregation of cerebral amyloid-β. This suggests that modulating the gut microbiome and amyloidogenesis through specific nutritional interventions might prove to be an effective strategy to prevent or reduce the risk of Alzheimer disease. This review examines the possible role of the gut in the dissemination of amyloids, the role of the gut microbiota in the regulation of the gut–brain axis, the potential amyloidogenic properties of gut bacteria, and the possible impact of nutrients on modulation of microbiota composition and amyloid formation in relation to the pathogenesis of Alzheimer disease. metadata Pistollato, Francesca and Sumalla Cano, Sandra and Elío Pascual, Iñaki and Masias Vergara, Manuel and Giampieri, Francesca and Battino, Maurizio mail francesca.pistollato@uneatlantico.es, sandra.sumalla@uneatlantico.es, inaki.elio@uneatlantico.es, manuel.masias@uneatlantico.es, francesca.giampieri@uneatlantico.es, maurizio.battino@uneatlantico.es (2016) Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease. Nutrition Reviews, 74 (10). pp. 624-634. ISSN 0029-6643

2015

Article Subjects > Biomedicine
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Cerrado Inglés In the last decade, specific dietary patterns, mainly characterized by high consumption of vegetables and fruits, have been proven beneficial for the prevention of both metabolic syndrome (MetS)-related dysfunctions and neurodegenerative disorders, such as Alzheimer’s disease (AD). Nowadays, neuroimaging readouts can be used to diagnose AD, investigate MetS effects on brain functionality and anatomy, and assess the effects of dietary supplementations and nutritional patterns in relation to neurodegeneration and AD-related features. Here we review scientific literature describing the use of the most recent neuroimaging techniques to detect AD- and MetS-related brain features, and also to investigate associations between consolidated dietary patterns or nutritional interventions and AD, specifically focusing on observational and intervention studies in humans. metadata Pistollato, Francesca and Sumalla Cano, Sandra and Elío Pascual, Iñaki and Masías Vergara, Manuel and Giampieri, Francesca and Battino, Maurizio mail francesca.pistollato@uneatlantico.es, sandra.sumalla@uneatlantico.es, inaki.elio@uneatlantico.es, manuel.masias@uneatlantico.es, francesca.giampieri@uneatlantico.es, maurizio.battino@uneatlantico.es (2015) The Use of Neuroimaging to Assess Associations Among Diet, Nutrients, Metabolic Syndrome, and Alzheimer’s Disease. Journal of Alzheimer's Disease, 48 (2). pp. 303-318. ISSN 13872877

This list was generated on Wed Jun 19 23:47:41 2024 UTC.

<a class="ep_document_link" href="/10290/1/Influence%20of%20E-learning%20training%20on%20the%20acquisition%20of%20competences%20in%20basketball%20coaches%20in%20Cantabria.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria

The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria.

Producción Científica

Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,

Alemany Iturriaga

<a href="/12750/1/s41598-024-63831-0.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Efficient deep learning-based approach for malaria detection using red blood cell smears

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.

Producción Científica

Muhammad Mujahid mail , Furqan Rustam mail , Rahman Shafique mail , Elizabeth Caro Montero mail elizabeth.caro@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,

Mujahid

<a href="/12751/1/s12874-024-02249-8.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

Producción Científica

Tumpa Rani Shaha mail , Momotaz Begum mail , Jia Uddin mail , Vanessa Yélamos Torres mail vanessa.yelamos@funiber.org, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Imran Ashraf mail , Md. Abdus Samad mail ,

Shaha

<a class="ep_document_link" href="/11941/1/healthcare-12-00942.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Risk Factors for Eating Disorders in University Students: The RUNEAT Study

The purpose of the study is to assess the risk of developing general eating disorders (ED), anorexia nervosa (AN), and bulimia nervosa (BN), as well as to examine the effects of gender, academic year, place of residence, faculty, and diet quality on that risk. Over two academic years, 129 first- and fourth-year Uneatlántico students were included in an observational descriptive study. The self-administered tests SCOFF, EAT-26, and BITE were used to determine the participants’ risk of developing ED. The degree of adherence to the Mediterranean diet (MD) was used to evaluate the quality of the diet. Data were collected at the beginning (T1) and at the end (T2) of the academic year. The main results were that at T1, 34.9% of participants were at risk of developing general ED, AN 3.9%, and BN 16.3%. At T2, these percentages were 37.2%, 14.7%, and 8.5%, respectively. At T2, the frequency of general ED in the female group was 2.5 times higher (OR: 2.55, 95% CI: 1.22–5.32, p = 0.012). The low-moderate adherence to the MD students’ group was 0.92 times less frequent than general ED at T2 (OR: 0.921, 95%CI: 0.385–2.20, p < 0.001). The most significant risk factor for developing ED is being a female in the first year of university. Moreover, it appears that the likelihood of developing ED generally increases during the academic year.

Producción Científica

Imanol Eguren García mail imanol.eguren@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es, Sandra Conde González mail , Anna Vila-Martí mail , Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, Raquel Martínez Díaz mail raquel.martinez@uneatlantico.es, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es,

Eguren García

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Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning

Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning. Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data. Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID. Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections.

Producción Científica

X. Sumbul mail , Arshiya Sultana mail , Md Belal Bin Heyat mail , Khaleequr Rahman mail , Faijan Akhtar mail , Saba Parveen mail , Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, Vivian Lipari mail vivian.lipari@uneatlantico.es, Isabel De la Torre Díez mail , Azmat Ali Khan mail , Abdul Malik mail ,

Sumbul