Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions by Mohamed Rebbouj, Said Lotfi

“…Background and purpose The aim of this study is to incorporte machine learning techniques in physical education activities assessment so we can plan a training session and learning cycle based on predictive analyses using machine learning algorithms. …”
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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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Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm by Xiao Zhou, Ling Guo, Rui Li, Ling Liu, Juan Pan

“…Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved <i>k</i>-NN data mining algorithm. …”
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Interpretable Ensemble Learning Approach for Predicting Student Adaptability in Online Education Environments by Shakib Sadat Shanto, Akinul Islam Jony

“…Various machine learning (ML) and deep learning (DL) models, including decision tree (DT), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), XGBoost, and artificial neural networks (ANNs), are applied for adaptability prediction. …”
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Learner Support Systems in Distance Learning Education Systems (Comparative Analysis of VU and AIOU) by Ahmad Attiq-ul-Rehman, Amina Shahzadi

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Research on learning achievement classification based on machine learning. by Jianwei Dong, Ruishuang Sun, Zhipeng Yan, Meilun Shi, Xinyu Bi

“…In order to improve the accuracy and robustness of student performance classification, we adopted Gaussian Distribution based Data Augmentation technique (GDO), combined with multiple Deep Learning (DL) and Machine Learning (ML) models. We explored the application of different Machine Learning and Deep Learning models in classifying student grades. …”
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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

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CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms by Milad Nouri, Shadman Veysi

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Predicting student retention: A comparative study of machine learning approach utilizing sociodemographic and academic factors by Reymark D. Deleña, Norniña J. Dia, Redeemtor R. Sacayan, Joseph C. Sieras, Suhaina A. Khalid, Amer Hussien T. Macatotong, Sacaria B. Gulam

“…The study offers a scalable and transferable modeling framework for higher education institutions seeking to implement early warning systems. …”
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A Machine-Learning Model for the Prediction of Triple-Negative Breast Cancer Based on Multiparameter MRI by Cai Y, Li Y, Wang W, Zhou Y, Wang J, Zhang L, Lu H

“…In the primary cohort, univariate analysis, logistic regression analysis and Boruta algorithm were used to determine the independent predictors for TNBC and non-TNBC. The machine learning classifier XGboost was developed based on the features to predict TNBC. …”
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A Hybrid Machine Learning and Optimization Model to Minimize the Total Cost of BRT Brake Components by Saeed Najafi-Zangeneh, Naser Shams Gharneh, Ali Arjomandi-Nezhad, Erfan Hassannayebi

“…This study aims to implement a hybrid machine learning and optimization model to minimize the total investment and reliability-related costs in a bus rapid transit (BRT) system. …”
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Developing a Fatigue Detection Model for Hospital Nurses Using HRV Measures and Machine Learning by Wynona Salsabila Hafiz, Maya Arlini Puspasari, Dewi Yunia Fitriani, Richard Joseph Hanowski, Danu Hadi Syaifullah, Salsabila Annisa Arista

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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. …”
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Assessing safety in horizontal curves using surrogate safety measures and machine learning by Navid Nadimi, Hojjat Fathipour, Abbas Sheykhfard

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Power management in isolated microgrids using machine learning-based robust model predictive control by Chou-Yi Hsu, Amit Ved, Hannah Jessie Rani R, Zayd Ajsan Balsem, Nora Rashid Najem, Abhayveer Singh, P․Sasi Kiran, Ankita Aggarwal, Satish Kumar Samal, Alireza Kamranfar

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Early prediction of cognitive impairment in adults aged 20 years and older using machine learning and biomarkers of heavy metal exposure by Ali Nabavi, Farimah Safari, Mohammad Kashkooli, Sara Sadat Nabavizadeh, Hossein Molavi Vardanjani

“…Objective: To develop and validate machine learning models for early prediction of cognitive impairment risk using demographics, clinical factors, and biomarkers of heavy metal exposure. …”
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How accurately can supervised machine learning model predict a targeted psychiatric disorder? by Haitham Jahrami, Amir H. Pakpour, Waqar Husain, Achraf Ammar, Zahra Saif, Ali Husain Alsalman, Adel Aloffi, Khaled Trabelsi, Seithikurippu R. Pandi-Perumal, Michael V. Vitiello

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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

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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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