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

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A Generic Modeling Method of Multi-Modal/Multi-Layer Digital Twins for the Remote Monitoring and Intelligent Maintenance of Industrial Equipment by Maolin Yang, Yifan Cao, Siwei Shangguan, Xin Chen, Pingyu Jiang

“…For example, (1) existing DT modeling methods are usually focused on specific types of equipment rather than being generally applicable to different types of equipment and requirements. (2) Existing DT models usually emphasize working condition monitoring and have relatively limited capability for modeling the operation and maintenance mechanism of the equipment for further decision making. (3) How to integrate artificial intelligence algorithms into DT models still requires further exploration. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…The discussion is organized into three distinct sections, each focusing on the critical roles of AI and machine learning (ML) in FSW. …”
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 Who Benefits from AI? Examining Different Demographics' Fairness Perceptions across Personal, Work, and Public Life by Sejin Paik, Ekaterina Novozhilova, Kate K. Mays, James E. Katz

“…Women consistently perceive AI as less beneficial and more harmful across all contexts compared to men, reflecting broader gendered concerns about algorithmic decision-making Ethnicity differences also emerge, with Hispanic and Other minority groups (including Asian, Indigenous, and Native American individuals) reporting higher perceived benefits from AI. …”
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AI-Based Damage Risk Prediction Model Development Using Urban Heat Transport Pipeline Attribute Information by Sungyeol Lee, Jaemo Kang, Jinyoung Kim, Myeongsik Kong

“…These factors were applied to three machine learning algorithms: Random Forest, eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). …”
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Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal st... by Gege Zhang, Sijie Dong, Li Wang

“…LASSO regression was used to screen for risk factors, and three machine learning algorithms—logistic regression (LR), random forest (RF), and XGBoost—were employed to build predictive models. …”
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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

“…In this study, we systematically evaluate the mathematical foundations and empirical performance of five representative models for automated fake news classification: three classical machine learning algorithms (Logistic Regression, Random Forest, and Light Gradient Boosting Machine) and two state-of-the-art deep learning architectures (A Lite Bidirectional Encoder Representations from Transformers—ALBERT and Gated Recurrent Units—GRUs). …”
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Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. …”
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Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions. by Yiye Zhang, Yufang Huang, Anthony Rosen, Lynn G Jiang, Matthew McCarty, Arindam RoyChoudhury, Jin Ho Han, Adam Wright, Jessica S Ancker, Peter Ad Steel

“…Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. …”
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Navigating ethical minefields: a multi-stakeholder approach to assessing interconnected risks in generative AI using grey DEMATEL by Sridhar Jonnala, Sridhar Jonnala, Nisha Mary Thomas, Nisha Mary Thomas, Sarthak Mishra

“…Through a comprehensive literature review and expert validation across three key stakeholder groups (AI developers, end users, and policymakers), we identified and analyzed 14 critical ethical challenges across the input, training, and output modules, including both traditional and emerging risks, such as deepfakes, intellectual property rights, data transparency, and algorithmic bias. …”
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Estimating Self-Confidence in Video-Based Learning Using Eye-Tracking and Deep Neural Networks by Ankur Bhatt, Ko Watanabe, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

“…To assess the collected data, we compare three different algorithms: a Long Short-Term Memory (LSTM), a Support Vector Machine (SVM), and a Random Forest (RF), thereby providing a comprehensive evaluation of the data. …”
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A comprehensive survey on machine learning for workplace injury analysis: risk prediction, return to work strategies, and demographic insights by Gonzalo A. Vivian, Richard A. Bauder, Taghi M. Khoshgoftaar

“…Abstract This survey paper explores the application of machine learning (ML) techniques in the domain of workplace injuries, focusing on three key areas: risk prediction, return to work (RTW) strategies, and demographic analysis. …”
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Models of user experience quality in computer information systems by П. Р. Пелех, В. М. Юзевич

“…Unlike traditional Quality of Service (QoS) or subjective Quality of Experience (QoE) models alone, EPQuE integrates technical and perceptual factors through an adaptive algorithmic pipeline. The model incorporates three synergistic components: a Fuzzy Inference System (FIS) to interpret imprecise or ambiguous user feedback; Multi-Criteria Decision Analysis (MCDA), used to normalize and prioritize heterogeneous parameters in real time; a regression-based machine learning model for dynamic adjustment of input weights based on contextual variations. …”
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Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models. by Al Mahmud Siam, Pankaj Bhowmik, Md Palash Uddin

“…Our framework integrates three complementary feature selection techniques: Pearson correlation, information gain (IG), and random forest importance (RFI), each optimized for the dataset's characteristics. …”
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Development of a Smart Waste Management System for Route Optimization and Adaptive Demand Management in Dubai by H. Hassan, T. Ali, A. Tamimi

“…The system has four major components: (1) a mobile field application to add and modify collection points in real time; (2) a route optimization module that minimizes travel distance and CO₂ emissions while accounting for real-world constraints; (3) an interactive dashboard for decision-makers to monitor analytics, visualize routes, and make real-time adjustments; and (4) a navigator app for truck drivers to follow optimized routes seamlessly. …”
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Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation by Feng Wei, Shuyu Chen

“…Unlike individual recommendation, group recommendation must consider both individual preferences and group dynamics, thereby enhancing decision-making efficiency for groups. One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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Research Progress on Non-destructive Detection Techniques for Potatoes Quality by YANG Guang-hui, LI Hong-ling, ZHANG Hua, LI Hui, LIU Xiao-long, JIA Shang-yun, HU Bo, SHANG Yi-bo

“…Since the “14th Five-Year Plan”, with the continuous deepening of the “three rural work”, the quality inspection and grading of potatoes have played a decisive role in the expansion of the potato industry and the development of the processing industry. …”
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A Two-Stage Robust Optimization Strategy for Long-Term Energy Storage and Cascaded Utilization of Cold and Heat Energy in Peer-to-Peer Electricity Energy Trading by Yun Chen, Yunhao Zhao, Xinghao Zhang, Ying Wang, Rongyao Mi, Junxiao Song, Zhiguo Hao, Chuanbo Xu

“…The Nested Column and Constraint Generation (NC&CG) algorithm enhances privacy and solution precision. …”
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