Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine by Yanhua Zhao, Kai Zhang, Aojun Guo, Fukang Hao, Jie Ma

“…To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. …”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…However, there are currently no effective methods of predicting who is likely to experience suicidal thoughts and behavior. Machine learning (ML) approaches can be leveraged to develop models that evaluate the complex etiology of suicidal behavior, facilitating the timely identification of at-risk individuals and promoting individualized treatment allocation.Materials and methodsThis retrospective case-control study used longitudinal sociodemographic, psychosocial, and clinical data of 1,126 PLWH from Uganda to evaluate the potential of ML in predicting suicidality. …”
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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Abstract Objective To investigate machine learning‐based regression models to predict the postoperative apnea‐hypopnea index (AHI) for evaluating the outcome of velopharyngeal surgery in adult obstructive sleep apnea (OSA) subjects. …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…This study aimed to develop a machine learning (ML) model to predict the risk of ARDS in patients with sepsis using circulating immune cell parameters and other physiological data. …”
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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

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Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning by Nayomi Fernando, Lasantha Seneviratne, Nisal Weerasinghe, Namal Rathnayake, Yukinobu Hoshino

“…Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. …”
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Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. …”
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Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

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Machine Learning Innovations for Improving Mineral Recovery and Processing: A Comprehensive Review* by Korie, Josephmartin Izuchukwu*, Chudi-Ajabor, Ogochukwu Gabriela, Ezeonyema, Chukwudalu Chukwuekezie, Oshim, Francisca Ogechukwu

“…To overcome the limitations of traditional mineral processing and recovery methods, cutting-edge technologies, including Machine learning (ML), emerge as a paradigm shift in this sector, offering predictive insights, data analysis, and real-time monitoring capabilities. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…This study is focused on developing a machine learning (ML) model that is clinically acceptable for accurately detecting vitamin D status and eliminates the need for 25-OH-D determination while addressing overfitting. …”
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Assessing cyber risks in construction projects: A machine learning-centric approach by Dongchi Yao, Borja García de Soto

“…This study develops a Machine Learning (ML)-centric approach to assess common cyber risks for construction projects. …”
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Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning by Xudong Zhao, Qingfen Ma, Jingru Li, Zhongye Wu, Hui Lu, Yang Xiong

“…To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. …”
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Prediction and Optimization of Civil Aviation Flight Delays Based on Machine Learning Algorithms by Qingwei Zhong, Yingxue Yu, Yiru Huang, Tianhang Zhang

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Ecological risks of PFAS in China’s surface water: A machine learning approach by Xinmiao Huang, Huijuan Wang, Xiaoyong Song, Zilin Han, Yilan Shu, Jiaheng Wu, Xiaohui Luo, Xiaowei Zheng, Zhengqiu Fan

“…This study investigated the ecological risks of PFAS in surface water in China under different Shared Socioeconomic Pathways (SSPs) using machine learning modeling, based on concentration data collected from 167 published papers. …”
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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

“…Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm‐extreme learning machine (PSO‐ELM). …”
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A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning by Y. Chen, W. Li, Y. Luo, L. Ji, S. Li, Y. Long

“…To reduce development time and costs, this paper proposes a rapid impeller design method focused on hydraulic performance, integrating traditional similarity design theory with machine learning. The proposed model uses neural networks to predict empirical coefficients, determine key dimensions such as the impeller’s inlet diameter, outlet diameter, outlet width, and axial distance. …”
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Projecting Future Wetland Dynamics Under Climate Change and Land Use Pressure: A Machine Learning Approach Using Remote Sensing and Markov Chain Modeling by Penghao Ji, Rong Su, Guodong Wu, Lei Xue, Zhijie Zhang, Haitao Fang, Runhong Gao, Wanchang Zhang, Donghui Zhang

“…This study employs high-resolution projections from NASA’s Global Daily Downscaled Projections (GDDP) and the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), combined with a machine learning and Cellular Automata–Markov (CA–Markov) framework to forecast the land cover transitions to 2040. …”
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A highly generalized federated learning algorithm for brain tumor segmentation by Jun Wen, Xiusheng Li, Xin Ye, Xiaoli Li, Hang Mao

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Hybrid high-performance computing enhanced machine learning framework for nano-thermal conductivity in MWNT-oil-based solar cooking systems by Nischal P. Mungle, Sumit Kumar, Dnyaneshwar M. Mate, Sham H. Mankar, Tejas R. Patil, Hirkani Padwad, Niteen T. Kakade, Nilesh Shelke, Haytham F. Isleem, Vikrant S. Vairagade

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Data-driven insights of flow over heated elliptic cylinders: Machine learning and CFD perspectives on non-Newtonian forced convection by Anika Tahsin Meem, Md. Zhangir Hossain, Hasina Akter, Md. Mamun Molla

“…To alleviate the computational cost of high-fidelity CFD simulations, surrogate machine learning (ML) models — Random Forest, XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) – are trained to predict CD, CL, and q′′¯. …”
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