Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience by Nicholas A. Coles, Bartosz Perz, Maciej Behnke, Johannes C. Eichstaedt, Soo Hyung Kim, Tu N. Vu, Chirag Raman, Julian Tejada, Van-Thong Huynh, Guangyi Zhang, Tanming Cui, Sharanyak Podder, Rushi Chavda, Shubham Pandey, Arpit Upadhyay, Jorge I. Padilla-Buritica, Carlos J. Barrera Causil, Linying Ji, Felix Dollack, Kiyoshi Kiyokawa, Huakun Liu, Monica Perusquia-Hernandez, Hideaki Uchiyama, Xin Wei, Houwei Cao, Ziqing Yang, Alessia Iancarelli, Kieran McVeigh, Yiyu Wang, Isabel M. Berwian, Jamie C. Chiu, Dan-Mircea Mirea, Erik C. Nook, Henna I. Vartiainen, Claire Whiting, Young Won Cho, Sy-Miin Chow, Zachary F. Fisher, Yanling Li, Xiaoyue Xiong, Yuqi Shen, Enzo Tagliazucchi, Leandro A. Bugnon, Raydonal Ospina, Nicolas M. Bruno, Tomas A. D'Amelio, Federico Zamberlan, Luis R. Mercado Diaz, Javier O. Pinzon-Arenas, Hugo F. Posada-Quintero, Maneesh Bilalpur, Saurabh Hinduja, Fernando Marmolejo-Ramos, Shaun Canavan, Liza Jivnani, Stanisław Saganowski

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Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning by Xiyu Cao, Jianli Ma, Xiaoyi He, Yufei Liu, Yang Yang, Yaqi Wang, Chuantao Zhang

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A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

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DDNet: A Robust, and Reliable Hybrid Machine Learning Model for Effective Detection of Depression Among University Students by Nasirul Mumenin, Mohammad Abu Yousuf, Madini O. Alassafi, Muhammad Mostafa Monowar, Md. Abdul Hamid

“…The hyperparameter of the used models has been optimized using random search. The optimal configuration has been determined through extensive experimentation with various machine learning (ML) models and settings to ensure high performance. …”
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Predicting Equatorial Spread F at JICAMARCA Sector Via Supervised Machine Learning by Shunzu Gao, Chao Xiong, Hongtao Cai, Qian Pan, Weijia Zhan, Hong Zhang, Yuhao Zheng

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Hybrid FRP strengthening of reinforced concrete deep beams: Experimental, theoretical and machine learning-based study by Phromphat Thansirichaisree, Qudeer Hussain, Mingliang Zhou, Ali Ejaz, Shabbir Ali Talpur, Panumas Saingam

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Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model by Ahsan Zafar, Yanbo Che, Muneer Ahmed, Muhammad Sarfraz, Ashfaq Ahmad, Mohammad Alibakhshikenari

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Accurate multi-category student performance forecasting at early stages of online education using neural networks by Naveed Ur Rehman Junejo, Muhammad Wasim Nawaz, Qingsheng Huang, Xiaoqing Dong, Chang Wang, Gengzhong Zheng

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Implementing the Hybrid Neuro-Fuzzy System to Model Specific Learning Disability in Special University Education Programs by Lin Wending

“…The computerized model is proposed based on a combination of fuzzy logic and machine learning utilizing association rules and cellular automata. …”
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A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems by Achin Jain, Arun Kumar Dubey, Shakir Khan, Arvind Panwar, Mohammad Alkhatib, Abdulaziz M Alshahrani

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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

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Analyzing Fairness of Computer Vision and Natural Language Processing Models by Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

“…Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. …”
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Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training by Nikhil Gupta, Kavin Khatri, Yogender Malik, Amit Lakhani, Abhinav Kanwal, Sameer Aggarwal, Anshul Dahuja

“…Abstract Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. …”
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Accuracy of LLMs in medical education: evidence from a concordance test with medical teacher by Vinaytosh Mishra, Yotam Lurie, Shlomo Mark

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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

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Analysis and research on the integration of education, technology and industry in smart education by Shao Haiyan, Lv Jie, Sun Xuan, Wang Na, Wang Peng, Zhu Dongqing, Xue Hao, Chen Bin, Ma Yuzhen

“…The focus of this manuscript is on pioneering integration strategies through case-based teaching, the joint educational framework of industry-academic convergence, the development of a multifaceted assessment approach, and the new ‘teacher-machine-student’ teaching model. …”
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Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings by Bruna Rocha, Álvaro Figueira

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Compressive strength of nano concrete materials under elevated temperatures using machine learning by Abdullah M. Zeyad, Alaa A. Mahmoud, Alaa A. El-Sayed, Ayman M. Aboraya, Islam N. Fathy, Nikos Zygouris, Panagiotis G. Asteris, Ibrahim Saad Agwa

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Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu, Nitesh Dhar Badgayan

“…To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. …”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

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