Research on a Visually Assisted Efficient Blind-Guiding System and an Autonomous Shopping Guidance Robot Arm Adapted to the Complex Environment of Farmers’ Markets by Mei Liu, Yunhua Chen, Jinjun Rao, Wojciech Giernacki, Zhiming Wang, Jinbo Chen

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Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots by Jianping Sun, Weian Yang, Xin Meng, Jun Peng, Zhaoping Tang

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Prediction of Vehicle Interior Wind Noise Based on Shape Features Using the WOA-Xception Model by Yan Ma, Hongwei Yi, Long Ma, Yuwei Deng, Jifeng Wang, Yudong Wu, Yuming Peng

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Adaptive Determination of Optimum Switching Frequency in SiC-PWM-Based Motor Drives: A Speed-Dependent Core Loss Correction Approach by Sepideh Amirpour, Sima Soltanipour, Torbjorn Thiringer, Pranav Katta

“…The analysis integrates electromagnetic field simulations in Ansys Maxwell with the drive system control algorithm in Ansys Twin Builder, ensuring an accurate representation of their interactions. …”
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Advanced genetic algorithm (GA)-independent component analysis (ICA) ensemble model for predicting trapped humans through hybrid dimensionality reduction by Enoch Adama Jiya, Ilesanmi B. Oluwafemi

“…To choose relevant subset features from the data and for better generalization in various contexts, this work uses an adaptive human presence detector algorithm that hybridizes dimensionality reduction techniques genetic algorithm (GA), which maximizes feature selection, and independent component analysis (ICA), which lowers the dimensionality of the chosen features. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…We compared 10 ML models based on radiomics features: support vector machine (SVM), logistic regression (LR), random forest, extra trees (ET), naïve Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron (MLP), gradient boosting ML (GBM), light GBM (LGBM), and adaptive boost (AB). …”
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Investigation of comparative machine learning models in effluent dephenolization process onto H3PO4-anchored corn cob by Chijioke Elijah Onu, Joseph Tagbo Nwabanne, Ositadimma Chamberline Iheanocho, Paschal Enyinnaya Ohale, Chiamaka Peace Onu, Marcel Ikenna Ejimofor, Suraj Kumar Bhagat, Christian O. Asadu, Christopher C. Obi, Chidiogo Ezekwem

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Towards Economic Sustainability: A Comprehensive Review of Artificial Intelligence and Machine Learning Techniques in Improving the Accuracy of Stock Market Movements by Atoosa Rezaei, Iheb Abdellatif, Amjad Umar

“…Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. …”
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Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis. by Ayşe Banu Birlik, Hakan Tozan, Kevser Banu Köse

“…A retrospective analysis of 543 patients was performed using six machine learning algorithms applied to preoperative clinical data to assess predictive accuracy and clinical outcomes. …”
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Design and Motion Control of a Novel Weak-coupling Parallel Hip-joint Rehabilitation Mechanism by Xing Jiyuan, Xu Jilong, Liu Fucai

“…The parameters of controllers are adaptively adjusted online using the fuzzy algorithm. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Six classification models—five machine learning algorithms (Multilayer Perceptron, Random Forest, Decision Tree, K-Nearest Neighbors, and Adaptive Boosting) and one deep learning model (Long Short-Term Memory, LSTM)—were evaluated for classifying four levels of misalignment severity. …”
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Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study by Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon

“…To improve the performance of the machine learning method, we performed 5-fold cross-validation and utilized adaptive synthetic sampling to address data imbalance. …”
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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

“…Four machine learning algorithms were trained, Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and logistic regression, to predict cognitive decliners, defined as a decline of ≥3 K-MMSE points over 9 months, and ranked variable importance using the SHapley Additive exPlanations methodology.ResultsCatBoost outperformed the other models in classifying cognitive decliners within 9 months. …”
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Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study. by Shahrokh Mousavi, Zahrasadat Jalalian, Sima Afrashteh, Akram Farhadi, Iraj Nabipour, Bagher Larijani

“…<h4>Background</h4>Angina pectoris, a comparatively common complaint among older adults, is a critical warning sign of underlying coronary heart disease. We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…”
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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 is particularly critical in regions like Prince Edward Island (PEI), where potato production is not only a staple of local agriculture but also a cornerstone of the regional economy, accounting for a significant proportion of agricultural revenue and employment. Although machine learning algorithms have been extensively applied in agricultural yield prediction, previous studies have not fully leveraged the potential of capturing both short- and long-term dependencies. …”
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Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB₁ in Corn Silage by Daqian Wan, Haiqing Tian, Lina Guo, Kai Zhao, Yang Yu, Xinglu Zheng, Haijun Li, Jianying Sun

“…Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). …”
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Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios by Arifur Rahman Rifath, Md Golam Muktadir, Mahmudul Hasan, Md Ashraful Islam

“…This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. …”
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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…Here we propose a classical machine learning system, built around a Random Forest classifier paired with a novel feature extraction algorithm adapted from Explainable Boosted Linear Regression (EBLR). …”
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Efficient Short-Term Wind Power Prediction Using a Novel Hybrid Machine Learning Model: LOFVT-OVMD-INGO-LSSVR by Zhouning Wei, Duo Zhao

“…The time-varying properties of the fluctuating sub-signals of the wind power sequences were analyzed with the optimized variational mode decomposition (OVMD) algorithm. The Northern Goshawk optimization (NGO) algorithm was improved by incorporating a logical chaotic initialization strategy and chaotic adaptive inertia weights. …”
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Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery by Xinbao Chen, Xinbao Chen, Yaohui Zhang, Shan Wang, Zecheng Zhao, Chang Liu, Junjun Wen

“…To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. …”
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