Journal of Artificial Intelligence and Metaheuristics

This paper provides a detailed review of related works for classifying secure DNS traffic, with emphasis on the identification of threats relating to DoH using machine learning algorithms. In the present study, with the help of DoHBrw-2020 dataset consisting the network traffic data of DoH protocol during its testing phase, we compare the performance of various machine learning algorithms: Decision Tree, SVM, KNN, Na¨ıve Bayes, Neural Network (MLP), Gradient Boosting, and SVM with RBF kernel. As for each model, we have Accuracy, Sensitivity, Specificity, Positive Predicted Value, Negative Predicted Value, and F Score. They reveal the fact that the chosen Decision Tree model produces the highest accuracy and equals to 99. 65% and all the criteria of the assessment should be well managed. It is important that the various machine learning methods contribute to the study’s discovery of high potential in improving DNS traffic security and offers an understanding on the best models to use for real-time detection of DoH threats. From these outcomes, it can draw many perspectives to the further creation and implementation of safer DNS solutions within contemporary information security paradigms.

Education contributes a crucial portion to the world’s development; thus, it is crucial to focus on education enrollment and quality education. It is essential not only that children enroll in school but also that they receive proper education to improve individuals and, consequently, society. This paper aims to use machine learning to predict educational outcomes based on the World Educational Data obtained from Kaggle to analyze the data, preprocess it, and evaluate the performances of the different regression models. The following models consist of Support Vector Regression (SVR), CatBoost, RandomForestRegressor, ExtraTreesRegressor, GBoost, MLPRegressor, GradientBoosting Regressor, DecisionTreeRegressor, KNeighborsRegressor, LinearRegression, and Pipeline. Evaluation measures used included MSE, RMSE, MAE, MBE, r, R2, NSE, and WI. Analyzing the performance comparison, the best accuracy was associated with CatBoost with an r value equal to 0.999996 and an R2 value of 0. 999993; The MSE score was 0.04024. The outcomes of the present paper demonstrate that the application of advanced machine learning algorithms can be used effectively to predict educational outcomes, thus enabling policymakers and educational planners to use them for designing effective educational policies and overcoming existing global challenges in the sphere of education.

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, Extratrees, 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 like genetic algorithms optimize forecasting accuracy. Challenges include data quality, model complexity, and computational demands. However, the potential of advanced machine learning is a beacon of hope. It can revolutionize CO2 forecasting, making it more accurate and efficient. Composite models combining approaches show promise alongside real-time data integration and advanced machine learning. Future research should prioritize comprehensive databases and, importantly, stress the need for interdisciplinary collaboration to refine models. Improvements in forecasting can aid policy decisions and combat climate change, highlighting the growing need for accurate CO2 predictions and advanced analytical techniques.

Machine learning (ML) based techniques have enjoyed significant popularity in addressing the hostility of numerous problems in a range of applications, such as finance, marketing, production, environment, health care, and security. One of the most important distinctions between machine learning and human ways of thinking is their ability to observe patterns, make interpretations, reveal some hidden relationships, and analyze huge amounts of data. Machine learning (ML) technology can lead to improved specificity, sensitivity, predictability, and steadiness of such systems. Through this review, though, we will have an in-depth discourse on the application of machine learning in the field of medicine and how the latest technologies are mostly deployed in diagnostics. Medical applications that are widely used, including but not limited to machine learning solutions for medical chemistry, wearable sensors, cancer, the brain, and medical imaging, will be discussed in detail, with a focus on model adjustments to address the problems faced by the applications. In the course of the work, academics, practitioners, and decision-makers will have plenty of opportunities to utilize the findings, references, and insights of this study to improve their work and steer future research.

This article review focuses on feature selection as the main parameter that plays a major role in tuning machine learning models. Several algorithms of optimization such as MFO (Moth-Flame Optimization), the GA-GSA algorithm’s hybrid type, SOA (Seagull Optimization Algorithm), WOA (Whale Optimization Algorithm), GOA (Grasshopper Optimization Algorithm), HGSO (Henry Gas Solubility Optimization), and SafeOpt are widely used in engineering design, power systems scheduling, The paper stresses the importance of optimization in improving efficiency, lessening mistakes and increasing understandability of machine learning models. The literature addresses the widest directions in the usage of optimization for the following fields of science such as structural engineering, additive manufacturing, and landslide susceptibility mapping. A comprehensive summary table is generated, which shows an overview of each study, algorithm, focus, and methodology and has a stoke of key findings. The conclusions reveal the adaptiveness, competitiveness and compossibility of the optimization algorithms applied to a wide range of domains. The summary shows how optimization has the potential to change decision-making processes and activities by being a decisive factor that determines the future of branches of various industries. The main objective of this work is to direct researchers and practitioners by providing them with some innovative ideas and approaches and offering insights on the existing cutting-edge approaches while laying the groundwork for future innovations in optimization.