A machine learning model for robust prediction of sepsis-induced coagulopathy in critically ill patients with sepsis by Jia Sun, Jia Sun, Lixin Zhang, Zhaotang Gong, Hongling Ma, Dan Wu, Rihan Wu, Guleng Siri, Guleng Siri

“…We selected 17 features to construct ML prediction models. The gradient boosting machine (GBM) model was deemed optimal as it achieved high predictive accuracy and reliability across the training, internal, and external validation datasets. …”
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Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing by Samuel M. Gbashi, Obafemi O. Olatunji, Paul A. Adedeji, Nkosinathi Madushele

“…The optimal contamination fraction for the anomaly detection models was 4%. …”
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Electrolytic coating of iron-chromium alloy of sulphate-chloride electrolyte in machine parts recovery process modelling by A. S. Lanuta, Yu. V. Shtefan, V. К. Fiodorov, N. I. Korneichuk

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Discrete Element Method-Based Stratified Soil Modeling to Improve the Precision of Soil–Machine Interaction Simulations by Baoer Hao, Xuejie Ma, Lixin Wang, Xin Tong

“…Compared with excavation simulation and field tests, the error was within 10%, confirming that the model can accurately represent soil behavior. The results demonstrate that integrating the JKR and Bonding contact models provides an effective framework for simulating soil–machine interactions and establishes a robust numerical parameter basis for optimizing agricultural machinery design and soil management practices.…”
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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…This study highlights the potential of ML-driven stress symptom detection models for controlled seedling production, enabling real-time decision-making to optimize crop health and productivity.…”
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Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach by Mohammed B. Alshawki, Ihab Ahmed Najm, Alaa Khalaf Hamoud

“…However, finding the optimal congestion control model is an important yet challenging task. …”
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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…Such heterogeneity can be characterized by ML models and optimally mapped into network states, providing new insights to consider when developing personalized drugs.…”
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Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers by Hamidreza Amirhassankhani, Abbas Toloie Eshlaghy, Reza Radfar, Alireza pourebrahimi

“…The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. …”
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Machine Learning-Based Modelling and Predictive Maintenance of Turning Operation under Cooling/Lubrication for Manufacturing Systems by Gurpreet Singh, Jothi Prabha Appadurai, Varatharaju Perumal, K. Kavita, T. Ch Anil Kumar, DVSSSV Prasad, A. Azhagu Jaisudhan Pazhani, K. Umamaheswari

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A Dual Resource Constrained Unrelated Parallel Machine Scheduling Model Considering Tardiness and Workload Balance by Karina Amanda Larasati, Sukoyo Sukoyo, Muhammad Akbar

“…However, it is still uncertain that the WSI value is better because no boundaries have been set between the two objective functions to achieve optimal values in the MILP model. The MIQP model focuses on the Workload Smoothness Index (WSI) value to give a limit to the total tardiness objective function. …”
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Prediction of Student Academic Performance Utilizing a Multi-Model Fusion Approach in the Realm of Machine Learning by Wei Zou, Wei Zhong, Junzhen Du, Lingyun Yuan

“…Finally, we integrated multiple machine learning models to create a practical framework for predicting student academic performance, which can be applied in student digital management. …”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

“…This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolution dataset from drilling operations in southeast Iraq. …”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

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Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review by Muaaz Bin Kaleem, Wei He, Heng Li

“…Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. …”
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Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients by Yu Zhu, Renrui Liang, Ying Wang, Jian-Jun Yang, Ning Zhou, Cheng-Mao Zhou

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Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures by Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior, Alexandre Duarte

“…This study predicts binary corporate debt levels (high or low) using supervised machine learning (ML) models and firms’ characteristics as predictive variables. …”
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Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects by ZHANG Zijiao, DING Shunjing, ZHAO Di, LIANG Jun, LEI Jianbo

“…Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. …”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. Various models were employed, including machine learning algorithms and multiple linear regression. …”
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Development of Machine Learning Models for Estimating Metabolizable Protein Supply from Feed in Lactating Dairy Cows by Mingyung Lee, Dong Hyeon Kim, Seongwon Seo, Luis O. Tedeschi

“…This study aimed to develop novel machine learning models to predict rumen-undegradable protein (RUP) and duodenal microbial nitrogen (MicN) based on dietary protein intake. …”
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Predicting College Student Engagement in Physical Education Classes Using Machine Learning and Structural Equation Modeling by Liguo Zhang, Jiarui Gao, Liangyu Zhao, Zetan Liu, Anlin Guan

“…Nine machine learning algorithms were employed to develop interpretable predictive models, rank the importance of digital technology tools, and identify the optimal predictive model for student engagement. …”
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