Organização e Gestão em Escolas de Samba Mirins
Tesis Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster Cerrado Portugués As escolas de samba são planetas no universo da cidade do Rio de Janeiro, exercendo uma forma espontânea e peculiar de organização e gestão. No seu dia a dia há o trabalho com a preservação da herança cultural negra e mestiça. Tendo como centro o carnaval, estas agremiações desdobram-se no cotidiano, fazendo com que as novas gerações percebam a sua existência e importância e os mais velhos se achem capazes de ensinar o que lhes foi legado pelos seus familiares, amigos e lideranças. As agremiações mirins, em sua maioria procedentes das “escolas-mães”, têm como escopo, além da preservação desta cultura carioca cujo samba já é caracterizado como “Patrimônio Cultural Brasileiro”, incentivar atividades sócio-culturais visando à formação para a cidadania, inclusão social e formação profissionalizante para crianças e adolescentes das comunidades onde estas agremiações estão situadas. Este trabalho toma como problema de investigação, a organização e gestão nestas escolas de samba mirins, desde o momento da escolha de um enredo e de um samba até a organização dos ensaios, confecção e distribuição de fantasias, visando ao desfile principal. Para dar conta do objetivo deste projeto, os procedimentos metodológicos adotados baseiam-se em pesquisas bibliográficas, levantamento e análise de dados decorrentes de visitas assistemáticas e não participantes em algumas destas agremiações mirins, assim como entrevistas, com abordagens qualitativas e não estruturadas, com presidentes, diretores de alas e carnavalescos.A organização varia de agremiação para agremiação, dependendo da sua estrutura, porém o resultado final mostra-se positivo em todas, pois contam com o amor dos seus integrantes à sua escola e ao samba. O sucesso dos desfiles dependem, exclusivamente, da organização e gestão executadas nas agremiações em todos os quesitos avaliados durante o desfile, onde todas as escolas e seus componentes são campeões. metadata Vieira da Silva, Celso Luiz mail celsosilva@littleengland.com.br (2022) Organização e Gestão em Escolas de Samba Mirins. Masters thesis, SIN ESPECIFICAR.
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As escolas de samba são planetas no universo da cidade do Rio de Janeiro, exercendo uma forma espontânea e peculiar de organização e gestão. No seu dia a dia há o trabalho com a preservação da herança cultural negra e mestiça. Tendo como centro o carnaval, estas agremiações desdobram-se no cotidiano, fazendo com que as novas gerações percebam a sua existência e importância e os mais velhos se achem capazes de ensinar o que lhes foi legado pelos seus familiares, amigos e lideranças. As agremiações mirins, em sua maioria procedentes das “escolas-mães”, têm como escopo, além da preservação desta cultura carioca cujo samba já é caracterizado como “Patrimônio Cultural Brasileiro”, incentivar atividades sócio-culturais visando à formação para a cidadania, inclusão social e formação profissionalizante para crianças e adolescentes das comunidades onde estas agremiações estão situadas. Este trabalho toma como problema de investigação, a organização e gestão nestas escolas de samba mirins, desde o momento da escolha de um enredo e de um samba até a organização dos ensaios, confecção e distribuição de fantasias, visando ao desfile principal. Para dar conta do objetivo deste projeto, os procedimentos metodológicos adotados baseiam-se em pesquisas bibliográficas, levantamento e análise de dados decorrentes de visitas assistemáticas e não participantes em algumas destas agremiações mirins, assim como entrevistas, com abordagens qualitativas e não estruturadas, com presidentes, diretores de alas e carnavalescos.A organização varia de agremiação para agremiação, dependendo da sua estrutura, porém o resultado final mostra-se positivo em todas, pois contam com o amor dos seus integrantes à sua escola e ao samba. O sucesso dos desfiles dependem, exclusivamente, da organização e gestão executadas nas agremiações em todos os quesitos avaliados durante o desfile, onde todas as escolas e seus componentes são campeões.
| Tipo de Documento: | Tesis (Masters) |
|---|---|
| Palabras Clave: | Organização, Gestão, Jovens, Formação, Escolas de samba mirins |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster |
| Depositado: | 17 Nov 2023 23:30 |
| Ultima Modificación: | 17 Nov 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/2298 |
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Detection and classification of brain tumor using a hybrid learning model in CT scan images
Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model’s complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3–4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings.
