Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation by Xuqian Bai, Shuailong Fan, Ruiqi Li, Tianjin Dai, Wangye Li, Sumeng Ye, Long Qian, Lu Liu, Zhitao Zhang, Haorui Chen, Haiying Chen, Youzhen Xiang, Junying Chen, Shikun Sun

“…The model is developed based on the soil water balance equation and incorporates multi-source remote sensing data. A random forest algorithm is employed as the core predictive framework. …”
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Position Accuracy Improvement of the Inertial Navigation System using LSTM Algorithm without GNSS Signals by Mohammad Sabzevari, MasoudReza Aghabozorgi Sahaf

“…This system works by modeling errors and correcting them when GNSS signals are absent. …”
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A comparison of various imputation algorithms for missing data. by Jürgen Kampf, Iryna Dykun, Tienush Rassaf, Amir Abbas Mahabadi

“…The subroutines to be compared are predictive mean matching, weighted predictive mean matching, sampling, classification or regression trees and random forests.<h4>Methods</h4>We compare these subroutines on real data and on simulated data. …”
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Comparison of Machine Learning Methods for Calories Burn Prediction by Alfred Tan Jing Sheng, Zarina Che Embi, Noramiza Hashim

“…This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. …”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

“…Then, the thermal compression flow behavior of the as-cast composite was comparatively researched using a traditional Arrhenius model and advanced machine learning methods (Linear Regression, AdaBoost, Random Forest, and XGBoost). The flow stresses were predicted under various thermal operating conditions, and the performance of all models was assessed using root mean square error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). …”
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An overview of AI in Biofunctional Materials by Dazhou Li

“…Case studies include rapid optimization of nanoparticle synthesis via Bayesian frameworks and the discovery of biodegradable stent materials through random forest screening. Despite remaining challenges in data quality and regulatory alignment, these advances underscore AI’s capacity to deliver high-performance, sustainable biomaterials and point toward an interdisciplinary roadmap for next-generation therapeutic solutions.…”
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COMPARISON OF CENTROID METHOD AND TREE STANDARD FORMULA FOR ESTIMATION LOG VOLUME by Mohammed Al-allaf

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Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems by Halbast Rashid Ismael, Adnan Mohsin Abdulazeez, Dathar A. Hasan

“…The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. …”
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A hybrid time series forecasting approach integrating fuzzy clustering and machine learning for enhanced power consumption prediction by Khalaf Alsalem

“…A comparison of Random Forest, Support Vector Machine, K-Nearest Neighbors, Extreme Gradient Boosting, and Multilayer Perceptron models took place through Root Mean Square Error, Mean Absolute Error, and R² metric evaluation. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…The findings indicate that Random Forest outperforms the other algorithms, reaching a coefficient of determination (R2) of 0.96127 during evaluation, with the lowest error metrics. …”
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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…Model performance was evaluated using the coefficient of determination (R2) and mean squared error. ResultsThe random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. …”
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Simulating Land Use and Evaluating Spatial Patterns in Wuhan Under Multiple Climate Scenarios: An Integrated SD-PLUS-FD Modeling Approach by Hao Yuan, Xinyu Li, Meichen Ding, Guoqiang Shen, Mengyuan Xu

“…Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. …”
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Predicting Residential Energy Consumption in South Africa Using Ensemble Models by David Attipoe, Donatien Koulla Moulla, Ernest Mnkandla, Alain Abran

“…The accuracy of each ensemble model was evaluated by assessing various performance indicators, including the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination R2. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. …”
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Evaluation of Noah-MP Land Surface Model-Simulated Water and Carbon Fluxes Using the FLUXNET Dataset by Bofeng Pan, Xiaolu Wu, Xitian Cai

“…For ET, the simulations were most accurate for open shrublands and deciduous broadleaf forests, while showing the largest deviation for woody savannas. …”
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Analysis of Meshing Contact Characteristics of the Gear Transmission System Based on Data Mining Technology by Li Shengjia, Ma Yali, Zhao Yongsheng, Pu Dajun, Yan Shidang

“…The results show that the prediction error of the prediction model based on support vector machine is the smallest, and the average absolute percentage error is 3.87%, which is far less than the theoretical calculation error. …”
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A Comparative Analysis of Machine Learning Models in News Categorization by Mohammad Hossein Zolfagharnasab, Siavash Damari

“…In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. …”
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Prediction of Automotive Wire Harness Aging Based on CNN-biLSTM-Attention by Kun Xia, Qi Zhu, Qingqing Yuan, Jingxia Wang

“…The results show the system achieves a mean absolute error (MAE) of 0.02806, with 32.50% and 62.06% error reduction compared to LSTM and Random Forest models, respectively, demonstrating effective prediction performance.…”
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Prediction of Surrounding Rock Deformation in a Highway Tunnel Using an LSTM-RF Hybrid Model by Chen Yintao, Shao Xin, Chang Xiangyu, Siti Norafida Bt. Jusoh, Lu Zhongxiang, Bao Hong Quan, Han Xinkai, Xu Jun

“…This study introduces a novel hybrid model integrating long short-term memory (LSTM) networks and random forest (RF) to enhance the precision of tunnel deformation predictions during construction. …”
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BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE by Stevanny Budiana, Felivia Kusnadi, Robyn Irawan

“…The decision tree-based models such as Gradient Boosting Machine, Random Forest, Decision Tree, and Bayesian Additive Regression Tree model are compared to find the best model by comparing mean squared error and program runtime. …”
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