Analysis of Electromagnetic Characteristics of Outer Rotor Type BLDC Motor Based on Analytical Method and Optimal Design Using NSGA-II by Tae-Seong Kim, Jun-Won Yang, Kyung-Hun Shin, Gang-Hyeon Jang, Cheol Han, Jang-Young Choi

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The Effect of Hydraulic Partitioning on Prediction the Rate of Bed Load Transport in Gravel-bed Rivers using Support Vector Machine by Kiyoumars Roushangar, Mohammad Hosseini, Saman Shahnazi

“…The RBF kernel function was used as core tool of support vector machine for all proposed models. After optimization of parameters for kernel function, the bed load transport rate was predicted and obtained results from different models were investigated in terms of correlation coefficient (R), Root mean square error (RMSE) and Nash-Sutcliffe (NSE). in order to assess the capability of SVM in quantification of bed load under varied hydraulic conditions, Froude number (Fr) and bed slope of channel (S0) were selected as a parameters describing the hydraulic conditions and median diameter of the sediment particles (D50) and shear Reynolds number (Re*) were considered as a representative of sediment characteristic. …”
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Heuristic modeling of the process of milling aviation parts with opposed end mills by synthesis of a predictive model by E. A. Shestakova, V. O. Ievlev, R. M. Yanbaev

“…The influence of the main negative factors affecting the quality of machined surfaces is revealed. Optimal schemes for machining with opposed cutters are proposed. …”
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Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics by Ye Ruan, Xingyuan Liu, Yantong Jin, Mingming Zhao, Xingda Zhang, Xiaoying Cheng, Yang Wang, Siwei Cao, Menglu Yan, Jianing Cai, Mengru Li, Bo Gao

“…The rad-score was calculated for each patient. Five machine learning classifiers were used to build radiomics models, and the optimal model was selected. …”
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From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI by Gizem Izmir Tunahan, Goksu Tuysuzoglu, Hector Altamirano

“…Drawing on over 1.3 million ten-minute passive infrared (PIR) sensor observations collected throughout 2023 at the UCL Bartlett Library, we modeled seat-level occupancy using 24 spatial, environmental, and temporal features through advanced machine learning algorithms. …”
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Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults: a retrospective study by Qiaoli Wang, Tao Liang, Yuexi Li, Peng Zhou, Xiaoqin Liu

“…After feature screening, 10 factors were selected for the prediction model. Among six machine—learning models, the Extra Trees model had the best performance, with an AUC of 0.827, Accuracy of 0.744, and Recall of 0.736. …”
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Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms by Mehmet Eroğlu, Ali Osman Turgut, Mürsel Küçük, Muhammed Furkan Önen

“…GridSearchCV was utilized for hyperparameter optimization. The performance of the models was evaluated using various goodness‐of‐fit metrics, including RMSE, MAE, R2, MAPE, RAE, MAD and SD ratio. …”
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Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP by Wei Liu, Zhiping Cai, Yifan Chen, Xingqun Guan, Jieying Feng, Haixiong Chen, Baoliang Guo, Fusheng OuYang, Chun Luo, Rong Zhang, Xinjie Chen, Xiaohong Li, Cuiru Zhou, Shaomin Yang, Ziwei Liu, Qiugen Hu

“…This study aims to develop and validate an optimal machine learning model to predict cholangiocyte phenotype HCC based on T1 mapping gadoxetic acid-enhanced MRI and to implement individual applications via the Shapley Additive explanation (SHAP). …”
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…The proposed model overcomes difficulties and issues related to EEG signals, leading to an accurate emotion detection system and providing enhanced machine understanding of human–machine interactions.…”
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Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making by Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

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Composite dietary antioxidant index and HPV infection from single and mixed associations to SHAP-interpreted machine learning predictions by Pei Zhang

“…In addition, nine machine learning (ML) methods were employed to construct predictive models, and SHapley Additive exPlanations (SHAP) was used to further interpret the optimal model.ResultsThis study enrolled 9,224 adult female participants. …”
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Cysteine pattern barcoding-based dataset filtration enhances the machine learning-assisted interpretation of Conus venom peptide therapeutics. by Rimsha Bibi, Noshaba Qasmi, Sajid Rashid

“…The model's performance could be further enhanced by incorporating additional datasets and optimizing feature selection, potentially broadening its applicability to larger peptide datasets. …”
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An Empirical Study on the Deficiencies and Optimization of the Management System of Tourist Attractions Based on Human Resource Management by Yan Tang, Siran Bai, Lingling Cui

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Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm by Kharisma Gunanta Ginting, Nugroho Prasetyo, Al Vino Gunawan, Magdalena Sihombing, Adli Abdillah Nababan

“…However, the classification of anxiety levels is often hampered by data imbalance, which can reduce the accuracy of predictive models. This study aims to develop a patient anxiety level classification model in the ED using the Support Vector Machine (SVM) algorithm with the application of the Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance issue. …”
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Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors by Catherine Park, Namhee Kim, Chang Won Won, Miji Kim

“…Among the diverse CF-associated characteristics, the machine learning-based model identified six optimal features (key predictors): motor capacity, education level, physical function limitation, nutritional status, balance confidence, and activities of daily living. …”
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A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools by Saad Javed Cheema, Masoud Karbasi, Gurjit S. Randhawa, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal, Farhat Abbas, Qamar Uz Zaman, Aitazaz A. Farooque

“…Different performance metrics such as correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to compare different model's performance. Results showed that the CGO-XGBoost model outperformed conventional machine learning models. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…It examines the performance of a logistic regression model enhanced by Bayesian optimization for detecting TOR traffic using the UNB-CIC TOR-NonTOR datasets and a Bayesian-optimized random forest model for the CIC-Darknet2020 dataset. …”
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Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach by Seong-Jin Kim, Xue-Cheng Jin, Rajaraman Bharanidharan, Na-Yeon Kim

“…The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. …”
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High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques by Saad A. Alsamraee, Sanjeev Khanna

“…Finally, during the deployment phase, the optimal model was employed to forecast energy demand for the full year 2023—the primary objective of this study—exhibiting high robustness through close adherence to actual demand patterns.…”
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Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy by Wlla E. Al-Hammad, Masahiro Kuroda, Ghaida Al Jamal, Mamiko Fujikura, Ryo Kamizaki, Kazuhiro Kuroda, Suzuka Yoshida, Yoshihide Nakamura, Masataka Oita, Yoshinori Tanabe, Kohei Sugimoto, Irfan Sugianto, Majd Barham, Nouha Tekiki, Miki Hisatomi, Junichi Asaumi

“…<b>Results</b>: Our findings indicate that the decision tree (DT) model demonstrated consistently high robustness at 240 and 270 cGy, while the random forest model performed optimally at 300 cGy. …”
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