Adaptability of Yuanjiang River Valley <i>Danaus genutia</i> to Different Host Plants in Yunan by Jun Yao, Ting Du, Yangyang Li, Chengli Zhou, Lei Shi

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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. …”
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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

“…The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. …”
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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…Four ML models are trained: Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Artificial Neural Networks (ANN). …”
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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…Venant’s system of equations is built to generate the feature dataset, which is then combined with the random forest algorithm to create a nonlinear prediction model. …”
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The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection by Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li, Du Chen

“…For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). …”
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Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities by Tengyun Hu, Tuo Chen, Guojiang Yu, Meng Zhang, Yan Ding, Han Liu, Yu Wang

“…Then, by combining the random forest regression method with data on vacated building spaces, we developed strategies to optimize and simulate the layout for various reuse functions, including recultivation and regreening in the planned non-construction areas, as well as residential, industrial, and public service facilities in planned construction areas. …”
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Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics by W. J. Riley, J. Tao, Z. A. Mekonnen, R. F. Grant, E. L. Brodie, E. Pegoraro, M. S. Torn

“…Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control‐plot CO2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in Fs and decreases in fine root biomass. …”
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Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado, João Manuel R. S. Tavares

“…The simulation results indicate that combining the post-processing scheme with deep learning improves the Matthews correlation coefficient (MCC) by approximately 5.7% compared to the baseline method. …”
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Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives by Nicolás Cabrera, Jose R. Mora, Edgar A. Marquez

“…In this sense, a modeling evaluation of the inhibition of Pin1 using quinazoline, benzophenone, and pyrimidine derivatives was performed by using multilinear, random forest, SMOreg, and IBK regression algorithms on a dataset of 51 molecules, which was divided randomly in 78% for the training and 22% for the test set. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…<h4>Methods</h4>We conducted a plasmode simulation study using data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2013 to 2018. …”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…These digital twins can continuously simulate and predict health outcomes based on real-time WBAN data, enabling proactive healthcare interventions. …”
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An efficient retrieval method on Google Earth Engine and comparison with hybrid methods: a case study of leaf area index retrieval by Sijia Li, Zhiguang Tang, Kaisen Ma, Zhenyi Wang, Wenjuan Li

“…The performances of LUT and hybrid methods, including random forest (RF), gradient boosting regression tree (GBRT), classification and regression tree (CART), support vector regression (SVR), and Gaussian process regression (GPR), were evaluated on GEE by simulation experiments. …”
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Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model by Ngoc-Kim-Khanh Nguyen, Quang Nguyen, Hai-Ha Pham, Thi-Trang Le, Tuan-Minh Nguyen, Davide Cassi, Francesco Scotognella, Roberto Alfieri, Michele Bellingeri

“…We demonstrate this approach by simulating two effective node attack strategies, i.e., the recalculated degree (RD) and initial betweenness (IB) node attack strategies, and predicting network robustness by using two machine learning models, multiple linear regression (MLR) and the random forest (RF) algorithm. …”
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Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication by Jayant Kumar Rai, Swati Yadav, Ajay Kumar Dwivedi, Vivek Singh, Pinku Ranjan, Anand Sharma, Somesh Kumar, Stuti Pandey

“…The proposed antenna is optimized through the different ML algorithms Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). …”
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Rapid Path Planning Algorithm for Percutaneous Rigid Needle Biopsy Based on Optical Illumination Principles by Jian Liu, Shuai Kang, Juan Ren, Dongxia Zhang, Bing Niu, Kai Xu

“…Furthermore, we employ a random forest-based method to model clinician preferences. …”
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Spatiotemporal Dynamics and Multi-Scenario Projections of the Land Use and Habitat Quality in the Yellow River Basin: A GeoDetector-PLUS-InVEST Integrated Framework for a Coupled H... by Xiuyan Zhao, Jie Li, Fengxue Ruan, Zeduo Zou, Xiong He, Chunshan Zhou

“…This study combines land use, climate, and socio-economic data with spatial–statistical models (GeoDetector [GD]–Patch-generating Land Use Simulation [PLUS]–Integrated Valuation of Ecosystem Services and Trade-Offs [InVEST]) to analyze land use changes (2000–2020), evaluate habitat quality, and simulate scenarios to 2040. …”
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Palaeoclimatic Signatures Based on Pollen Fingerprints: Reconstructing Mid–Late Holocene Climate Dynamics in Northwestern Himalaya, India by Anupam Nag, Anjali Trivedi, Anjum Farooqui, P. Morthekai

“…The palynological analysis provides insight into the palaeovegetation and palaeoclimatic dynamics of a subtropical, dense, mixed deciduous forest, predominantly characterized by Sal (<i>Shorea robusta</i>). …”
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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…This study examines three ML models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB). …”
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Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs by Sayed Gomaa, Ahmed Ashraf Soliman, Mohamed Mansour, Fares Ashraf El Salamony, Khalaf G. Salem

“…Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. …”
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