Prediction of Coalbed Methane Production Using a Modified Machine Learning Methodology by Hongyang Zhang, Kewen Li, Shuaihang Shi, Jifu He

“…The model is trained using the mean absolute error (<i>MAE</i>) loss function, optimized using the Adam optimizer, and finally evaluated using metrics such as <i>MAE</i>, root mean square error (<i>RMSE</i>), and R squared (<i>R</i>²) scores. …”
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Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode by Genming Lai, Ruiqi Zhang, Chi Fang, Juntao Zhao, Taowen Chen, Yunxing Zuo, Bo Xu, Jiaxin Zheng

“…Here we introduce a novel scheme, hybrid ab initio molecular dynamics combined with machine learning potential (HAML), to accelerate the modeling of electrode-electrolyte interface reactions. …”
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Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation by Nanon Sonnatthanon, Kasem Choocharukul

“…A Virtual Geometry Group deep learning model, optimized using Bayesian optimization, is trained to maximize the F1-score and Area Under the Curve, ensuring high prediction performance. …”
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Prediction of Enthalpy of Mixing of Binary Alloys Based on Machine Learning and CALPHAD Assessments by Shuangying Huang, Guangyu Wang, Zhanmin Cao

“…The enthalpy of mixing, a critical thermodynamic property in the liquid phase reflecting element interaction strength and pivotal for studying phase equilibria, can now be predicted efficiently using machine learning. This study proposes a model combining machine learning with the Calculation of Phase Diagram (CALPHAD) to predict the enthalpy of mixing. …”
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Enhancing Distributed Machine Learning through Data Shuffling: Techniques, Challenges, and Implications by Zhang Zikai

“…This paper proposes potential solutions to enhance the efficacy of data shuffling, including hybrid methodologies, automated stratification processes, and optimized coding strategies. This work aims to guide future research on data shuffling in distributed machine learning environments, ultimately advancing model robustness and generalization across complex real-world applications.…”
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The analysis of English language teaching with machine translation based on virtual reality technology by Tao Su, Lynette P. Rue

“…Based on the latest development of deep learning (DL), a new MT model is proposed in this study, and it is successfully integrated with VR technology to optimize the quality of ELT. …”
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Identification of soil texture and color using machine learning algorithms and satellite imagery by Jiyang Wang

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Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang, Ali Khalfallah

“…Model performance was optimized using k-fold cross-validation and hyperparameter tuning via grid search, with R-squared and RMSE as evaluation metrics. …”
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Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques by Tanzina Akter, Md. Farhad Hossain, Mohammad Safi Ullah, Rabeya Akter

“…By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. …”
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Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

“…The optimal model was applied to extrapolate PML-V2 ET data for the period from 1983 to 2000. …”
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Prediction of bank credit customers churn based on machine learning and interpretability analysis by Ying Li, Keyue Yan

“…Nowadays, traditional machine learning methods for building predictive models of credit card customer churn are no longer sufficient for effective customer management. …”
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A new classification algorithm for low concentration slurry based on machine vision by Chuanzhen Wang, Xinyi Wang, Andile Khumalo, Fengcheng Jiang, Jintao Lv

“…Abstract Machine vision was utilized in this study to accurately classify the low concentration slurry. …”
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In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing by Robert D. Weeks, Jennifer M. Ruddock, J. Daniel Berrigan, Jennifer A. Lewis, James. O. Hardin

“…Herein, a machine learning (ML) model that estimates ink rheology in‐situ from a simple printed test pattern is reported. …”
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Machine learning predicts prognosis in patients with gastroenteropancreatic neuroendocrine tumors with liver metastases by Fuli Gao, Jian Chen, Xiaodan Xu

“…However, accurate survival prediction models remain insufficient. This study aimed to develop machine learning-based models to predict the 1-year, 3-year, and 5-year overall survival in these patients. …”
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A machine learning approach for wind turbine power forecasting for maintenance planning by Hariom Dhungana

“…This study presents and compares nine machine learning (ML) methods for forecasting, Interpretable ML, Explainable ML, and Blackbox model. …”
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Machine learning and public health policy evaluation: research dynamics and prospects for challenges by Zhengyin Li, Hui Zhou, Zhen Xu, Qingyang Ma

“…The challenges discussed include model interpretability, data bias, the continuation of historical health inequities, and data privacy concerns, while proposing ways to better apply machine learning in the context of big data.ResultsMachine learning techniques hold promise in overcoming some limitations of traditional methods, offering more precise evaluations of public health policies. …”
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Evaluating the Thermohydraulic Performance of Microchannel Gas Coolers: A Machine Learning Approach by Shehryar Ishaque, Naveed Ullah, Sanghun Choi, Man-Hoe Kim

“…These findings highlight the potential of machine learning as a robust tool for optimizing thermal system performance and guiding the design of energy-efficient heat exchange technologies.…”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…Abstract Background This study aims to develop a machine learning (ML)-based predictive model for evaluating the efficacy of percutaneous pulmonary balloon angioplasty (BPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) by integrating clinical and echocardiographic parameters. …”
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Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning by Je-Doo Ryu, Hoon-Hee Lee, Kyoung-Nam Ha, Sung-Ryul Kim, Min Cheol Lee

“…The model is trained with vibration signals from various machining positions, with structured time-series datasets improving performance and generalization. …”
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Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning by Chunxiu Zhao, Bingbing Xiang, Jie Zhang, Pingliang Yang, Qiaoli Liu, Shun Wang

“…Predictive models were constructed using nine different machine learning algorithms. …”
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