Construction and analysis of China’s carbon emission model based on machine learning by Jian Sun, Xinzi Wang, Mengkun Liang, Xiaoru Ren, Xuezhi Liu

“…Abstract To cope with the severe challenge of China’s huge carbon emissions, this study introduced a comprehensive research paradigm, “modelling + SHAP analysis + scenario prediction,” from the machine learning perspective. …”
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Research and Application of Heart Disease Prediction Model Based on Machine Learning by Bao Yongli

“…Future research can stack models and optimize data sources to improve the practical performance of the model. …”
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Comparative analysis of machine learning models for the detection of fraudulent banking transactions by Pedro María Preciado Martínez, Ricardo Francisco Reier Forradellas, Luis Miguel Garay Gallastegui, Sergio Luis Náñez Alonso

“…This research presents a comparative analysis of machine learning models for detecting fraudulent banking transactions, a growing problem in the digital financial sector. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…However, machine learning models offer a time-efficient solution for predicting pavement performance. …”
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Machine learning based multi-parameter droplet optimisation model study by Ting Li, Likun Lu, Qingtao Zeng, Kexin Liao

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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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Compatibility Model between Encapsulant Compounds and Antioxidants by the Implementation of Machine Learning by Juliana Quintana-Rojas, Rafael Amaya-Gómez, Nicolas Ratkovich

“…The compatibility between antioxidant compounds (ACs) and wall materials (WMs) is one of the most crucial aspects of the encapsulation process, as the encapsulated compounds’ stability depends on the affinity between the compounds, which is influenced by their chemical properties. A compatibility model between the encapsulant and antioxidant chemicals was built using machine learning (ML) to discover optimal matches without costly and time-consuming trial-and-error experiments. …”
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A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model by Yi Lu, Jie Yin, Peiyan Chen, Hui Yu, Sirong Huang

“…Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. …”
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Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies by Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet, Stephane Bourassa

“…Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. …”
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TBM shield mud cake prediction model based on machine learning by Qi Zhang, Peng Xu, Jing Zhang, Zhao Yang, Yu Li, Xintong Kong, Xiao Yuan

“…The optimal predictive model for mud cake formation was determined by assessing the precision, recall, and F1 scores of the models. …”
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Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes by Qinfei Zhao, Weiquan Hu, Yu Xia, Shengyun Dai, Xiangsheng Wu, Jing Chen, Xiaoying Yuan, Tianyu Zhong, Xuxiang Xi, Qi Wang

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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…By evaluating the performance differences among these machine learning models, this study aims to assist clinicians in early prediction and diagnosis of POP, providing optimal interventions and treatments.MethodsRetrospective data from electronic medical records was collected for 264 patients diagnosed with postoperative pneumonia and 264 healthy control surgical patients. …”
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Optimal Design of Welded Structure Using SVM by Sebghatullah Jueyendah, Carlos Humberto Martins

“…Engineers and researchers can develop more efficient, load-resistant, reliable, and cost-effective structures using optimization techniques, Sensitivity Analysis (SA), and support vector machine (SVM) applications. …”
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Movement and distribution of particles in the tank of jet and agitation combined flotation machine by Tao WANG, Wei ZHOU, Zilei WANG, Shujie WANG, Lingling WANG, Liang LI

“…In order to explore the movement and distribution of particles in the tank of jet and agitation co-flotation machine, a geometric model of the tank of jet and agitation co-flotation machine was established. …”
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A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling by Reza Lotfi, Soheila Sadeghi, Sadia Samar Ali, Fatemeh Ramyar, Ehsan Ghafourian, Ebrahim Farbod

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Feeding precision experiment of projective feeding machine by ZHANG Fengdeng, ZHU Songming, ZHANG Peiqi, JI Baimin, WANG Keyu, WEN Yanci, YE Zhangying

“…The above results can provide a reference for optimization and application of the type of feeding machine.…”
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Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretabi... by Xiang Zhang, Wei Shao, Ming Qiu, Chenglin Xiao, Liming Ma

“…This study evaluated machine learning (ML) models on the Wisconsin Breast Cancer Dataset (WBCD), refined to 554 unique instances after addressing 5% missing values via mean imputation, removing 15 duplicates, and normalizing features with Min–Max scaling. …”
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Modeling and Estimating LIDAR Intensity for Automotive Surfaces Using Gaussian Process Regression: An Experimental and Case Study Approach by Recep Eken, Oğuzhan Coşkun, Güneş Yılmaz

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A Review of Generative Design Using Machine Learning for Additive Manufacturing by Parankush Koul

“…This review explores how generative design is combined with machine learning (ML) to achieve additive manufacturing (AM) and its societal transformative effect. …”
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Dynamic demand response strategies for load management using machine learning across consumer segments by Ravi Kumar Goli, Nazeer Shaik, Manju Sree Yalamanchili

“…<p>Grid optimization and stability are essential for sustainable power management while energy demand keeps increasing. …”
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