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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Three-Dimensional In Situ Stress Distribution in a Fault Fracture Reservoir, Linnan Sag, Bohai Bay Basin by Jiageng Liu, Yanzhong Wang, Jing Li, Xiaoyu Meng, Jiayi Teng, Zhicheng Wang, Mingzhi Li, Rui Zhu

“…Machine learning techniques, specifically a back propagation (BP) neural network, are utilized to invert the boundary conditions of the study area. …”
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Dropout Kolmogorov–Arnold Networks: A Novel Data-Driven Impedance Modeling Approach for Voltage-Source Converters by Moetasem Ali, Yasser Abdel-Rady I. Mohamed

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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 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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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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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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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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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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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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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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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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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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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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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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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…To identify the optimal model, both traditional machine learning and transfer learning approaches were employed, followed by model fusion using post-fusion techniques. …”
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Quality-Aware PPG-Based Blood Pressure Classification for Energy-Efficient Trustworthy BP Monitoring Devices With Reduced False Alarms by Yalagala Sivanjaneyulu, M. Sabarimalai Manikandan, Srinivas Boppu, Linga Reddy Cenkeramaddi

“…In this paper, we present four SQA methods and nine machine learning (ML) based BP classification models, including logistic regression, decision tree, random forest, multilayer perceptron, k-nearest neighbours, XGBoost, AdaBoost, Bagged Tree, and one-dimensional convolutional neural network (1D-CNN). …”
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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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Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method by Ali Khosrozadeh, Seyed Ali Niknam, Fatemeh Hajizadeh

“…This study proposes an ensemble learning regression model to accurately predict burr size and surface roughness during the slot milling of aluminum alloy (AA) 6061. …”
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