Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

Forecasting student performance, sorting students into groups according to their strengths, and working to improve future test scores are all crucial for any institution in today's competitive world. It is important to give students ample notice before a school year begins if they are to be coached to improve their grades by focusing on a certain subject area. Examining this can helps a school significantly reduce its dropout rate. This analysis predicts how well students will do in a given course based on how they did in previous, similar courses. Discovering previously unknown relationships among vast stores of data is the goal of data mining. Insights and forecasts might be gained from these recurring structures. The term education data mining describes the assortment of data mining programs used in the educational sector. The primary focus of these tools is on analyzing the information gathered from classrooms and educators. Potential applications of this research include classification and forecasting. It looks into several machine learning methods, including Naive Bayes, ID3, C4.5, and SVM. The experimental analysis uses data collection containing UCI machinery students' grades and other outcomes. Accuracy and error rate are two metrics used to evaluate algorithms.

Diabetic foot (DF) is one of the most common chronic complications of poorly controlled diabetes mellitus (DM). Early diagnosis of DF and effective treatment is usually difficult by traditional approaches. Lately, it has been found a strong relationship between temperature variation and diabetic foot ulcer emergence. Thus, the current study focused on monitoring the temperature of feet using thermal images and its analysis techniques. The proposed system was based on employing a deep convolutional neural network (CNN) on thermal foot images. Experimental results showed that the proposed CNN has a maximum accuracy of 99.3% with minimum losses. When comparing the proposed system to other relevant systems, the proposed system approved greater accuracy, lower elapsed and testing time, which offers an automatic diagnostic tool for the diabetic foot and differentiates between its types. Thus, a simple, cost-effective, and accurate computer aided design (CAD) system could be presented to get a valuable system for the clinicians in hospitals.

Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs, improving material utilization, and ensuring structural safety in modern construction. Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents, especially with the growing use of supplementary cementitious materials and recycled aggregates. This study presents an integrated machine learning framework for concrete strength prediction, combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms, with a particular focus on the Somersaulting Spider Optimizer (SSO). A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models, including CatBoost, XGBoost, ExtraTrees, and RandomForest. Among these, CatBoost demonstrated superior accuracy across multiple performance metrics. To further enhance predictive capability, several bio-inspired optimizers were employed for hyperparameter tuning. The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients, outperforming other metaheuristic approaches such as Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer. Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing, confirming the robustness of the optimized models. The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization, supporting data-driven decision making in sustainable and resilient infrastructure development.

American scientific publishing group

Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes. This study introduces a hybrid approach integrating Long Short-Term Memory (LSTM) networks with the Hybrid Greylag Goose and Particle Swarm Optimization (GGPSO) algorithm to optimize preterm birth classification using Electrohysterogram signals. The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings, capturing key physiological features such as contraction patterns, entropy, and statistical variations. Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability. LSTM networks effectively capture temporal patterns in uterine activity, while the GGPSO algorithm finetunes hyperparameters, mitigating overfitting and improving classification accuracy. The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34% accuracy, 96.91% sensitivity, 97.74% specificity, and 97.23% F-score, significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications. By combining deep learning with metaheuristic optimization, this study contributes to advancing intelligent auto-diagnosis systems, facilitating early detection of preterm birth risks and timely medical interventions.