Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures by Mohsin Ali, Li Chen, Qadir Bux Alias Imran Latif Qureshi, Deema Mohammed Alsekait, Adil Khan, Kiran Arif, Muhammad Luqman, Diaa Salama Abd Elminaam, Amir Hamza, Majid Khan

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Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm by Xiaojie Zhou, Majid Khan Majahar Ali, Farah Aini Abdullah, Lili Wu, Ying Tian, Tao Li, Kaihui Li

“… Air pollution significantly impacts human health and socioeconomic development, making accurate air quality prediction crucial. This study proposes a hybrid CNN-LSTM-Attention model optimized with an improved Dung Beetle Optimization (IDBO) algorithm to enhance predictive performance. …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

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Harmonized Integration of GWO and J-SLnO for Optimized Asset Management and Predictive Maintenance in Industry 4.0 by A. N. Arularasan, P. Ganeshkumar, Mohammad Alkhatib, Tahani Albalawi

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Springback Angle Prediction for High-Strength Aluminum Alloy Bending via Multi-Stage Regression by Enzhi Gao, Di Xue, Yiming Li

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Student’s Digital Intentions Prediction Using CatBoost by Zulwisli, Ambiyar, Muhammad Anwar, Andhika Herayono

“…A recent study developed a predictive model using the CatBoost algorithm to understand better the factors influencing digital entrepreneurship. …”
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Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms by Hang Zhou, Jie Wu, Jiangtao Yang, Qingyang Fan

“…In order to discover and explore new III–V semiconductor materials more efficiently and accurately, this paper adopts a machine-learning method optimized by the beetle algorithm and combined with first-principle calculation verification to efficiently and accurately predict the performance of III–V nitride materials and study their physicochemical properties. …”
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Course Scheduling Using Genetic Algorithms Enhanced by Linear Regression for Data Mining Course Participants by Arie Susetio Utami, Agust Isa Martinus, Freddy Wicaksono, Rangga Manggala Yudha

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Developing a hybrid machine learning model for employee turnover prediction: Integrating LightGBM and genetic algorithms by Hojat Talebi, Amid Khatibi Bardsiri, Vahid Khatibi Bardsiri

“…In this study, we propose a novel hybrid model for employee turnover prediction that integrates Genetic Algorithms (GA) for feature selection with LightGBM for classification. …”
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Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury by Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan

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A Comparative Analysis of Different Machine Learning Algorithms Developed with Hyperparameter Optimization in the Prediction of Student Academic Success by Bahar Demirtürk, Tuba Harunoğlu

Subjects: “…student academic success prediction…”
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Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm by Javad Shahlaei Bagheri

“…Purpose: This study aimed to predict and manage physical injuries caused by gym equipment and facilities using the SVM algorithm.Method: This study was of a developmental-applied type. …”
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Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach by Qoriani Widayati, Kusworo Adi, R Rizal Isnanto, Eka Puji Agustini, Dewa Rizki Rahmat Julianto, Fawwaz Bimo Prakasa

“…Student loyalty is a crucial factor supporting the sustainability of higher education institutions. The aim of this study is to predict student loyalty using a machine learning approach, specifically the random forest algorithm. …”
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A hybrid model combining environmental analysis and machine learning for predicting AI education quality by Xinyu Ren

“…The results showed that corrective measures in all environments can help the development of AI education in universities. The results of conducting a case study and examining various evaluation indicators showed that the proposed approach in this study has a good accuracy in predicting the target variable (quality of education). …”
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Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Yaqoob Wani

“…Abstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing educational outcomes. …”
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Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions by Wesam Ahmed, Mudasir Ahmad Wani, Pawel Plawiak, Souham Meshoul, Amena Mahmoud, Mohamed Hammad

“…Ten regression models including K-Nearest Neighbors Regressor, Linear Regression, CatBoost, XGBoost, AdaBoost, and ensemble voting regression (VR) algorithm based on the top five heterogeneous regressors as base models are employed to predict academic outcomes. …”
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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Results he RF algorithm showed high performance in both prediction approaches. …”
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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. …”
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Predictive Direct Power Control of Single-phase Power Electronic Transformer Rectifier by SONG Pingang, ZHU Weichang, GE Wang

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Investigating the Predictive Performance of Process Data and Result Data in Complex Problem Solving Using the Conditional Gradient Boosting Algorithm by Fatma Nur Aydin, Kubra Atalay Kabasakal, Ismail Dilek

“…This study aims to examine the predictive performance of process data and result data in complex problem-solving skills using the conditional gradient boosting algorithm. …”
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