Machine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study by Sun Q, Liu Z, Ding T, Shi C, Hou N, Sun C

“…Feature selection was performed using the Lasso algorithm and Pearson correlation coefficient, and a random forest regression model was constructed. Model performance was evaluated using mean square error (MSE), coefficient of determination (R²), Pearson linear correlation coefficient (PLCC), Spearman rank correlation coefficient (SRCC), and Kendall rank correlation coefficient (KRCC).Results: After feature selection, three key features were retained: main pulmonary artery CT value, ascending aorta CT value, and the difference in noise values between the left and right main pulmonary arteries. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…The applicability of backpropagation (BP) neural network, random forest (RF), convolutional neural network (CNN), and CNN-RF models for remote sensing inversion of PP concentration is assessed through model comparison. …”
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Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…This performance significantly exceeded that of LSTM, Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR), with GRU reducing the root mean squared error (RMSE) by 12.3 %–27.5 % compared to LSTM in long-term predictions. …”
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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

“…Three machine learning models—Random Forest, Naïve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. …”
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Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area by Yunhao Han, Bin Wang, Jingyi Yang, Fang Yin, Linsen He

“…The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. …”
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Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters by Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães

“…In this case, Random Forest presented the best performance, with a lower risk of overfitting. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

“…Model performance validation analysis was evaluated via R-squared (R2), root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) statistical models. …”
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Wind power generation prediction using LSTM model optimized by sparrow search algorithm and firefly algorithm by Wenjing Zhang, Hongjing Yan, Lili Xiang, Linling Shao

“…The model first employs a bidirectional long short-term memory network to capture the long-term dependency features of time series, and uses random forests for nonlinear modeling and feature selection. …”
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SWOT Water Surface Elevation in Herbaceous Wetlands of Florida's Everglades by Solomon Kica, Tamlin M. Pavelsky, Jessica V. Fayne, Brent A. Williams

“…Additional evaluation is needed in shrubby and forested wetlands.…”
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Integration of Genetic Algorithm with Machine Learning for Properties Prediction by Rathachai Chawuthai, Siripan Murathathunyaluk, Nalin Amornratthamrong, Run Arunchaipong, Amata Anantpinijwatna

“…Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…The results show that random forest and ensemble learning algorithms delivered the highest predictive accuracy, evidenced by the most substantial coefficient of determination (R2) and minimal error metrics. …”
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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

“…The study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL). …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. …”
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Evaluation of Soil Moisture Retrievals from a Portable L-Band Microwave Radiometer by Runze Zhang, Abhi Nayak, Derek Houtz, Adam Watts, Elahe Soltanaghai, Mohamad Alipour

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Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes by T. V. Zolotova, D. A. Volkova

“…The mathematical basis of machine learning methods is the Z-score method, the isolation forest method, support vector method for outlier detection, and winsorization and multiple imputation methods for outlier correction. …”
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SS-OPDet: A Semi-Supervised Open-Set Detection Framework for Dead Pine Wood Detection by Xiaojian Lu, Shiguo Huang, Songqing Wu, Feiping Zhang, Mingqing Weng, Jianlong Luo, Xiaolin Li

“…Pine wilt disease poses a significant threat to pine forests worldwide, necessitating efficient and accurate detection of dead pine wood for effective disease control and forest management. …”
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Groundwater estimation and determination of its probable recharge source in the Lower Swat District, Khyber Pakhtunkhwa, Pakistan, using analytical data and multiple machine learni... by Imran Ahmad, Ibrar Ul Haq, Mansoor Ahmad, Iram Gul, Mursaleen Khan, Khushnuma Khushnuma, Ubaid Ullah, Maqsood Ur Rehman, Mohamed Metwaly

“…The study applied six ML models, including random forest, support vector machine (SVM), and ridge Regression, to predict groundwater zones, with random forest achieving the highest accuracy (R2 = 0.95, root mean square error (RMSE) = 8.49, and mean absolute error (MAE) = 4.03), followed by decision tree (R2 = 0.93). …”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…Its evaluation index R2 reaches 0.976 1, and the error between the predicted result and the actual value is the smallest. …”
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Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow by Arash Rasaizadi, Seyedehsan Seyedabrishami

“…First, three base models, including K-nearest neighbors, random forest, and recurrent neural network, are trained. …”
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A Bag-of-Words Approach for Information Extraction from Electricity Invoices by Javier Sánchez, Giovanny A. Cuervo-Londoño

“…The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. …”
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