Prediction of the Non-Reducing Biomineralization of Nuclide–Microbial Interactions by Machine Learning: The Case of Uranium and <i>Bacillus subtilis</i> by Shirong Qiang, Leijin Liu, Siqi Li, Shuang Wang, Xinyang Huang, Jiaxin Yang, Jiayu Song, Yue Zhang, Yongxiang Huang, Qiaohui Fan

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Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics by Nam Trung Tran

“…This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. …”
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Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN by Md. Al Mehedi Hasan, Fuad Al Abir, Md. Al Siam, Jungpil Shin

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An Empirically Validated Framework for Automated and Personalized Residential Energy-Management Integrating Large Language Models and the Internet of Energy by Vinícius Pereira Gonçalves, Andre Luiz Marques Serrano, Gabriel Arquelau Pimenta Rodrigues, Matheus Noschang de Oliveira, Rodolfo Ipolito Meneguette, Guilherme Dantas Bispo, Maria Gabriela Mendonça Peixoto, Geraldo Pereira Rocha Filho

“…The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. …”
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Research on Fault Diagnosis Method of High-Speed EMU Air Compressor Based on ICEEMDAN and Wavelet Threshold Combined Noise Reduction by Liqiang Peng, Akang Guo, Shuzhao Zhang

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A Hybrid Machine Learning-Based Framework for Data Injection Attack Detection in Smart Grids Using PCA and Stacked Autoencoders by Shahid Tufail, Hasan Iqbal, Mohd Tariq, Arif I. Sarwat

“…The generated classes address the imbalances in the data to enhance the generalizability of the model and address diverse attack scenarios. Various machine learning algorithms were evaluated, and the Random Forest (RF) model consistently achieved superior accuracy, ranging from 99.32% to 95.89%. …”
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A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM by ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun

“…To validate the proposed method, it is compared with other machine learning models under the same data segmentation condition for daily and hourly data sets. …”
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Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada by Dania Tamayo-Vera, Kai Liu, Antonio Bolufé-Röhler, Xiuquan Wang

“…This research highlights the efficacy of integrating these temporal dependencies into machine learning models to enhance the accuracy of potato yield predictions. …”
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Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection. by Ekramul Haque Tusher, Mohd Arfian Ismail, Abdullah Akib, Lubna A Gabralla, Ashraf Osman Ibrahim, Hafizan Mat Som, Muhammad Akmal Remli

“…This study investigates the prognostic accuracy of several ensemble machine learning (ML) models for diagnosing HCV infection. …”
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A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets by Arju Manara Begum, Mahadee Al Mobin

“…Using data from 1990 to 2023, we apply a robust data pipeline comprised of six machine learning models and sequential squeeze feature selection incorporating eleven economic, industrial, and energy consumption variables. …”
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Rapid Flood Inundation Mapping for Effective Management: A Machine Learning and Pixel‐Based Classification Approach in Feni District, Bangladesh by Kabir Uddin, Sazzad Hossain, Birendra Bajracharya, Bayes Ahmed, Md. Khairul Islam

“…To assess the accuracy of flood map, this study focuses on pixel‐based digital classification and machine learning (ML) techniques separately for flood inundation mapping. …”
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The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implic... by Ping Ye, Binglin Guo, Huyong Qin, Cheng Wang, Yang Liu, Yuyang Chen, Pengfei Bian, Di Lu, Lei Wang, Weiping Zhao, Yonggan Yang, Li Hong, Peng Gao, Peiyong Ma, Binggen Zhan, Qijun Yu

“…Therefore, it is recommended to explore commercialization pathways tailored to local conditions and to develop machine learning models for performance prediction and life-cycle analysis, thereby promoting the widespread application of BC in industry and construction. …”
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Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors by Alexandru Bogdan Ilies, Cornel Cheregi, Hassan Hassan Thowayeb, Jan Reinald Wendt, Maur Sebastian Horgos, Liviu Lazar

“…However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. …”
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Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning by Xietian Xia, Shaohua Lei, Hui Lu, Zenghui Xu, Xiang Li, Xing Chen, Niancheng Hong, Jie Xu, Kun Shi, Jiacong Huang

“…The particle absorption coefficient of pigments (<i>a</i>_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of <i>a</i>_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. …”
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Unraveling the significance of cuproptosis in hepatocellular carcinoma heterogeneity and tumor microenvironment through integrated single-cell sequencing and machine learning appro... by Wang Liu, Liangjing Xia, Yuan peng, Qiang Cao, Ke Xu, Huiyan Luo, Yongjun Peng, Yanping Zhang

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Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models by Sami Naouali, Oussama El Othmani

“…This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. …”
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Personalized dose reduction strategies for biologic disease-modifying antirheumatic drugs for treating axial spondyloarthritis: a clinical and economic evaluation with predictive m... by Bui Hai Binh, Nguyen Thi Thu Phuong, Vu Thi Thanh Hang, Ngo Thi Thuc Nhan, Nguyen Thi Nhu Hoa, Hoang Van Dung

“…This study aimed to evaluate the clinical effectiveness and cost-efficiency of bDMARD dose reduction in patients with AS and apply machine learning to identify key factors influencing disease control. …”
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Evaluation of Decision Tree and Support Vector Machine Classifiers in Comparison for Flood Prediction by Suha Abdullah

“…This indicates that both the models had some false negatives for floods. The current study focuses more on machine learning applications and disaster readiness in flood risk assessment for better and more effective mitigation. …”
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Intelligent Modelling Techniques for Enhanced Thermal Comfort and Energy Optimisation in Residential Buildings by Shamaila Iram, Hafiz Muhammad Athar Farid, Abduljelil Adeola Akande, Hafiz Muhammad Shakeel

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