Method for Increasing the Energy Efficiency of the Gear Teeth Cutting Process by Smoothing the Cutting Force Variation by Gabriel Radu Frumusanu, Mihail Bordeanu, Florin Susac

“…Recent studies show that the energy consumed for detaching chips represents only about 15% of the total energy involved in material machining. The available solutions for energy optimization in cutting processes mentioned are the improvement of manufacturing equipment, the optimization of processes, and appropriate production scheduling. …”
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Optimal-Transport-Based Positive and Unlabeled Learning Method for Windshear Detection by Jie Zhang, Pak-Wai Chan, Michael Kwok-Po Ng

“…To address this issue, we propose to use a positive and unlabeled learning method in this paper to identify windshear events from unreported cases based on wind velocity data collected by Doppler light detection and ranging (LiDAR) plan position indicator (PPI) scans. An optimal-transport-based optimization model is proposed to distinguish whether a windshear event appears in a sample constructed by several LiDAR PPI scans. …”
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Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement by Dimitris C. Gkikas, Prokopis K. Theodoridis

“…Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. …”
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Research on the Construction of Crossborder e-Commerce Logistics Service System Based on Machine Learning Algorithms by Jinbo Xu, Shibiao Mu

“…First, we introduce the meaning of query recommendation, analyze the mechanism of e-commerce platform shopping search, redesign the query recommendation process on this basis, establish a Markov decision process model for the problem, and solve the optimal recommendation strategy through deep machine learning algorithms. …”
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Data-driven power marketing strategy optimization and customer loyalty promotion by Bo Chen, Wei Cui

“…Additionally, the results underscore the model’s effectiveness in forecasting and optimizing marketing outcomes, offering a scalable solution for the evolving power sector. …”
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Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

“…Twelve ML algorithms were evaluated using 10-fold cross-validation on a training set (70%). The optimal model was selected based on AUC-ROC and further optimized via hyperparameter tuning. …”
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DYNAMIC SIMULATION FOR TRANSMISSION SYSTEM OF COAL WINNING MACHINE HAULAGE PART BASED ON RECURDYN SOFTWARE by PU ZhiXin, ZHOU ShuYe, DING DanDan

“…To establish a rigid modle and a rigid-flexible coupling model of the transmission system of a coal winning machine haulage part,based on the theory of non-linear contact theory and multi-body dynamic. …”
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Using AI for Optimizing Packing Design and Reducing Cost in E-Commerce by Hayder Zghair, Rushi Ganesh Konathala

“…In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. …”
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Imaging-based machine learning to evaluate the severity of ischemic stroke in the middle cerebral artery territory by Gang Xie, Jin Gao, Jian Liu, Xuwei Zhou, Zhengkai Zhao, Wuli Tang, Yue Zhang, Lingfeng Zhang, Kang Li

“…Abstract Objectives This study aims to develop an imaging-based machine learning model for evaluating the severity of ischemic stroke in the middle cerebral artery (MCA) territory. …”
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Diagnosis of bipolar disorder based on extracted significant biomarkers using bioinformatics and machine learning algorithms by Hamid Mohseni, Massoud Sokouti, Akram Nezhadi, Ali Sayadi

“…The obtained gene expression data were trained by artificial neural network and decision tree method to identify the best models. Four parameters of sensitivity, specificity, accuracy, and area under the curve (AUC) were used to check the optimality of the model resulting from the training of machine learning algorithms. …”
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Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

“…Model weights and optimal thresholds varied markedly across sites. …”
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A Survey on Machine Learning Enhanced Integrated Sensing and Communication Systems: Architectures, Algorithms, and Applications by Mikael Ade Krisna Respati, Byung Moo Lee

“…This technology utilizes the same communication resources for communicating and sensing within the same framework, enabling more efficient use of resources. Currently, machine learning (ML) has been developed in the field of communications, including sensing and wireless communications, due to its ability to tackle complex optimization problems, estimate complex issues, and extract and exploit spatial/temporal patterns that can improve ISAC performance. …”
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Rainfall nowcasting by integrating radar and rain gauge data with machine learning for Ischia Island, Italy by Fereshteh Taromideh, Giovanni Francesco Santonastaso, Roberto Greco

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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…Model performance was compared​ to four other machine learning algorithms using the area under the curve (AUC). ​…”
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Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture by Tang M, Zhang M, Dang Y, Lei M, Zhang D

“…Among the six developed models, the eXGBM model demonstrated the optimal model, with the area under the curve (AUC) value of 0.929 (95% CI: 0.900– 0.959), followed by the RF model (AUC: 0.916, 95% CI: 0.885– 0.948). …”
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Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment by Sheng Hu, Junyu Liu, Jiayi Hong, Yuting Chen, Ziwen Wang, Jibo Hu, Shiying Gai, Xiaochao Yu, Jingjing Fu

“…The support vector machine (SVM) was the optimal ML algorithm for constructing the models. …”
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Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods by Shakti P. Padhy, Soumya R. Mishra, Li Ping Tan, Karl P. Davidson, Xuesong Xu, Varun Chaudhary, R.V. Ramanujan

“…In this study, we assess Fe-Co-Ni alloy compositions identified in our previous work through a machine learning (ML) framework, which used both multi-property ML models and multi-objective Bayesian optimization to design compositions with predicted high values of saturation magnetization, Curie temperature, and Vickers hardness. …”
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Comparative Study on Total Organic Carbon Content Logging Prediction Method Based on Machine Learning by TANG Shengshou, YANG Bin, JIN Jiulong, LIU Hongrui, DAI Xingyu, PU Jincheng

“…The Δlog R calculation model with R2=0.624 8, the BP neural network prediction model with R2=0.814 4, the support vector machine prediction model with R2=0.702 9 and the XGBoost prediction model with R2=0.937 0 were established. …”
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Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma by Bo Lan, Zongyun He, Zhe Chen, Haibing Tao, Tao Liu, Jin Yang

“…All patients were randomly divided into the training cohort and validation cohort (8:2). Eight machine learning algorithms were used to construct predictive model for synchronous BM in the training cohort, and the optimal model was selected for further validation. …”
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Longitudinal Analysis of Risk Factors for Pulmonary Function Decline in Chronic Lung Diseases Over Five Years by Li L, Meng J, Chen J

“…Both calibration and decision curves further substantiated the reliability of the model in identifying patients at increased risk for pulmonary function decline.Conclusion: The predictive model developed in this study serves as a valuable tool for clinicians to target early interventions and optimize treatment strategies to enhance the quality of care and patient outcomes in the management of CLDs.Keywords: chronic lung diseases, pulmonary function decline, latent class growth modeling, random forest model, health services, machine learning in healthcare…”
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