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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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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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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Methods and reliability study of moral education assessment in universities: A machine learning-based approach by Ting Jin

“…The study demonstrates how machine learning efficiently assesses student moral education performance by leveraging PCA to identify patterns and using ML models to make accurate predictions. …”
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A model for predicting academic performance on standardised tests for lagging regions based on machine learning and Shapley additive explanations by Mario Suaza-Medina, Rita Peñabaena-Niebles, Maria Jubiz-Diaz

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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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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. …”
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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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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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Analyzing click data with AI: implications for student performance prediction and learning assessment by Mahdi-Reza Borna, Hanan Saadat, Aref Tavassoli Hojjati, Elham Akbari

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Research Training and Machine Learning: from Matching to Convergence by Осипенко Л.Е., Козицына Ю.В., Коротков А.В.

“…Existing applied solutions with the use of machine learning in pedagogy are analyzed, ways of its extrapolation to the convergence model of research training and machine learning are shown. …”
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Practical aspects of using СhatGPT in higher education by A. A. Paskova

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The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

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Exploring Metaverse Technologies in Entrepreneurship Through Machine Learning-Based Topic Modeling by Krishnashree Achuthan, Sasangan Ramanathan, Raghu Raman

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Assessing an Innovative Virtual Museum Application using Technology Acceptance Model by Shinta Puspasari, Ermatita, Zulkardi

“…This study discusses the assessment of a virtual museum application development based on machine learning models for Palembang culture education through the Sultan Mahmud Badaruddin II museum cultural heritage by using the Technology Acceptance Model approach. …”
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Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses by Fahimeh Ahmadi, Mohsen Hajihassani, Tryfon Sivenas, Stefanos Papanikolaou, Panagiotis G. Asteris

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Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 by Morteza Abbasi, Amir Hossein Namadchi, Mehdi Abbasi, Mohsen Abbasi

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