Large Language Model and Traditional Machine Learning Scoring of Evolutionary Explanations: Benefits and Drawbacks by Yunlong Pan, Ross H. Nehm

“…Few studies have compared Large Language Models (LLMs) to traditional Machine Learning (ML)-based automated scoring methods in terms of accuracy, ethics, and economics. …”
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AI Tutor: Transforming Education with Intelligent Learning by Fazel Keshtkar, Sebastian Chalarca, Giulio Bardelli, Allan Ilyasov, Sebastian Torres, Syed Ahmad Chan Bukhari

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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

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Comparing the Effectiveness of Machine Learning and Deep Learning Models in Student Credit Scoring: A Case Study in Vietnam by Nguyen Thi Hong Thuy, Nguyen Thi Vinh Ha, Nguyen Nam Trung, Vu Thi Thanh Binh, Nguyen Thu Hang, Vu The Binh

“…This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. …”
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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines by Cheng Yin, Le Thanh Binh, Duong Tran Anh, Son T. Mai, Anh Le, Van-Hau Nguyen, Van-Chien Nguyen, Nguyen Xuan Tinh, Hitoshi Tanaka, Nguyen Trung Viet, Long D. Nguyen, Trung Q. Duong

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Positive relationship between education level and risk perception and behavioral response: A machine learning approach. by Zhipeng Wei, Zhichun Zhang, Liping Guo, Wenjie Zhou, Kehu Yang

“…Utilizing non-parametric estimation techniques in machine learning, particularly the Random Forest and XGBoost algorithms, this study develops predictive models to analyze the impact of 27 influencing factors on behavioral responses following risk perception. …”
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Data driven decisions in education using a comprehensive machine learning framework for student performance prediction by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Zeeshan Babar, Abeer Aljohani, Muhammad Arif

“…This study underscores the transformative potential of machine learning in education, paving the way for more adaptive and student-centered learning environments.…”
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Determinants Factors in Predicting Life Expectancy Using Machine Learning by B. Kouame Amos, I. V. Smirnov

“…However, with the existence of various conditions and complications witnessed in society today, several factors need to be taken into consideration to predict life expectancy. Therefore, various machine learning models have been developed to predict life expectancy.   …”
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Navigating the data frontier in science assessment: Advancing data augmentation strategies for machine learning applications with generative artificial intelligence by Paul P. Martin, Nicole Graulich

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The Impact of Digital Technology Use on Teaching Quality in University Physical Education: An Interpretable Machine Learning Approach by Liguo Zhang, Zetan Liu, Liangyu Zhao, Jiarui Gao

“…This study examined 1158 university students across China using a cross-sectional design, integrating interpretable machine learning models with structural equation modeling to systematically assess how the frequency of use of seven common digital technologies influences teaching quality in physical education classes. …”
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Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
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Noise Calculation Technique Using Time Varying Model by Saber Izadpanah Tous, Mahmoud Behroozi, Ehsan Kargaran, Hooman Nabovati

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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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Editorial: Exploring machine learning applications in visceral surgery by Marialuisa Lugaresi, Christof Mittermair, Gabriel Sandblom

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Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…To accomplish this goal, we have proposed an integrated system which encompasses the use of machine learning (ML) algorithms. This hybrid model is aimed at linking various activities to VAK model of learning and hence place students in their various class learning preferences derived from their activities and the patterns created during the learning processes. …”
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Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement by Pradnyawant M. Gote, Praveen Kumar, Hemant Kumar, Prateek Verma, Moses Makuei Jiet

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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

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MACHINE LEARNING MODELS FOR PERFORMANCE PREDICTION OF YOUNG PLAYERS IN FOOTBALL ACADEMIES – A REVIEW by Armand Florin Bertea, Cezar Honceriu

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On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models by Ma Shaofu, Anas Mahmood Al-Juboori, Asmaa Hussein Alwan, Abdel-Salam G. Abdel-Salam

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