Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model by Lin Zhu, Benao Xing, Xingbao Li, Min Chen, Minping Jia

“…The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%. …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Research investigations demonstrate that the ELNET-BDE model attains significantly lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than contesting Machine Learning (ML) algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). …”
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Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns by Zhe Wang, Xiang Que, Meifang Li, Zhuoming Liu, Xun Shi, Xiaogang Ma, Chao Fan, Yan Lin

“…Observations also disclose significant spatial trends, showing elevated LD incidence rates in locales with vast, uninterrupted deciduous forests, alongside contributions from wetland ecosystem-related variables to the rise in disease occurrence. …”
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Bayesian surrogate assisted neural network model to predict the hydrogen storage in 9-ethylcarbazole by Ahsan Ali, Mohammad Usman, Hafiz Muhammad Ali, Uzair Sajjad, Md. Abdul Aziz, M. Nasiruzzaman Shaikh

“…The error density curve centered around zero emphasized the model’s accuracy and uniform error distribution.…”
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Group-Specific SVM With Bilevel Programming Methods for Parameter Optimization and Explainable AI in Urban Quality of Life Prediction by Gonzalo Rios-Vasquez, Hanns De La Fuente-Mella, Jose Ceroni-Diaz

“…The proposed approach is benchmarked against Linear Regression, Regression Tree, Random Forest, and Gradient Boosting models. The evaluation is conducted using a cross-validation procedure computing the Mean Absolute Error, the Mean Squared Error, and the Mean Absolute Percentage Error as performance metrics. …”
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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

“…MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. …”
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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…<span class="inline-formula"><i>R</i>²=0.633</span>, 0.633, 0.721, 0.637, 0.768, and 0.609), coefficient of variation (CV % <span class="inline-formula">=</span> 6.35 and 5.14, <span class="inline-formula"><i>P</i><i>&lt;</i>0.01</span>), mean square error (MSE <span class="inline-formula">=</span> 3.296, 3.296, 2.904, 4.461, 2.277, and 4.121), root mean square error (RMSE <span class="inline-formula">=</span> 1.815, 1.815, 1.704, 2.112, 1.509, and 2.030), mean absolute error (MAE <span class="inline-formula">=</span> 1.409, 1.409, 1.279, 1.702, 1.193, and 1.628), and mean absolute percentage error (MAPE <span class="inline-formula">=</span> 3.925, 3.925, 3.578, 4.002, 3.335, and 3.967), between actual and predicted values of live body weight. …”
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Research on Export Oil and Gas Concentration Prediction Based on Machine Learning Methods by Xiaochuan Wang, Baikang Zhu, Huajun Zheng, Jiaqi Wang, Zhiwei Chen, Bingyuan Hong

“…Both models demonstrate that the Random Forest method is more effective in predicting the exported oil and gas concentration with multiple-parameter inputs, providing a relevant basis for subsequent control of exported oil and gas concentration.…”
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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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TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations by Hamad Almujibah, Abdulrazak H. Almaliki, Caroline Mongina Matara, Adil Abdallah Mohammed Elhassan, Khalaf Alla Adam Mohamed, Mudthir Bakri, Afaq Khattak

“…The TPE-tuned LCE model outperformed benchmark algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multiple Linear Regression (MLR) achieved the lowest Mean Absolute Error (MAE) of 1.94, Mean Squared Error (MSE) of 21.50, Root Mean Squared Error (RMSE) of 4.64, Residual Standard Ratio (RSR) of 0.38, and the highest Coefficient of Determination (R2) of 0.87. …”
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Large-scale groundwater pollution risk assessment research based on artificial intelligence technology: A case study of Shenyang City in Northeast China by Lingjun Meng, Yuru Yan, Haihua Jing, Muhammad Yousuf Jat Baloch, Shouying Du, Shanghai Du

“…Compared with the Artificial Neural Network (ANN) and Random Forest (RF) models, the performance evaluation parameters mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) are closer to 0, and the coefficient of determination (R2) is closer to 1. …”
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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

“…., feed-forward neural networks (FNN), extreme gradient boosting (XGBoost), and random forest (RF). Using a dataset of 14,610 solvents (14,580 after data cleaning) and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error (MAE), root mean squared error (RMSE), and mean relative error (MRE). …”
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On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska by Cecilia Borries-Strigle, Uma S. Bhatt, Peter A. Bieniek, Mitchell Burgard, Eric Stevens, Heidi Strader, Richard L. Thoman, Alison York, Robert H. Ziel

“…Variables from these forecasts are used to calculate Buildup Index (BUI), an operationally used fire weather index from the Canadian Forest Fire Danger Rating System. The BUI outlooks are evaluated based on Alaska wildfire subseason, BUI tercile, and predictive service area subregion with the area under the ROC curve (AUROC), Heidke, and mean squared error (MSE) skill scores. …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…According to the model&#x2019;s results, the particle size classification accuracy is 91.67%, the F1 score is 0.92, the coefficient of determination (R2) for the water content regression is 0.89023, the mean squared error (MSE) is 0.00082, the root mean square error (RMSE) is 0.02872, and the mean absolute error (MAE) is 0.01558. …”
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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…The results showed that the prediction performance validated using the test dataset indicated that RF had the lowest values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percent Error (MAPE). …”
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Bayesian optimization with Optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization by Bamidele A. Dada, Nnamdi I. Nwulu, Seun O. Olukanmi

“…The concordance correlation coefficient (CCC), R-squared (R²), and mean absolute percentage error (MAPE) increased, while the root mean squared error (RMSE) and mean absolute error (MAE) decreased. …”
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A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman

“…This study employs various statistical metrics to evaluate the predictive ability across both short-term noise and long-term trends, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Statistic (DS). …”
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Interpretable machine learning approach for TBM tunnel crown convergence prediction with Bayesian optimization by Wanrui Hu, Kai Wu, Kai Wu, Heng Liu, Weibang Luo, Xingxing Li, Peng Guan

“…The results indicate that the LightGBM model achieved the best prediction performance on the test set, with root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient values of 0.9122 mm, 0.6027 mm, 0.0644, and 0.9636, respectively; the average SHAP values for the six input features of the LightGBM model were ranked as follows: Time (0.1366) &gt; Rock grade (0.0871) &gt; Depth ratio (0.0528) &gt; Still arch (0.0200) &gt; Saturated compressive strength (0.0093) &gt; Rock quality designation (0.0047). …”
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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River by Shuguang Li, Sultan Noman Qasem, Shahab S. Band, Rasoul Ameri, Hao-Ting Pai, Saeid Mehdizadeh

“…Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. …”
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A Robust Regression-Based Modeling to Predict Antiplasmodial Activity of Thiazolyl–Pyrimidine Hybrid Derivatives against <i>Plasmodium falciparum</i> by Kevin S. Umoette, Charles O. Nnadi, Wilfred O. Obonga

“…The models were evaluated using R², mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), <i>p</i>-values, <i>F</i>-statistic, and variance inflation factor (VIF). …”
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