Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates by Yuming Shi, Huan Zhong, Jason C. Rogalski, Leonard J. Foster

“…Assuming the causes of MVs could be different in different regions, we then investigated the optimal imputation method in each bin, using normalized root mean square error (NRMSE), and found that the optimal imputation method varies across bins. …”
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Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit by Swellam W. Sharshir, Abanob Joseph, Mohamed S. Abdalzaher, A.W. Kandeal, A.S. Abdullah, Zhanhui Yuan, Huizhong Zhao, Mahmoud M. Salim

“…Besides, four separate train-test splits, 95 %:5 %, 90 %:10 %, 80 %:20 %, and 70 %:30 %, are employed to assess the performance of each regressor in terms of R-squared (R2), mean squared error (MSE), and mean absolute error (MAE). All the models showed prediction accuracy enhancement with increasing the train dataset size. …”
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A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. …”
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Accounting for alternation in temporal quality analysis in MapBiomas Brazil by Ana Paula Matos, Maria Hunter, Robert Gilmore Pontius, Luis Rodrigo Baumann, Leandro Leal Parente, Laerte Guimarães Ferreira

“…Alternation, a newly defined error component, captures the number of land use transitions a location experiences throughout time. …”
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Spatial Accuracy Evaluation for Mobile Phone Location Data With Consideration of Geographical Context by Xiaoqing Song, Yi Long, Ling Zhang, David G. Rossiter, Fengyuan Liu, Wei Jiang

“…In this study, we built a linear evaluation model based on geographical weighted regression (GWR) and a nonlinear evaluation model based on a random forest (RF) to quantify the relationship between geographical factors and the positioning bias of MPL data. …”
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Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites by Khalil Benabderazag, Moussa Guebailia, Zouheyr Belouadah, Lotfi Toubal, Salah Eddine Tachi

“…The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. …”
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An Empirical Analysis of Above-Ground Biomass and Carbon Sequestration Using UAV Photogrammetry and Machine Learning Techniques by Thinnakon Angkahad, Teerawong Laosuwan, Satith Sangpradid, Narueset Prasertsri, Yannawut Uttaruk, Titipong Phoophathong, Joe Nuchthapho

“…This research aims to analyze above-ground biomass and carbon sequestration using unmanned aerial vehicle (UAV) photogrammetry and machine learning methods, focusing on a case study of the dry dipterocarp forest in the Ban Hin Lat and Hin Lat Phatthana Community Forests. …”
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A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting by Jiawen Li, Binfan Lin, Peixian Wang, Yanmei Chen, Xianxian Zeng, Xin Liu, Rongjun Chen

“…It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). …”
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In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning by Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, Nasem Badreldin

“…This study evaluated the performance of three machine learning (ML) models—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR)—for predicting potato N status and examined the impact of feature selection techniques, including Partial Least Squares Regression (PLSR), Boruta, and Recursive Feature Elimination (RFE). …”
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Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach by Xinyu Yang, Qiang Yu, Buyanbaatar Avirmed, Yu Wang, Jikai Zhao, Weijie Sun, Huanjia Cui, Bowen Chi, Ji Long

“…The results indicate the following: (1) significant spatial variation exists, with high-value CUE areas (≥0.7) in the northwest due to favorable climatic conditions, while low-value areas (<0.6) in the east are affected by decreasing precipitation and overgrazing; (2) CUE increased at an annual rate of 1.03%, with a 43% acceleration after the 2005 climate shift, highlighting the synergistic effects of ecological engineering; (3) our findings reveal that the interaction of evapotranspiration and temperature dominates CUE spatial differentiation, with the random forest model accurately predicting CUE dynamics (root mean square error (RMSE) = 0.0819); (4) scenario simulations show the SSP3-7.0 pathway will peak CUE at 0.6103 by 2050, while the SSP5-8.5 scenario will significantly reduce spatial heterogeneity. …”
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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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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…The proposed framework leverages three boosting DT algorithms, Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), and Random Forest (RF) regressors as base learners, operating in parallel. …”
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CFS-MOES Ensemble Model on Metaheuristic Search-Based Feature Selection by Santosini Bhutia, Bichitrananda Patra, Mitrabinda Ray

“…Three classifiers, namely, K-nearest neighbour (KNN), multilayer perceptron (MLP), and random forest (RF), were chosen as the base classifiers based on their F-measure score. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). …”
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Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula by Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

“…This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. …”
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Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques by Harjeet Kaur, Srimanta Gupta, Surya Parkash, Raju Thapa, Arindam Gupta, G. C. Khanal

“…The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…To address these challenges, we propose a novel methodology that combines a genetic algorithm (GA) with Random Forest Cross-Validation (RF-CV) to evaluate input features and select the most relevant subset. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…Three standalone ML models—K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained, with RF achieving R² = 0.963 and XGB achieving R² = 0.946 on the test set. …”
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Estimation of Cylinder Grasping Contraction Force of Forearm Muscle in Home-Based Rehabilitation Using a Stretch-Sensor Glove by Adhe Rahmatullah Sugiharto Suwito P, Ayumi Ohnishi, Tsutomu Terada, Masahiko Tsukamoto

“…The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. …”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…Experiments showed that MBAUNet achieved over 90% precision and recall, with an F1 score of 94.01%, outperforming vanilla UNet, DeepLabV3+, random forest, and support vector machine, while halving the number of parameters and training time compared to UNet. …”
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