The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies by Xu Shasha, Wu Haipeng, Luo Junting, Chen Jian, Jia Huihan

“…This paper, based on the Support Vector Machine (SVM) model, explores its application in industrial carbon accounting, focusing on the interaction between carbon emissions prediction and optimization of control strategies. …”
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Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations by Maram A. Alkhayyal, Almetwally M. Mostafa

“…Previous studies have employed various Machine Learning (ML) models for path loss prediction. …”
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Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing by Tareq Nafea Alharby, Bader Huwaimel

“…The models of Kernel Ridge Regression (KRR), Multi-Linear Regression (MLR), and Orthogonal Matching Pursuit (OMP) were optimized in prediction of three Hansen solubility parameters. …”
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Numerical Simulation and Experimental Validation of Residual Stress in Heavy Machine Tool Crossbeam Casting During Demolding by Jingfan Cheng, Yiqi Zhang, Dunming Liao

“…This study investigates a heavy-duty CNC machine tool crossbeam casting manufactured by a leading heavy machine tool producer. …”
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Using machine learning to predict anesthetic dose in fish: a case study using nutmeg oil by Mert Minaz, Cem Alparslan, Akif Er

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An Interpretable Machine Learning Framework for Analyzing the Interaction Between Cardiorespiratory Diseases and Meteo-Pollutant Sensor Data by Vito Telesca, Maríca Rondinone

“…Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). …”
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Linear Active Disturbance Rejection Control System for the Travel Speed of an Electric Reel Sprinkling Irrigation Machine by Lingdi Tang, Wei Wang, Chenjun Zhang, Zanya Wang, Zeyu Ge, Shouqi Yuan

“…Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. …”
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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data by Salha Al-Ahmari, Farrukh Nadeem

“…<b>Objectives</b>: The aim of this study is to evaluate and enhance the predictive capabilities of machine learning models for SSIs by assessing the effects of feature selection, resampling techniques, and hyperparameter optimization. …”
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Dynamics-Guided Support Vector Machines for Response Analysis of Steel Frame Under Sine Wave Excitation by Yao Wang, Huaiman Li

“…To address this issue, a novel dynamics-guided support vector machine (DG-SVM) method is proposed, which embeds an optimization process to reduce dependence on the time step size. …”
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Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

“…For predicting T staging, the support vector machine (SVM) model demonstrated the highest accuracy, with training and validation accuracies of 0.909 and 0.907, respectively. …”
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A systematic review of AI-enhanced techniques in credit card fraud detection by Ibrahim Y. Hafez, Ahmed Y. Hafez, Ahmed Saleh, Amr A. Abd El-Mageed, Amr A. Abohany

“…This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). …”
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An Improved Extreme Learning Machine (ELM) Algorithm for Intent Recognition of Transfemoral Amputees With Powered Knee Prosthesis by Yao Zhang, Xu Wang, Haohua Xiu, Wei Chen, Yongxin Ma, Guowu Wei, Lei Ren, Luquan Ren

“…To overcome the challenges posed by the complex structure and large parameter requirements of existing classification models, the authors propose an improved extreme learning machine (ELM) classifier for human locomotion intent recognition in this study, resulting in enhanced classification accuracy. …”
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Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms by Nian-Ze Hu, Bo-An Lin, Yen-Yu Wu, Hao-Lun Huang, You-Xin Lin, Chih-Chen Lin, Po-Han Lu

“…By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. …”
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Real-Time Task Scheduling and Resource Planning for IIoT-Based Flexible Manufacturing with Human–Machine Interaction by Gahyeon Kwon, Yeongeun Shim, Kyungwoon Cho, Hyokyung Bahn

“…In this paper, instead of treating dynamic scheduling as a prediction problem, we model it as deterministic planning in response to explicit, observable user input. …”
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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS by Ruizhi Zhang, Dayong Zhang, Bo Shu, Yang Chen

“…A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. …”
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Prediction of rock fracture pressure in hydraulic fracturing with interpretable machine learning and mechanical specific energy theory by Xiaoying Zhuang, Yuhang Liu, Yuwen Hu, Hongwei Guo, Binh Huy Nguyen

“…For more precise predictions, incorporating additional characteristics from the mechanical specific energy framework into the machine learning model is essential. The study emphasizes the feasibility of employing machine learning methods to predict fracture pressure and their usefulness in determining optimal engineering sites.…”
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Generalized MPC-DSVPWM Methods: Reduction Techniques and Explainable Machine Learning With Conformal Prediction for PMSM Drives by Hasan Ali Gamal Al-Kaf, Sadeq Ali Qasem Mohammed, Kyo-Beum Lee

“…Additionally, machine learning methods have been implemented; however, they require complex optimization methods, as the classification number increases exponentially with the level of the inverter. …”
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Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…A two-phase optimization approach, combining Random Search and Adaptive Sampling, is employed to refine the hyperparameters of the machine learning model. …”
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