Journal of Artificial Intelligence and Metaheuristics

This study investigates machine-learning approaches for classifying secure DNS traffic and identifying threats associated with DNS over HTTPS (DoH). Using the DoHBrw-2020 dataset, we compare Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, a multilayer perceptron (MLP), Gradient Boosting, and SVM with an RBF kernel. Models are evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score. The Decision Tree achieves the highest accuracy (99.65%) and strong results across the evaluation criteria. These findings demonstrate the potential of machine learning to improve DNS traffic security and support the development of real-time DoH threat detection. The results also inform the design of safer DNS solutions for contemporary information-security environments.

Education is central to social and economic development, making school enrollment and the quality of learning important global priorities. This study applies machine-learning regression to the World Educational Data dataset from Kaggle. After data exploration and preprocessing, eleven models are compared: Support Vector Regression (SVR), CatBoost, Random Forest, Extra Trees, XGBoost, a multilayer perceptron (MLP), Gradient Boosting, Decision Tree, K-Nearest Neighbors, Linear Regression, and a pipeline model. Performance is evaluated using MSE, RMSE, MAE, MBE, the correlation coefficient, R2, NSE, and WI. CatBoost reports the strongest correlation and coefficient of determination (both approximately 0.99999) with an MSE of 0.04024. The results illustrate the potential of machine-learning methods to support analysis and prediction of educational outcomes and to inform educational planning.

Student performance prediction is essential so that institutions can assist in identifying weak performers and initiate corrective measures. This research assesses different regression models by applying data from Kaggle, which involves data cleaning like managing missing values and scaling of the data, hence feature extraction, then model imposition and authenticity. The models followed are Linear Regression, SVR, MLPRegressor, Gradient Boosting, CatBoost, XGBoost, Random Forest, Extra Trees, Decision Tree and K-neighbors. The analysis shows that Linear Regression produced the best result as it has the lowest MSE score of 0.000521 and high accuracy regarding other measures, including RMSE, MAE, and R2. The results reveal that regression models can be used to predict students’ performance and be helpful to the various stakeholders in the system. The findings of this study will help develop required models for decision-making to improve students’ performance.

This paper focuses on Exploratory Data Analysis of the data from the “International Student Demographics,” which is available on Kaggle and comprises data collected through the academic years, as well as total students, U.S. students, undergraduate, graduate, non-degree students, and OPT columns. In the given work, the author intends to provide a chronological overview of the demographic data of international students. Operations like handling missing values and outliers were done to prepare the data for a more elaborate analysis. All descriptive analyses during the study included time series plots and bar charts where time series was used to evidence key trends and distributions. The analyses of the research questions indicate that there has been growth in international student enrollment over the decades, particularly in undergraduate and OPT student categories, with influences from world events such as COVID-19 and the alteration of immigration policies. Country-wise contribution reveals that the maximum number of articles originated from East Asia and South and Central Asia, with a special focus on engineering, social sciences, and humanities. Solutions: The paper articulates the finality of trends affecting educational institutions and policymakers by focusing on the implications of international students’ demographics. Based on the findings above, future research directions are proposed to improve the findings and support evidence-based practice relating to international education.

CO2 emission prediction is crucial for environmental policy and climate change mitigation. This review explores time series analysis and metaheuristic optimization in CO2 forecasting, summarizing research findings and methodological insights. Time series analysis uncovers past patterns and future trends, while metaheuristic methods such as genetic algorithms optimize forecasting accuracy. Challenges include data quality, model complexity, and computational demands. Advanced machine learning has the potential to make CO2 forecasting more accurate and efficient. Composite models that combine approaches show promise, alongside real-time data integration and advanced machine learning. Future research should prioritize comprehensive databases and interdisciplinary collaboration to refine models. Improvements in forecasting can aid policy decisions and climate-change mitigation, highlighting the growing need for accurate CO2 predictions and advanced analytical techniques.

Machine learning (ML)-based techniques are increasingly used to address problems in fields such as finance, marketing, production, the environment, health care, and security. ML can identify patterns, interpret data, reveal hidden relationships, and analyze large datasets, potentially improving the specificity, sensitivity, predictability, and stability of diagnostic systems. This review examines the application of machine learning in medicine and its use in diagnostics, including medical chemistry, wearable sensors, cancer, brain health, and medical imaging. It discusses application-specific model adaptations and offers findings and references to guide academics, practitioners, decision-makers, and future research.

This review focuses on feature selection as an important parameter in tuning machine-learning models. It surveys optimization algorithms including Moth-Flame Optimization (MFO), the hybrid GA–GSA algorithm, Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Henry Gas Solubility Optimization (HGSO), and safety-constrained Bayesian optimization. These algorithms are used in engineering design, power-system scheduling, and other application areas. The review emphasizes optimization’s role in improving efficiency, reducing errors, and increasing model interpretability, and examines applications in structural engineering, additive manufacturing, and landslide-susceptibility mapping. A summary table presents the studies, methods, and key findings. The reviewed work demonstrates the adaptability and competitiveness of optimization algorithms across diverse domains and aims to guide researchers and practitioners by describing current approaches and directions for future research.