Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines by Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen, Li Zhang, Chuyang Wang

“…This study confirms the effectiveness of integrating Bayesian statistics with machine learning for power data restoration, providing a high-precision and low-complexity solution for equipment condition monitoring in complex operational environments. …”
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Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning by Yuanyuan Wang, Yu Ji, Jie Liu, Lianjiang Lv, Zihe Xu, Meimei Yan, Jialu Chen, Zhijun Luo, Xianjun Zeng

“…Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. …”
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A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…This system integrates Internet of Things (IoT) technology, machine learning, and smart automation to offer a safer, more responsive, and comfortable environment for babies. …”
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Aspects of Thermal Protection of Machine-Building and Power Equipment: Application of Oxidation-Resistant Combined Nickel-Based Coatings by V. N. Varavka, O. V. Kudryakov, V. I. Grishchenko

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Prediction on Permeability Coefficient of Continuously Graded Coarse-Grained Soils: A Data-Driven Machine Learning Method by Jinhua Wang, Haibin Ding, Lingxiao Guan, Yulin Wang

“…Gray relational analysis (GRA) revealed that all input parameters (α, β, d_max, n) significantly influence <i>k</i> (R > 0.6). …”
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Machine Learning to Retrieve Gap-Free Land Surface Temperature from Infrared Atmospheric Sounding Interferometer Observations by Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Carmine Serio, Italia De Feis

“…We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. …”
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Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes by Ibrahim M. Moustafa, Iman Akef Khowailed, Shima A. Mohammad Zadeh, Dilber Uzun Ozsahin, Mubarak Taiwo Mustapha, Paul A. Oakley, Deed E. Harrison

“…Abstract This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical outcomes in patients suffering from fibromyalgia syndrome (FMS). …”
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Machine-learning-based spatial analysis of the spring states in the southernmost Eurasian permafrost, Hangai Mountains, central Mongolia by Mamoru Ishikawa, Azumi Okazaki, Avirmed Dashtseren, Khurelbaatar Temuujin, Tetsuya Hiyama

“…To predict the states of remaining 1392 springs, machine learning approaches, including logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied, incorporating vegetation indices and topographically derived hydrological parameters as explanatory variables. …”
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Influence of machine type, traffic intensity, and travel speed on selected soil physical properties during skidding operations by Ramin Naghdi, Ahmad Solgi, Parviz Rahmani, Petros A. Tsioras

“…The number of passes, machine type, and travel speed all show significant effects (p < 0.05) on bulk density, total porosity, and rut depth. …”
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Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

“…To tackle this, this research examined the tensile strength behavior of fiber-reinforced rubberized recycled aggregate concrete (FR3C) using nine machine learning (ML) models. In this study, nine machine learning models—Random Forest, K-Nearest Neighbors, Support Vector Regression, Decision Tree, Artificial Neural Network, AdaBoost, Gradient Boost, CatBoost, and Extreme Gradient Boost—were trained and tested using a dataset of 346 samples representing various mix proportions. …”
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Advancing Water Quality Management: An Integrated Approach Using Ensemble Machine Learning and Real-Time Interactive Visualization by Jigna K. Pandya, Suresh S. Khandelwal, Rupesh Kumar Tipu, Kartik S. Pandya

“…A comprehensive dataset of water quality parameters was collected and preprocessed to ensure data integrity. …”
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Nondestructive discrimination of advanced clones and cultivars of strawberry using an innovative approach involving image analysis and machine learning by Ewa Ropelewska, Agnieszka Masny

“…As many as 2172 image parameters were extracted from the image of each fruit converted to different color channels R, G, B, L, a, b, X, Y, Z, U, V, and S and textures with the highest discriminative power were selected to develop models using various machine learning algorithms, such as Multilayer Perceptron, MultiClass Classifier, IBk, and LMT, Linear Discriminant, Quadratic SVM, Subspace Discriminant, and Wide Neural Network. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…Pearson correlation analysis was used to identify the parameters affecting mechanical and thermal properties. …”
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Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring by Joseph Farah, Jordy Davelaar, Daniel Palumbo, Michael Johnson, Jonathan Delgado

“…However, extraction of black hole parameters from observations of the n = 1 subring is not trivial. …”
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Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries by Harish Venu, Manzoore Elahi M. Soudagar, Tiong Sieh Kiong, N. M. Razali, Hua-Rong Wei, Armin Rajabi, V. Dhana Raju, T. M. Yunus Khan, Naif Almakayeel, Erdem Cuce, Huseyin Seker

“…Abstract This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. …”
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Distribution Ratio Prediction of Major Components in 30%TBP/kerosene-HNO3 System Based on Machine Learning by YU Ting1, ZHANG Yinyin2, ZHANG Ruizhi3, JIN Wenlei2, LUO Yingting2, ZHU Shengfeng3, HE Hui1, YE Guoan1, GONG Helin4

“…The machine learning model prediction results show that the machine learning method proposed in this paper achieves better performance than the traditional distribution ratio mathematical model, effectively improves the accuracy of uranium distribution ratio prediction, and performs well in plutonium and HNO3 distribution ratio prediction.…”

Cross-Scale Feature Blending Model for Surface Defect Identification in Machine Tool Elements Resilient to Contaminant Interference by Dong Wu, Chunhua Guo, Renpu Li, Zhigang Ma

“…Ball screw drives (BSDs) play a crucial role in various industrial applications, particularly in CNC machines. However, they are vulnerable to external variables potentially leading to premature wear, degradation, and failure. …”
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Machine Learning-Enabled Prediction and Mechanistic Analysis of Compressive Yield Strength–Hardness Correlation in High-Entropy Alloys by Haiyu Wan, Baobin Xie, Hui Feng, Jia Li

“…This work employs an integrated machine learning framework to investigate the compressive yield strength (σ_y) and hardness (HV) correlation across a dataset of cast HEAs. …”
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Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…To address these challenges and improve our ability to predict and manage wetland soil properties, this study employs an in situ Internet of Things (IoT)‐based monitoring network and a interpretable, cascaded machine learning model to predict these critical soil parameters. …”
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Hypothermic Machine Perfusion Is Associated with Improved Short-Term Outcomes in Liver Transplantation: A Retrospective Cohort Study by Alexandru Grigorie Nastase, Alin Mihai Vasilescu, Ana Maria Trofin, Mihai Zabara, Ramona Cadar, Ciprian Vasiluta, Nutu Vlad, Bogdan Mihnea Ciuntu, Corina Lupascu Ursulescu, Cristina Muzica, Irina Girleanu, Iulian Buzincu, Florin Iftimie, Cristian Dumitru Lupascu

“…Introduction: Liver transplantation remains the definitive treatment for end-stage liver disease but faces critical challenges including organ shortages and preservation difficulties, particularly with extended criteria donor (ECD) grafts. Hypothermic machine perfusion (HMP) represents a promising alternative to traditional static cold storage (SCS). …”
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