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LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination
Internet connectivity has significantly enhanced the efficiency of daily operations, information retrieval, and global communication. However, this heightened reliance on technology has also exposed us to cybersecurity threats that are often beyond our control. Consequently, securing the data, privacy, and critical systems demands essential cybersecurity measures. This study focuses on the role of artificial intelligence in strengthening security systems to thwart network breaches. The study proposes a comprehensive three-part approach for software-defined networking (SDN) security. The first is that the concentration is on assuring data integrity and reliability for an SDN intrusion dataset. This involves critical steps such as data cleaning, preprocessing, and normalization. In the second step, six popular feature selection strategies are applied, which encompass recursive feature elimination (RFE), polynomial features, artificial neural networks, SelectKBest, least absolute shrinkage and selection operator (LASSO), and correlation-based features. These techniques help identify and incorporate significant and relevant features, thereby improving the overall model performance. The third part involves the creation of a lightweight hybrid model (LwHM) that leverages the strengths of k-nearest neighbors and decision tree models, utilizing a voting classifier. The LwHM surpasses the performance of the InSDN dataset, achieved an impressive accuracy score of 99.93% with RFE features, and enhance the SDN security efficiently.
Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model
The wafer map is a data visualization of a thin semiconductor fabric made of crystalline silicon, such as defects or test results. The wafer map is a base for creating electronic coordinate circuits and photovoltaic cells. During the wafer map production, any fault results in a product failure. The wafer map faults are undetectable to the naked eye, which is a big challenge. Hotspot detection in wafer maps is significantly important to evaluate the manufacturing process and. improve product yield. The hotspot detection in the wafer maps is the primary aim of this research. A novel wafer map hotspot detector (WHD) is proposed based on three stack fully connected conventional neural network layers and a dense layer. Data augmentation uses the segmented images of the wafers to build the proposed model. The proposed model is evaluated through several evalua-tion parameters and state-of-the-art studies comparative analysis. The proposed model achieved a 94% training and 90% testing performance accuracy for hotspot detection and shows better results than existing approaches. This study helps semiconductor engineers improve wafer manufacturing designs and efficiency in the semiconductor industry.
Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies
Background: Recovery after a training session or match is a key factor in injury prevention and sports performance. The purpose of this systematic review was to analyze and consolidate the available scientific evidence from the main databases on the use of infrared thermography in the assessment of fatigue, injury risk factors, and recovery in soccer players.Methods: The literature search was conducted following the PRISMA guidelines and the PICOS model until June 30, 2025, in the main scientific databases (ScienceDirect, EMBASE, Web of Science (WOS), Cochrane Library, SciELO, MEDLINE/PubMed, SPORTDiscus, and Scopus). The risk of bias and methodological quality were assessed using the Cochrane Handbook guidelines and the PEDro scale.”Results: The initial literature search yielded a total of 510 records. After applying the inclusion and exclusion criteria, the final sample consisted of 20 studies, which were of high methodological quality. The results showed the effects of infrared thermography in assessing fatigue, identifying injury risk factors, and monitoring recovery processes in soccer players. The studies also systematically reported the characterization of the population, the assessment methods used, the variables analyzed, the methodological design, the main results, and the effects of the intervention.Conclusions: Infrared thermography shows promise as a valid, reliable, and non-invasive tool for assessing skin temperature, reflecting temperature changes in response to physiological processes. It allows for the analysis of structural or metabolic fatigue and thermal asymmetries. Therefore, thermography could be used to design individualized recovery protocols.
Predicting Academic Award Recognition Across Disciplines Using Publication-Based Bibliometric Indices and SHAP-Driven Explainability
Researcher evaluation underpins critical academic decisions, yet traditional bibliometric indicators lack predictive capability and cross-domain generalizability, while most predictive approaches offer limited interpretability and narrow domain validation. This study proposes a SHAP interpretable, multi-domain supervised learning framework for predicting academic award recognition using thirty two publication count-based bibliometric indices. A balanced dataset was constructed across four disciplines, namely Computer Science, Neuroscience, Mathematics, and Civil Engineering, comprising verified awardees from recognized professional societies and matched non-awardee researchers. Eight classifiers were evaluated under stratified five fold cross validation, assessed via accuracy, precision, recall, F1-score, and ROC AUC. The framework achieved domain-specific F1-scores of 0.70 in Computer Science, 0.73 in Neuroscience, 0.72 in Civil Engineering, and 0.78 in Mathematics, with SVM and XGBoost demonstrating the strongest cross-domain robustness across disciplines. SHAP analysis consistently identified normalized h index, h2 family, q2 index, and g index as dominant cross-domain predictors, while domain-specific indicators, including Rm and w indices in Neuroscience and P index in Civil Engineering, reflected disciplinary recognition patterns. By unifying publication-based feature engineering, multi-domain classification, and SHAP explainability within a single reproducible pipeline, this framework offers a scalable, transparent, and evidence-based tool for institutional researcher evaluation.
Environmental burden of fish in healthy and sustainable diets
Fish is widely promoted as part of healthy dietary patterns. The aim of this review was to summarise current literature on the environmental footprint of fish and its role within sustainable diets. Fish generally represents a minor share of total dietary environmental impacts, contributing to a smaller proportion of greenhouse-gas emissions (GHGe), land and water use than meat and other animal products. Several modelling studies showed that substituting meat with fish or increasing fish intake within optimised dietary patterns can reduce environmental impacts, although the magnitude varies by country, diet type, and fish species. However, some analyses reported increased GHGe associated with higher fish intake, especially in models ensuring nutritional quality. Overall, fish consumption is compatible with achieving nutritionally adequate and lower environmental impacts, although optimal match between environmental boundaries and nutritional needs is not always possible. These findings suggest that fish can play a constructive role in sustainable diets when integrated thoughtfully within broader dietary shifts.