Decoding survival in MASLD: the dominant role of metabolic factors by Zhiqiang Jin, Cheng Zeng, Yang Yang, Shan Zhong, Zhi Zhou

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Fatigue strength prediction of Cobalt alloys using material composition and monotonic properties: ML-based approach by Subraya Krishna Bhat, Amritanshu Ranjan, Y S Upadhyaya, Vishwanath Managuli

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Race course characteristics are the most important predictors in 48 h ultramarathon running by Beat Knechtle, David Valero, Elias Villiger, Katja Weiss, Pantelis T. Nikolaidis, Lorin Braschler, Rodrigo Luiz Vancini, Marilia Santos Andrade, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

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Determining Factors Affecting Cooperative Membership of the Beekeepers Using Decision Tree Algorithms by Tayfun Çukur, Figen Çukur

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Genotyping TOMM40′523 poly-T polymorphisms using whole-genome sequencing by Ricardo A. Vialle, Lei Yu, Yan Li, Roberto T. Raittz, Jose M. Farfel, Philip L. De Jager, Julie A. Schneider, Lisa L. Barnes, Shinya Tasaki, David A. Bennett

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Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments by M. Karthikeyan, R. Brindha, Maria Manuel Vianny, V. Vaitheeshwaran, Mrinal Bachute, Sanket Mishra, Bibhuti Bhusan Dash

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Advanced intrusion detection in internet of things using graph attention networks by Aamir S. Ahanger, Sajad M. Khan, Faheem Masoodi, Ayodeji Olalekan Salau

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AI-driven competency recommendations based on attendance patterns and academic performance by Junaidi, Teguh Wahyono, Irwan Sembiring

“…Gradient Boosting (GB) was the most effective model for weighting discipline and learning outcomes (Mean Squared Error [MSE]: 2.9224, Root Mean Squared Error [RMSE]: 1.4252, Coefficient of Determination [R2]: 0.9667), outperforming three alternatives. …”
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Integrating Multi-Variable Driving Factors to Improve Land Use & Land Cover Classification Accuracy using Machine Learning Approaches: A Case Study from Lombok Island by Miftahul Irsyadi Purnama, Hüseyin Oğuz Çoban

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Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics by Shuquan Su, Zhongran Ni, Tian Lan, Pengyao Ping, Jinling Tang, Zuguo Yu, Gyorgy Hutvagner, Jinyan Li

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Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

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Digits Recognition for Arabic Handwritten through Convolutional Neural Networks, Local Binary Patterns, and Histogram of Oriented Gradients by Bushra Mahdi Hasan, Zahraa Jasim Jaber, Ahmad Adel Habeeb

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Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing by Nuphar Avital, Idan Egel, Ido Weinstock, Dror Malka

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A metabolite‐based machine learning approach to diagnose Alzheimer‐type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker d... by Daniel Stamate, Min Kim, Petroula Proitsi, Sarah Westwood, Alison Baird, Alejo Nevado‐Holgado, Abdul Hye, Isabelle Bos, Stephanie J.B. Vos, Rik Vandenberghe, Charlotte E. Teunissen, Mara Ten Kate, Philip Scheltens, Silvy Gabel, Karen Meersmans, Olivier Blin, Jill Richardson, Ellen De Roeck, Sebastiaan Engelborghs, Kristel Sleegers, Régis Bordet, Lorena Ramit, Petronella Kettunen, Magda Tsolaki, Frans Verhey, Daniel Alcolea, Alberto Lléo, Gwendoline Peyratout, Mikel Tainta, Peter Johannsen, Yvonne Freund‐Levi, Lutz Frölich, Valerija Dobricic, Giovanni B. Frisoni, José L. Molinuevo, Anders Wallin, Julius Popp, Pablo Martinez‐Lage, Lars Bertram, Kaj Blennow, Henrik Zetterberg, Johannes Streffer, Pieter J. Visser, Simon Lovestone, Cristina Legido‐Quigley

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Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study by Ningkun Xiao, Ningkun Xiao, Xinlin Huang, Yujun Wu, Baoheng Li, Wanli Zang, Khyber Shinwari, Khyber Shinwari, Irina A. Tuzankina, Valery A. Chereshnev, Valery A. Chereshnev, Guojun Liu

“…AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. …”
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Dynamic AI-Enhanced Therapeutic Framework for Precision Medicine Using Multi-Modal Data and Patient-Centric Reinforcement Learning by R. Gayathri, S. K. B. Sangeetha, R. Sangeetha, G. Leena Rosalind Mary, Sandeep Kumar Mathivanan, Usha Moorthy

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Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and... by Mequannent Sharew Melaku, Lamrot Yohannes, Eliyas Addisu Taye, Nebebe Demis Baykemagn

“…Python 3.12 software was used for machine learning-based predictions of khat chewing among men. …”
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HSNMF enables accurate and effective analysis for the college students' psychological health education data and student life data by Yuanyuan Ma, Yuanyuan Ma, Lifang Liu

“…The results showed that the low-dimensional latent variables learned from HSNMF contained rich semantic information, further improving the performance of traditional machine learning models. …”
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Glycosylation profiling of triple-negative breast cancer: clinical and immune correlations and identification of LMAN1L as a biomarker and therapeutic target by Qianru Yu, Hanyi Zhong, Xinhao Zhu, Chang Liu, Xin Zhang, Jiao Wang, Zongyao Li, Songchang Shi, Haoran Zhao, Cixiang Zhou, Qian Zhao

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Evaluating Handwritten Answers Using DeepSeek: A Comparative Analysis of Deep Learning-Based Assessment by Sanskar Bansal, Vinay Gupta, Eshita Gupta, Peeyush Garg

“…Abstract Artificial intelligence is revolutionizing the education sector by making learning more accessible, efficient, and customized. …”
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