Prediction of Electrotactile Stimulus Threshold in Real Time Using Voltage Waveforms Between Electrodes by Vibol Yem, Yasushi Ikei, Hiroyuki Kajimoto

“…Second, we applied Random Forest regression using R and C related data as inputs. …”
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Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models by Yuwei Chen, Yadi Min, Haiqiang Jiang, Jing Luo, Mengxin Liu, Enliang Wang, Xingchao Liu, Ke Shi, Xiaoqi Li

“…Meanwhile, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient(R²) were used to evaluate the accuracy of the models. …”
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Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization by Yu Li, Jingxiao Zhao, Xiucheng Li, Zhao Xing, Qiqiang Duan, Xiaojun Liang, Xuemin Wang

“…The model achieved an Mean Squared Error (MSE) of 972.18, an Mean Absolute Error (MAE) of 24.75 and an Coefficient of Determination (R2) of 0.864 for YS, and an MSE of 812.28, an MAE of 22.87 and an R2 of 0.823 for UTS. …”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. …”
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Exergy efficiency optimization of a water-based titanium dioxide nanofluid hybrid solar collector using advanced machine learning models by Poyyamozhi Natesan, M.P. Rajakumar, Sreevidya R C, Srimanickam B, Suresh Vellaiyan, Nguyen Van Minh

“…Three statistical metrics, such as mean absolute error (MAE), coefficient of determination (R2), and root mean square error (RMSE), denoted as E1, E2, and E3 respectively, were used to evaluate model performance. …”
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Rainfall Prediction Using Integrated Machine Learning Models With K-Means Clustering: A Representative Case Study of Harirud Murghab Basin-Afghanistan by Ziaul Haq Haq Doost, Ali Alsuwaiyan, Abdulazeez Abdulraheem, Nabil M. Al-Areeq, Zaher Mundher Yaseen

“…The models were evaluated at three stations (Nazdik-i Herat, Shinya, and Torghundi) using coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), Mean absolute error (MAE), and median absolute error (MedAE) as evaluation metrics. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…Moreover, the XGBoost model exhibited the highest fitting accuracy and the smallest error in estimating nitrate leaching losses, with an R² value of 0.79 and an average absolute error (<i>MAE</i>) of 3.87 kg/ha. …”
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Leveraging Machine Learning Regression Algorithms to Predict Mechanical Properties of Evaporitic Rocks From Their Physical Attributes by Ayham Zaitouny, Hasan Arman, Anusuya Krishnan, Alaa Ahmed, Ahmed Gad

“…Nine ML models were trained (80:20 data split) and validated using R-squared (R2), mean absolute error (MAE), and root-mean square error (RMSE). Nonlinear correlations demonstrated strong relationships between the mechanical properties and physical attributes, such as saturated density (s) and natural unit weight (n). …”
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Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia by Idris Zubairu Sadiq, Yakubu Saddeeq Abubakar, Abdulkadir Rabiu Salisu, Babangida Sanusi Katsayal, Umar Saidu, Sani I. Abba, Abdullahi Garba Usman

“…Results: The ANN model outperformed the RFR and RLR models, with the lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) of 0.313 and 0.146 respectively. …”
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Predicting axial load capacity in elliptical fiber reinforced polymer concrete steel double skin columns using machine learning by Focai Yu, Haytham F. Isleem, Walaa J. K. Almoghayer, Ramy I. Shahin, Saad A. Yehia, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…Implemented AI techniques include five ML models — Gene Expression Programming (GEP), Artificial Neural Network (ANN), Random Forest (RF), Adaptive Boosting (ADB), and eXtreme Gradient Boosting (XGBoost) — and one DL model — Deep Neural Network (DNN).Due to the scarcity of experimental data on hybrid elliptical DSTCs, an accurate finite element (FE) model was developed to provide additional numerical insights. …”
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The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies by Yuyan Huang, Jian Zhao, Chengxu Zheng, Chuanhui Li, Tao Wang, Liangde Xiao, Yongkuai Chen

“…The results showed that in the test set of the fermentation degree models based on single features, the mean absolute error (MAE) ranged from 4.537 to 6.732, the root mean square error (RMSE) ranged from 5.980 to 9.416, and the coefficient of determination (R²) values varied between 0.898 and 0.959. …”
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Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks by Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav

“…For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. …”
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Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne by Yashun Wang, Huirong Chen, Jianting Gong, Yang Cui, Huiqin Zou, Yonghong Yan

“…The instance-based k-nearest neighbors model based on powder color performed best in predicting the amygdalin content [R2 = 0.9801, mean absolute error (MAE) = 0.2071, root mean squared error (RMSE) = 0.4170], followed by the random committee model in predicting the acid value (R2 = 0.9580, MAE = 1.5121, RMSE = 1.9099) and the random forest model in predicting the peroxide value (R2 = 0.8857, MAE = 0.0027, RMSE = 0.0035). …”
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A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings by Inho Jo, Yunku Lee, Namhyuk Ham, Juhyung Kim, Jae-Jun Kim

“…Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal–Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern ‘type’ has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal–Wallis <i>p</i> < 0.001). …”
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Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application by Chisom Onyenagubo, Yasser Ismail, Radian Belu, Fred Lacy

“…The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. …”
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Machine learning-based technique for directivity prediction of a compact and highly efficient 4-port MIMO antenna for 5G millimeter wave applications by Md Ashraful Haque, Kamal Hossain Nahin, Jamal Hossain Nirob, Md Kawsar Ahmed, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Abeer D. Algarni, Mohammed ElAffendi, Abdelhamied A. Ateya

“…Out of the five ML models, the one that has the greatest accuracy and has a low margin of error when predicting directivity is the Random Forest Regression model. …”
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Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

“…However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. …”
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Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients by Keyu Chen, Chuhui Wang, Yu Wei, Sinan Ma, Weijia Huang, Yalin Dong, Yan Wang

“…In the testing set, AUC24 was predicted using all models, and performance was evaluated using mean absolute error, mean squared error, root mean squared error, mean absolute percentage error (MAPE), and R². …”
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Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment by Yu-Qi Wang, Wenchong Tian, Hao-Lin Yang, Yun-Peng Song, Jia-Ji Chen, Qiong-Ying Xu, Wan-Xin Yin, Le-Qi Ding, Xi-Qi Li, Han-Tao Wang, Ai-Jie Wang, Hong-Cheng Wang

“…Leveraging multi-stage water quality parameters, we developed a machine learning (ML) framework for coagulation control that incorporates knowledge embedding (KE) through hyper-parametric constraints on threshold water quality, energy consumption, and economic costs. Random forest (RF) has the best performance among the eight methods with a percentage error of 2.53% and a coefficient of determination of 0.9922. …”
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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…The results indicate that Ensemble Learning and AdaBoost yield the highest accuracy algorithms in prediction capability as they tend to illustrate the lowest values of mean squared error and mean absolute relative error (%) and highest R-squared values. …”
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