Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis by Yuhan Zhang, Jue Guo, Na Yang, Xiangyun Li, Yuxiang Liu, Meiqin Yan, Peng Shen

“…The study findings were reported in accordance with the TRIPOD+AI guidelines.Results The RF model achieved the relatively optimal and stable performance, with an area under the curve of 0.855, accuracy of 0.773, F1 score of 0.769, and Brier score of 0.155 in test set. …”
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Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems by Abubakar Unguwanrimi Yakubu, Liu Qingsheng, Meng Kai, Chen Jinwei, Omer Abbaker Ahmed Mohammed, Jiahao Zhao, Qi Jiang, Xuanhong Ye, Junyi Liu, Qinglong Yu, Muhammad Aurangzeb, Shusheng Xiong

“…The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. …”
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Diagnosing schizophrenia with routine blood tests: a comparative analysis of machine learning algorithms by Yavuz Selim Ogur, Ayse Erdogan Kaya, Nur Banu Ogur, Beyza Erdogan Akturk

“…Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR) models were trained and evaluated via stratified 10-fold cross-validation.ResultsGroups were homogeneous in terms of age and sex. …”
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Current state and prospects of development of energy-optimal control systems for 2ES6 electric locomotives by S. G. Istomin, K. I. Domanov, A. P. SHATOKHIN, I. N. Denisov

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Optimizing Space Heating in Buildings: A Deep Learning Approach for Energy Efficiency by Fernando Almeida, Mauro Castelli, Nadine Corte-Real, Luca Manzoni

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Introducing MLOps to Facilitate the Development of Machine Learning Models in Agronomy: A Case Study by Dario Ruggeri, Gabriele Tazza, Laszlo Vidacs

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Surrogate-assisted optimization of roll-to-roll slot die coating by Christopher Passmore, Kai E. Wu, Jonathan R. Howse, George Panoutsos, Stephen J. Ebbens

“…Due to the lack of accurate first-principle models, machine learning offers a promising approach. …”
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Intelligent Virtual Machine Scheduling Based on CPU Temperature-Involved Server Load Model by Huan Zhou, Jiebei Zhu, Binbin Chen, Lujie Yu, Heyu Luo

“…However, existing server power load (SPL) models typically adopt linear approximations for model developments, which results in inaccuracy with actual SPL characteristics, hindering the optimal solution of virtual machine scheduling. …”
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Machine learning-based multiphysics model for corrosion fatigue crack propagation in aluminum alloy by Hongkun Wang, Zhenshuang Wu, Dongmei Jiang, Haitao Wang, En-Hou Han

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Forecasting formation density from well logging data based on machine learning model by Xiankang Cheng, Haoyu Zhang, Haoyu Zhang

“…Although various prediction models have been developed using density inversion, the Terzaghi correction, and machine learning techniques, these models are difficult to meet the high-precision requirements during the calculation process. …”
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An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization by Warda M. Shaban

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Dual possibilistic regression models of support vector machines and application in power load forecasting by Xianfei Yang, Xiang Yu, Hui Lu

“…In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. …”
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Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence by Abdulaziz AlMohimeed

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Predictive Modeling of Compressive Strength and Slump in High-Performance Concrete Utilizing Machine Learning by Yu Yang, Ye Chen, Peng Zhang, Weining Zhang

“…The research estimates the compressive strength and slump of the HPC by advanced machine learning regression frameworks such as ADAR, SVR, and three optimizers: GOA and CBOA. …”
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Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério, Luís F. F. M. Santos

“…Metrics such as precision, recall, F1-score and accuracy are used to assess the degree of correctness of the predictive models. The adjustment of hyperparameters is obtained with Grid Search and Bayesian Optimization. …”
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The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights by Majdi Maabreh, Arwa Maabreh, Basheer Qolomany, Ala Al-Fuqaha

“…Data set poisoning is a severe problem that may lead to the corruption of machine learning models. The attacker injects data into the data set that are faulty or mislabeled by flipping the actual labels into the incorrect ones. …”
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Enhancing Power Allocation in DAS: A Hybrid Machine Learning and Reinforcement Learning Model by S. Gnanasekar, K. C. Sriharipriya

“…The proposed method combines Machine Learning (ML) for predictive modeling with Multi-Agent Reinforcement Learning (MARL) for real-time coordination among RAUs. …”
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An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection by Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei

“…We analyze each model’s optimization framework, decision boundaries, and feature importance mechanisms, highlighting the empirical tradeoffs between representational capacity, generalization, and interpretability. …”
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Predicting congenital syphilis cases: A performance evaluation of different machine learning models. by Igor Vitor Teixeira, Morgana Thalita da Silva Leite, Flávio Leandro de Morais Melo, Élisson da Silva Rocha, Sara Sadok, Ana Sofia Pessoa da Costa Carrarine, Marília Santana, Cristina Pinheiro Rodrigues, Ana Maria de Lima Oliveira, Keduly Vieira Gadelha, Cleber Matos de Morais, Judith Kelner, Patricia Takako Endo

“…Based on a rigorous methodology, we propose six experiments using three feature selection techniques to select the most relevant attributes, pre-process and clean the data, apply hyperparameter optimization to tune the machine learning models, and train and test models to have a fair evaluation and discussion.…”
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Application of Support Vector Machines in High Power Device Technology by RAO Wei, LI Yong, YAN Ji

“…It presented a support vector machines regression model (SVR) with Gauss kernel function (RBF). …”
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