Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women by Minkyung Je, Seunghyeon Hwang, Suwon Lee, Yoona Kim

“…Abstract Background The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale population cohort study. …”
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A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation by Pablo Santiago López Freire, Jocelyn Estefanía Morocho Hidalgo, Leslye Pamela Calderón, Andy Stiwer Jhostin Quiroz

“…This study proposes the hybrid NEAML-BIOPASTAZA (Neutrosophic and Explainable Artificial Learning) model for Biodiversity and Legal-Ecological Assessment in Pastaza, which integrates multivariate statistical analysis, neutrosophic logic, and supervised machine learning to assess the relationship between environmental literacy and the effectiveness of the legal framework for biodiversity conservation in the Pastaza canton. …”
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Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era by Gabriel James, Anietie Ekong, Aloysius Akpanobong, Enefiok Etuk, Saviour Inyang, Samuel Oyong, Ifeoma Ohaeri, Chikodili Orazulume, Peace Okafor

“…This work contributes a novel machine learning framework for technostress assessment, providing a practical tool for organizations and policymakers to better understand and mitigate technology-induced stress. …”
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A comparative study of explainable machine learning models with Shapley values for diabetes prediction by Keona Pang

“…Over the years, numerous machine learning models have been developed, leading to successful applications across various fields. …”
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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

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Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…The top five contributing variables were ‘family issues and additional barriers’, ‘restriction of hours’, ‘available CV’, ‘self-employment considered’ and ‘education’. The Harrell’s Concordance index was range from 0.60 (Cox model) to 0.71 (Random Survival Forest) suggesting a better fit for the machine learning approaches. …”
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Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis by Xiao Chen, Zhiyun Shen, Tingyu Guan, Yuchen Tao, Yichen Kang, Yuxia Zhang

“…We applied a supervised machine learning approach including human annotation and machine learning–based models for topic modeling and sentiment analysis of the public discourse. …”
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Feature importance analysis of solar flares and prediction research with ensemble machine learning models by Yun Yang, Yun Yang

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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

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Health literacy prediction models based on machine learning methods: a scoping review by PAN Xiang, TONG Yingge, LI Yixuan, NI Ke, CHENG Wenqian, XIN Mengyu, HU Yuying

“…Modeling methods included regression models, tree-based machine learning methods, support vector machines and neural network models. …”
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Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data by Wenli Chen, Zirou Lin, Lishan Zheng, Mei-Yee Mavis Ho, Farhan Ali, Wei Peng Teo

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Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness by Aazad Abbas, Jay Toor, Denis Ariel Margalik, Jin Tong Du, Anne Versteeg, Nicholas J Yee, Joel A Finkelstein, Jihad Abouali, Markku T Nousiainen, Hans J Kreder, Jeremy Hall, Cari Whyne, Jeremie Larouche

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Enhancing Student Management Through Hybrid Machine Learning and Rough Set Models: A Framework for Positive Learning Environments by Ateeq Ur Rehman Butt, Hamid Ali, Muhammad Asif, Hessa Alfraihi, Mohamad Khairi Ishak, Khalid Ammar

“…Effective student management is crucial for fostering productive learning environments. This study presents a hybrid framework integrating machine learning (ML) techniques with rough set theory to enhance student management by identifying at-risk students and enabling personalized interventions. …”
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Assessment of flood vulnerability in a coastal metropolitan city for sustainable environmental using machine learning methods by Rana Alabdan, C. Sharmila, Nuha Alruwais, Haya Mesfer Alshahrani, S. Anbukkarasi, M. Sujatha, S. Vivek

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Intelligent System for Student Performance Prediction Using Machine Learning by Mustafa S. Ibrahim Alsumaidaie, Ahmed Adil Nafea, Abdulrahman Abbas Mukhlif, Ruqaiya D. Jalal, Mohammed M AL-Ani

“…This study aims to develop an intelligent solution for predicting student performance using supervised machine learning algorithms. This proposed focus on addressing the limitations of existing prediction models and enhancing prediction accuracy. …”
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Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms by Bo Liu, Haijia Wen, Mingrui Di, Junhao Huang, Mingyong Liao, Jingyuan Yu, Yutao Xiang

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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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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

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Machine learning approaches for forecasting compressive strength of high-strength concrete by Mohammed Shaaban, Mohamed Amin, S. Selim, Islam M. Riad

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