A Review of Reliability Assessment and Lifetime Prediction Methods for Electrical Machine Insulation Under Thermal Aging by Jian Zhang, Jiajin Wang, Hongbo Li, Qin Zhang, Xiangning He, Cui Meng, Xiaoyan Huang, Youtong Fang, Jianwei Wu

“…Furthermore, the potential applications of thermal lifetime models, such as electrical machine design optimization, fault prognosis, health management, and standard development are reviewed. …”
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ADVANCES IN THE DESIGN AND OPTIMIZATION OF SMART IRRIGATION SYSTEMS FOR SUSTAINABLE URBAN VERTICAL FARMING by Kuanysh Bakirov, Jamalbek Tussupov, Tamara Tultabayeva, Kadyrzhan Makangali, Gulzira Abdikerimova, Moldir Yessenova

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A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards by Roni Azriel, Oded Degani, Avital Bechar

“…It compares the current approach for optimizing manipulator configurations, which relies on simulation and optimization algorithms, with an improved methodology that integrates machine learning models to enhance the optimization process. …”
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Energy Consumption Prediction of Building Heating Systems Based on Model Identification Methods by 曲明璐, 杜尚赫, 张欣林, 于 震, 李 怀

“…The study conducts an in-depth analysis of time-series historical data generated by buildings using machine learning techniques. A general model identification method was developed through an algorithm that optimizes competition based on black-box models. …”
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Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization by Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao, Fan Zhang

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Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models by Martín Pérez Salomón, Sanchez Jimenez Flora, Fuentes Cantero Sandra, Jímenez Barragan Marta, Sanchez Mora Catalina, Borreguero Leon Juan M., Teresa Arrobas Velilla, Valido Morales Agustín, Delgado Torralbo Juan A., León Justel Antonio

“…Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize predictive models for critical COVID-19 disease. …”
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Application of the RFA-XGBoost model in predicting potential complaint users in mobile network by ZHANG Peng, GAO Yuan

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Modelling and Prediction of Particulate Matter, NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine by Ka In Wong, Pak Kin Wong, Chun Shun Cheung

“…With RVM, only a few experimental data sets can train the model due to the property of global optimal solution. …”
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Development of a machine learning prognostic model for early prediction of scrub typhus progression at hospital admission based on clinical and laboratory features by Youguang Lu, Zixu Wang, Junhu Wang, Yingqing Mao, Chuanshen Jiang, Jinpiao Wu, Haizhou Liu, Haiming Yi, Chao Chen, Wei Guo, Liguan Liu, Yong Qi

“…Additionally, a simplified model based on Support Vector Machine was constructed and evaluated as an alternative optimal model.Conclusions This study is the first to use machine learning algorithms to accurately predict the developments of ST patients upon admission to hospitals. …”
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Integrating machine learning-based classification and regression models for solvent regeneration prediction in post-combustion carbon capture: An absorption-based case by Farzin Hosseinifard, Mostafa Setak, Majid Amidpour

“…This research introduces a primary model comprising two sub-models aimed at optimizing PCC configurations. …”
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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We determined that the XGBoost models were the optimal models for predicting overall survival (OS) and breast cancer-specific survival (BCSS) in MBC patients with the most accurate performance (AUC=0.833-0.948). …”
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Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia by Fangmin Zhong, Fangyi Yao, Zihao Wang, Jing Liu, Bo Huang, Xiaozhong Wang

“…These findings suggest that a pronounced immunosuppressive effect is associated with a significantly worse prognosis for this subtype. The optimal risk score model was selected by employing the C-index as the criterion on the basis of training 10 machine learning algorithms on 9 multicenter AML cohorts. …”
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Development and external validation of a machine learning model for predicting drug-induced immune thrombocytopenia in a real-world hospital cohort by Hoang Van Dung, Vu Manh Tan, Nguyen Thi Dieu, Pham Van Linh, Nguyen Van Khai, Tran Thi Ngan, Nguyen Thi Thu Phuong

“…Objective To develop and externally validate a machine learning model for predicting the risk of DITP using routinely collected hospital data, and to optimize its clinical applicability through threshold adjustment. …”
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Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study by Yongsheng Zhang, Hongyu Zhang, Dawei Wang, Na Li, Haoyue Lv, Guang Zhang

“…Significant predictors were selected on the training set using recursive feature elimination methods, followed by prediction model development using 7 machine learning algorithms (logistic regression, random forest, support vector machine, multilayer perceptron, extreme gradient boosting machine, light gradient boosting machine, and categorical boosting machine [CatBoost]), optimized through grid search and 5-fold cross-validation. …”
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Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model by Meina Sun, Shihui Liu, Jie Min, Jie Min, Lei Zhong, Lei Zhong, Jinyu Zhang, Jinyu Zhang, Zhian Du

“…Feature selection was conducted utilizing LASSO regression, which was subsequently followed by the development of eight distinct machine learning models. The performance of these models in the temporal external validation cohort was rigorously assessed using the area under the receiver operating characteristic curve (AUROC) to determine the optimal model. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. …”
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Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision by Graig S. Ganitano, Benji Maruyama, Gilbert L. Peterson

“…Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three‐dimensional (3D) printing, but is time‐consuming and requires expertise. …”
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A cluster-based local modeling paradigm for high spatiotemporal resolution VPD prediction using multi-source data and machine learning by Mi Wang, Zhuowei Hu, Xiangping Liu, Wenxing Hou

“…By comparing global and local modeling performances across various machine learning algorithms, the optimal model is selected for VPD inversion. …”
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Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches by Burcu Ramazanli, Oyku Yagmur, Efe Cesur Sarioglu, Huseyin Enes Salman

“…Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. …”
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Toward Low-Resource Languages Machine Translation: A Language-Specific Fine-Tuning With LoRA for Specialized Large Language Models by Xiao Liang, Yen-Min Jasmina Khaw, Soung-Yue Liew, Tien-Ping Tan, Donghong Qin

“…Experiments on non-English centered low-resource Asian languages demonstrated that LSFTL improved COMET scores by 1-3 points compared to specialized multilingual machine translation models. Additionally, LSFTL’s parameter-efficient approach allows smaller models to achieve performance comparable to their larger counterparts, highlighting its significance in making machine translation systems more accessible and effective for low-resource languages.…”
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