Increment of Academic Performance Prediction of At-Risk Student by Dealing With Data Imbalance Problem by Nguyen Giap Cu, Thi Lich Nghiem, Thi Hoai Ngo, Manh Tuong Lam Nguyen, Hong Quan Phung

“…Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. …”
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Assessment of medical risks in airline passengers with recent acute and chronic coronary syndromes by I. N. Posokhov, E. A. Praskurnichiy

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Low-Cost, LiDAR-Based, Dynamic, Flood Risk Communication Viewer by Debra F. Laefer, Evan O’Keeffe, Kshitij Chandna, Kim Hertz, Jing Zhu, Raul Lejano, Anh Vu Vo, Michela Bertolotto, Ulrich Ofterdinger

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The future of AI in government services and global risks: insights from design fictions by Pedro Vitor Marques Nascimento, Paloma Beatriz Belchior de Siqueira, Nathalia Chrispim, Ramon Miranda Chaves, Carlos Eduardo Barbosa, Jano Moreira de Souza

“…The findings highlight three critical dilemmas: (1) AI’s dual role in enhancing efficiency while exacerbating algorithmic bias and surveillance concerns; (2) the potential displacement of human roles in public services, raising questions about accountability and transparency; and (3) the ethical trade-offs in AI-driven decision-making, particularly in law enforcement, healthcare, and education. …”
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Enhancing education quality with hybrid clustering and evolutionary neural networks in a multi phase framework by Saleem Malik, Chandrakanta Mahanty, Jnanaranjan Mohanty, Krunal Vaghela, T. Narmadha, R. Sivaranjani, Javed Khan Bhutto, Saiful Islam, Anwar Khan, Amanuel Zewdie

“…This algorithm’s strong feature selection and clustering help target educational interventions by revealing student learning behaviors. …”
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Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. by Md Merajul Islam, Nobab Md Shoukot Jahan Kibria, Sujit Kumar, Dulal Chandra Roy, Md Rezaul Karim

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”
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Development of a prognostic model for breast cancer patients based on intratumoral tumor-infiltrating lymphocytes using machine learning algorithms by Xinyi Wu, Chun Li

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Algorithmically Manufactured Minds: Generative and Agentic AI in a Time of Post-Truth, Reconfiguration of Student Agency and Death of Critical Pedagogy by Aras Bozkurt

“…This leads to a critical risk of “modal collapse”—an intellectual homogenization where student outputs converge on a narrow set of algorithmically-preferred styles and conclusions. …”
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Algorithms for the treatment of patients with chronic cerebral ischemia of stages I and II with use of Picamilon: results of an open clinical comparative study by Andrei B. Danilov, Natalia N. Shindryaeva, Irina V. Borodulina, Timothy D. Lunegov

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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…As the first statewide analysis of its kind, our spatial assessment provides a useful tool for resource allocation, risk factor reduction, and safety education efforts targeted to reduce the number of serious injuries or fatalities from structure fires.…”
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Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers by Hongqi Zhou, Weiyun Jin, Lindi Li, Xiangwen Nie, Weiwei Wu, Ran Chen, Qizhen Xie, Haixia Wu, Weiwei Jiang, Min Tang, Jinhai Wang, Maoyuan Wang

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Adherence to Clinical Quidelines on Preoperative Assessment and Correction of Cardiovascular Risk in Non-cardiac Surgery by M. D. Nigmatkulova, E. B. Kleymenova, L. P. Yashina, V. A. Otdelenov, S. A. Payushchik, O. D. Konova, D. A. Sychev

“…The number of inappropriate ECGs and echocardiographies, as well as incorrect treatment with beta-blockers, HMG CoA reductase inhibitors and ACE inhibitors (ARBs) in perioperative period evidence that the adherence of physicians to the clinical guidelines on preoperative assessment and perioperative management of patients remains low.It is reasonably to develop risk-based interdisciplinary protocols for preoperative examination, algorithms for interdisciplinary communication and interaction between specialists and the healthcare levels, as well as physicians' education for better adherence to clinical guidelines.…”
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Estimating the potential risks of developing clusters in medical industry using various ranking criteria by Y. G. Gertsik

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PROGNOSTIC VALUE OF DIFFERENT OBESITY MARKERS FOR CARDIOVASCULAR RISK ASSESSMENT IN URBAN EMPLOYED MONGOLIANS by K. V. Protasov, T. Myagmarsuren

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Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks by James C. L. Chow, Kay Li

“…These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. …”
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Semiparametric Transformation Models with a Change Point for Interval-Censored Failure Time Data by Junyao Ren, Shishun Zhao, Dianliang Deng, Tianshu You, Hui Huang

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Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa by Tirualem Zeleke Yehuala, Sara Beyene Mengesha, Nebebe Demis Baykemagn

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Repurposing FDA approved drugs for psoriasis indications through integrated molecular docking, one-SVM algorithm, and molecular dynamics simulation approaches by Saumya Choudhary, Noor Saba Khan, Pallavi Saxena, Parameswar Sahu, Dibyabhaba Pradhan, Harpreet Singh, George Thomas, Neeraj Kumar

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Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 by Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, Adem Tsegaw Zegeye

“…This study aimed to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 using machine learning algorithms. Data from Demographic and Health Surveys covering 147,466 men were analyzed. …”
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Evaluation of informativity risk factors of breast cancer in the female population of the Omsk region: Bayesian approach by N. G. Shirlina, D. V. Turchaninov, V. L. Stasenko, E. A. Vilms

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