Sperm swarm optimization for many objective power flow problems with enhanced performance evaluation in power systems by Wulfran Fendzi Mbasso, Ambe Harrison, Pradeep Jangir, Idriss Dagal, Hossam Kotb, Njimboh Henry Alombah, Raman Kumar, Aseel Smerat, Emmanuel Fendzi Donfack, Saad F. Al-Gahtani, Z. M. S. Elbarbary

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LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance by Nicole Pascucci, Donatella Dominici, Ayman Habib

“…DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. …”
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A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms by Yuan Zhang, Zhekui Fan, Wenjia Yan, Chentian Ge, Huasheng Sun

“…With the optimal model, three red-edge bands of Landsat OLI were subsequently obtained in alignment with their derived vegetation indices. …”
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Acoustic emission approach to optimize friction stir additive manufacturing process for magnesium alloy ZE41 by Divya Zindani, Divya Zindani, A. K. Lakshminarayanan, A. K. Lakshminarayanan, A. Joshua, A. Joshua, Robert Čep, K. Logesh

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Machine-learning-driven prediction of flow curves and development of processing maps for hot-deformed Ni–Cu–Co–Ti–Ta alloy by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, M.R. Rahul, Sumanta Samal, Eunhyo Song, Younggeon Lee, Yongho Jeon, Krishanu Biswas, Gandham Phanikumar, Byungmin Ahn

“…To reduce experimental efforts and enhance prediction accuracy, five machine learning (ML) models random Forest (RF), XGBoost (XGB), decision tree (DT), K-Nearest neighbor (KNN), and gradient boosting (GB) were applied to predict the flow stress–strain response and construct processing maps. …”
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Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 by Joshua Veli Tampubolon, Rinaldy Dalimi, Budi Sudiarto

“…We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. …”
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Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator by Vasileios I. Vlachou, Theoklitos S. Karakatsanis

“…The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. …”
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A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning by Junyao Ding, Jianchao Du, Hejie Wang, Song Xiao

“…Abstract The data acquisition methods are becoming increasingly diverse and advanced, leading to higher data dimensions, blurred classification boundaries, and overfitting datasets, affecting machine learning models’ accuracy. Many studies have sought to improve model performance through feature selection. …”
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Can green financial policy drive urban carbon unlocking efficiency? A causal inference approach based on double machine learning by Weixin Tang, Qihao Zhou

“…Utilizing panel data from 267 Chinese cities spanning 2011 to 2022 and treating the GFRIPZ policy as a quasi-natural experiment, this study employs a double machine learning (DML) model to empirically investigate the impact of green finance policy on urban carbon unlocking efficiency. …”
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Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture by Bhavneet Gulati, Zainab Zubair, Ankita Sinha, Nikita Sinha, Nupoor Prasad, Manoj Semwal

“…The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in <i>Mentha arvensis</i> using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. …”
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Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis by Shiqiu Liu, Li Liang

“…This study facilitates the data-driven design of novel cast TiAl alloys by systematically investigating the critical elements and their interactions affecting room-temperature (RT) tensile properties by the machine learning method based on SHAP analysis. Comparative analysis of three algorithms within the training dataset proved the random forest regression (RFR) as the optimal modeling approach. …”
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Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning by Nicolò Meneghetti, Michael Lassi, Verediana Massa, Silvestro Micera, Alberto Mazzoni, Claudia Alia, Andrea Bandini

“…In this study, we investigated the predictive power of local field potentials recorded 2 days post-stroke to forecast 1 month motor recovery in a mouse model of ischemic stroke. By employing a comprehensive machine learning approach, we identified key electrophysiological features that significantly enhanced prediction accuracy. …”
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2D flame temperature and soot concentration reconstruction from partial discrete data via machine learning: A case study by Mingfei Chen, Renhao Zheng, Xuan Zhao, Dong Liu

“…For the reconstruction of the temperature fields in Cases 1–3, the predicted values from the optimal RF model closely matched the measurement. …”
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A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery by Sveinn E. Armannsson, Magnus O. Ulfarsson, Jakob Sigurdsson

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Integration of machine learning and genome‐wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.) by Jinghan Peng, Xiong Lei, Tianqi Liu, Yi Xiong, Jiqiang Wu, Yanli Xiong, Minghong You, Junming Zhao, Jian Zhang, Xiao Ma

“…GP studies were conducted using the classical model genomic best linear unbiased prediction (GBLUP) and six ML models. …”
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Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China by Shaowu Lin, Sicheng Li, Ya Fang

“…The five most important gait parameters in the optimal model were left step height, walking speed, right step height, body sway, and step width. …”
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Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches by Junfeng Ge, Lin Zhu, Sijie Jiang, Wenyan Li, Rongzhan Lin, Jun Wu, Fengying Dong, Jin Deng, Yi Lu

“…Furthermore, the Light Gradient Boosting Machine model showed strong performance in predicting advanced CKM staging (AUC: 0.896, 95% CI: 0.882–0.911), while Logistic regression performed better in predicting all-cause mortality (AUC: 0.857, 95% CI: 0.831–0.884). …”
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Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence by Fatma Hilal Yagin, Radwa El Shawi, Abdulmohsen Algarni, Cemil Colak, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. …”
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Analyzing Dispersion Characteristics of Fine Particulate Matter in High-Density Urban Areas: A Study Using CFD Simulation and Machine Learning by Daeun Lee, Caryl Anne M. Barquilla, Jeongwoo Lee

“…Integrating computational fluid dynamics (CFD) simulations with interpretable machine learning (ML) models quantifies PM_2.5 concentrations across various urban configurations. …”
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Design and Testing of a Low-Speed, High-Frequency Straw Chopping and Returning Machine Using a Constant Breath Cam Mechanism by Han Lin, Jin He, Guangyuan Zhong, Hanyu Yang

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