Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“… BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA). …”
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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

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Teaching Quality Evaluation of Ideological and Political Courses in Colleges and Universities Based on Machine Learning by Lijun Qiao

“…This paper combines teaching evaluation theory and machine learning methods, analyzes the rationality of evaluation indicators through the acquired evaluation data, and optimizes the evaluation system. …”
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Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language by Mengya Cao

“…This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. …”
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Data-driven evaluation of background radiation safety using machine learning and statistical analysis by Muhammad Abid, Muhammad Shahid

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Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features by Xinzhu Liu, Rui Cang, Zihe Zhang, Ping Li, Hui Wu, Wei Liu, Shu Li

“…These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning (ML) has attracted substantial attention in the field of adolescent depression; however, studies establishing prediction models have primarily considered childhood or adolescent features separately, resulting in a lack of analyses that incorporate factors from both stages. …”
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Corrigendum to “Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications”

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Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

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Smell-ML: A Machine Learning Framework for Detecting Rarely Studied Code Smells by Esraa Hamouda, Abeer El-Korany, Soha Makady

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An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms by Anwer Mustafa Hilal, Maha M. Althobaiti, Taiseer Abdalla Elfadil Eisa, Rana Alabdan, Manar Ahmed Hamza, Abdelwahed Motwakel, Mesfer Al Duhayyim, Noha Negm

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The Application of Supervised Machine Learning Algorithms for Image Alignment in Multi-Channel Imaging Systems by Kyrylo Romanenko, Yevgen Oberemok, Ivan Syniavskyi, Natalia Bezugla, Pawel Komada, Mykhailo Bezuglyi

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Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study by Zainab Abdulelah Al Sudani, Golam Saleh Ahmed Salem

“…In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. …”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. by Ji-Ying Chen, Wu-Jie Chen, Zhi-Ying Zhu, Shi Xu, Li-Lan Huang, Wen-Qing Tan, Yong-Gang Zhang, Yan-Li Zhao

“…We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum from PCOS patients and non-PCOS subjects were collected for untargeted lipidomics analysis. …”
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Forecasting Stock Prices of Companies Producing Solar Panels Using Machine Learning Methods by Zaffar A. Shaikh, Andrey Kraikin, Alexey Mikhaylov, Gabor Pinter

“…The recurrent neural network LSTM is used in the article and this approach is based on complexity theory. Machine learning technologies are now being actively implemented in various sectors of the economy and are considered effective. …”
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Retracted: Modeling Social Network of Professional Sports Athletes Based on Machine Learning Algorithms by International Transactions on Electrical Energy Systems

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Correction: Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms. by PLOS One Staff

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Analyzing Rail Traffic Diversion Based on Machine Learning Technique considering Transportation Security by Jing Luo, Dai Zhou, Wenjun Ma, Guohua Zhao

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Decoding cortical folding patterns in marmosets using machine learning and large language model by Yue Wu, Xuesong Gao, Zhengliang Liu, Pengcheng Wang, Zihao Wu, Yiwei Li, Tuo Zhang, Tianming Liu, Tao Liu, Xiao Li

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Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction by Jian Li, Kewen Shao, Jia Du, Kaishan Song, Weilin Yu, Zhengwei Liang, Weijian Zhang, Jie Qin, Kaizeng Zhuo, Cangming Zhang, Yu Han, Yiwei Zhang, Bingrun Sui

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Machine Learning Method for Predicting the Merge and Morphology of Galaxies through Near-Infrared Spectroscopy by Samira Monfared, Neda Abdolvand, Mohammad Taghi Mirtorabi, Saeedeh Rajaee Harandi

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