Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.…”
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An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes by Juan Lu, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, Bing Huang

“…To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…This method makes full use of the diversity characteristics of base learners, leverages their classification ability, optimizes the selection method for ensemble learning, and enhances the predictive performance of the ensemble model. The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning by Banu İsmail Mendi, Hatice Şanlı, Mert Akın Insel, Beliz Bayındır Aydemir, Mehmet Fatih Atak

“…The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients.…”
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Predicting Students’ Performance Using a Hybrid Machine Learning Approach by Ropafadzo Duwati, Tawanda Mudawarima

“…Previous studies have employed individual ML algorithms for performance prediction; these models often suffer from limitations such as low accuracy and bias towards specific data characteristics. …”
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Using topological data analysis and machine learning to predict customer churn by Marcel Sagming, Reolyn Heymann, Maria Vivien Visaya

“…An effective way to further improve churn prediction capability of different ML algorithms is through the employment of topological data analysis (TDA). …”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

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An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari

“…Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”
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Short-Term Prediction of Traffic Flow Based on the Comprehensive Cloud Model by Jianhua Dong

“…These algorithms are designed to address the short-term traffic flow prediction problem. …”
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Using artificial intelligence techniques and econometrics model for crypto-price prediction by Abhidha Verma, Jeewesh Jha

“…The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). …”
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Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

“…However, the issue of data imbalance in these studies is often overlooked, despite its crucial role in enhancing the accuracy of CHD risk identification within classification algorithms. Objective To investigate the factors influencing CHD and to establish predictive models for CHD risk using two data balancing methods based on five algorithms, comparing the predictive value of these models for CHD risk. …”
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Predicting students’ academic procrastination tendencies using online learning trajectories by Gisu Sanem Öztaş, Gökhan Akçapınar

“…The study compared the performance of different machine learning algorithms in predicting students’ academic procrastination tendencies, analysed the important features of prediction models, and examined whether there is a difference between the academic performance of low and high academic procrastinators. …”
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Implementation of the healthy heart tool- an algorithm with potential cardiometabolic health benefits in persons with severe mental illness by Elisabeth Haug Lund-Stenvold, Petter A. Ringen, Ole A. Andreassen, Torfinn L. Gaarden, Cecilie B. Hartberg, Erik Johnsen, Silje Myklatun, Kåre Osnes, Kjetil Sørensen, Arne Vaaler, Serena Tonstad, John A. Engh, Anne Høye

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A machine learning-powered energy consumption prediction system with API by Toyeeb Adekunle Abd’Azeez, Lanre Olatomiwa

“…The lower MAPE and the higher R2 score indicate the superiority of the ExtraTreeRegressor over other algorithms. While energy consumption is characterised by high variance, our optimised model effectively interprets interactions between input features and predicts the equivalent energy consumed with a lower RMSE of 11.75. …”
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Machine Learning for Prediction of Relapses in Multiple Drug Resistant Tuberculosis Patients by A. S. Аlliluev, O. V. Filinyuk, E. E. Shnаyder, S. V. Аksenov

“…The objective of the study: to evaluate the possibility of using machine learning algorithms for prediction of relapses in multiple drug resistant tuberculosis (MDR TB) patients.Subjects and Methods. …”
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Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses by Miruna-Ioana Belciu, Alin Velea

“…This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. …”
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Specifics of predicting the profitability of individual bank products based on machine learning by Inna Strelchenko, Dmytro Stognii, Anatolii Strelchenko

“…It is shown that neural networks have the highest level of predictive accuracy, but their implementation requires significant computational resources. …”
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An explainable AI-based approach for predicting undergraduate students academic performance by Fatema Tuz Johora, Md Nahid Hasan, Aditya Rajbongshi, Md Ashrafuzzaman, Farzana Akter

“…Two eXplainable Artificial Intelligence (XAI) algorithms, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), were integrated to provide a comprehensible prediction of the best model and determine the significant factors. …”
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Feasibility of Conflict Prediction of Drone Trajectories by Means of Machine Learning Techniques by Victor Gordo, Javier A. Perez-Castan, Luis Perez Sanz, Lidia Serrano-Mira, Yan Xu

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Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke by Yajun Gao, Ruyu Bai, Bo Gao, Ma Li

“…Potential drugs or molecular compounds that interact with key genes were predicted by searching DGIdb, and the drug-gene interaction network was visualized by Cytoscape software. …”
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