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Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2
Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection.
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Breast cancer is a lethal carcinoma impacting a considerable number of women across the globe. While preventive measures are limited, early detection remains the most effective strategy. Accurate classification of breast tumors into benign and malignant categories is important which may help physicians in diagnosing the disease faster. This survey investigates the emerging inclination and approaches in the area of machine learning (ML) for the diagnosis of breast cancer, pointing out the classification techniques based on both segmentation and feature selection. Certain datasets such as the Wisconsin Diagnostic Breast Cancer Dataset (WDBC), Wisconsin Breast Cancer Dataset Original (WBCD), Wisconsin Prognostic Breast Cancer Dataset (WPBC), BreakHis, and others are being evaluated in this study for the demonstration of their influence on the performance of the diagnostic tools and the accuracy of the models such as Support vector machine, Convolutional Neural Networks (CNNs) and ensemble approaches. The main shortcomings or research gaps such as prejudice of datasets, scarcity of generalizability, and interpretation challenges are highlighted. This research emphasizes the importance of the hybrid methodologies, cross-dataset validation, and the engineering of explainable AI to narrow these gaps and enhance the overall clinical acceptance of ML-based detection tools.
Alveena Saleem mail , Muhammad Umair mail , Muhammad Tahir Naseem mail , Muhammad Zubair mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Shoaib Hassan mail , Imran Ashraf mail ,
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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.
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Polyphenols are naturally occurring compounds that can be found in plant-based foods, including fruits, vegetables, nuts, seeds, herbs, spices, and beverages, the use of which has been linked to enhanced brain health and cognitive function. These natural molecules are broadly classified into two main groups: flavonoids and non-flavonoid polyphenols, the latter including phenolic acids, stilbenes, and tannins. Flavonoids are primarily known for their potent antioxidant properties, which help neutralize harmful reactive oxygen species (ROS) in the brain, thereby reducing oxidative stress, a key contributor to neurodegenerative diseases. In addition to their antioxidant effects, flavonoids have been shown to modulate inflammation, enhance neuronal survival, and support neurogenesis, all of which are critical for maintaining cognitive function. Phenolic acids possess strong antioxidant properties and are believed to protect brain cells from oxidative damage. Neuroprotective effects of these molecules can also depend on their ability to modulate signaling pathways associated with inflammation and neuronal apoptosis. Among polyphenols, hydroxycinnamic acids such as caffeic acid have been shown to enhance blood-brain barrier permeability, which may increase the delivery of other protective compounds to the brain. Another compound of interest is represented by resveratrol, a stilbene extensively studied for its potential neuroprotective properties related to its ability to activate the sirtuin pathway, a molecular signaling pathway involved in cellular stress response and aging. Lignans, on the other hand, have shown promise in reducing neuroinflammation and oxidative stress, which could help slow the progression of neurodegenerative diseases and cognitive decline. Polyphenols belonging to different subclasses, such as flavonoids, phenolic acids, stilbenes, and lignans, exert neuroprotective effects by regulating microglial activation, suppressing pro-inflammatory cytokines, and mitigating oxidative stress. These compounds act through multiple signaling pathways, including NF-κB, MAPK, and Nrf2, and they may also influence genetic regulation of inflammation and immune responses at brain level. Despite their potential for brain health and cognitive function, polyphenols are often characterized by low bioavailability, something that deserves attention when considering their therapeutic potential. Future translational studies are needed to better understand the right dosage, the overall diet, the correct target population, as well as ideal formulations allowing to overcome bioavailability limitations.
Justyna Godos mail , Giuseppe Carota mail , Giuseppe Caruso mail , Agnieszka Micek mail , Evelyn Frias-Toral mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Julién Brito Ballester mail julien.brito@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Carmen Lilí Rodríguez Velasco mail carmen.rodriguez@uneatlantico.es, José L. Quiles mail jose.quiles@uneatlantico.es,
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