Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). …”
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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 highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level.   …”
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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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High-throughput 3D phenotyping in northern quahogs Mercenaria mercenaria for dimensional trait measurement and weight prediction by Kai Shen, Xu Wang, Kai Gao, Jayme Yee, Paul McDonald, Huiping Yang

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Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China by Jun Zhu, Zhong-Xiu Sun

“…Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CEC_soil by clay content led to significant overestimation of CEC_clay, with a mean error of 14.42 cmol(+) kg⁻¹. …”
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Developing supervised machine learning algorithms to classify lettuce foliar tissue samples into interpretation zones for 11 plant essential nutrients by Patrick Veazie, Hsuan Chen, Kristin Hicks, Jake Holley, Nathan Eylands, Neil Mattson, Jennifer Boldt, Devin Brewer, Roberto Lopez, Brian Whipker

“…Abstract Greenhouse crop nutrient management recommendations based on foliar tissue testing rely heavily on human interpretation, which can result in recommendation variations and errors. Critical nutrient ranges vary for each species, and the potential for error in interpretation increases due to this complexity. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…These results demonstrate that the PLSR model exhibits strong generalization ability, minimal prediction bias, and low average prediction error, offering a valuable reference for precision control of canopy spraying in orchards.…”
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Improving spatial resolution of Aqua MODIS and GCOM-C chlorophyll-a data for Cyprus coastal waters monitoring by Sofiia Drozd, Nataliia Kussul, Andrii Shelestov

“…Four images from spring-summer 2024 were selected for analysis, with Sentinel-3 spectral bands used as predictors and both multiple linear regression and random forest models applied. The results indicate that linear regression predicts higher coastal Chl-a values, while random forest smooths spatial gradients. …”
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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…Often traditional crop grading methods are inconsistent of errors and tend to be inefficient and suboptimal grading results which cause costs. …”
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Comparative analysis of machine learning algorithms for predicting tibial intramedullary nail length from patient characteristics by Yujian Hui, Hengda Hu, Jinghua Xiang, Xingye Du

“…Results The XGBoost model demonstrated superior clinical precision, achieving the lowest testing RMSE (9.15 mm) and MAE (7.56 mm), with an R2 of 0.871, explaining 87.1% of variance in nail length. While the random forest model had the highest R2 (0.874) and correlation coefficient (r = 0.935), XGBoost outperformed all models in error metrics, critical for minimizing surgical complications. …”
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Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction by Tanveer Alam Munshi, Khanum Popi, Labiba Nusrat Jahan, M. Farhad Howladar, Mahamudul Hashan

“…Regression metrics including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), maximum error (MaxE), and minimum error (MinE) were used to assess the effectiveness of the models. …”
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Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine Learning by Meiyan Zhao, Yuhu Zhang, Tao Hu, Peng Wang

“…First, the V-SVR model performs best with the lowest mean absolute error (MAE) of 0.016 and mean relative error (MRE) of 0.001. …”
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Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. …”
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DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization. by Zhe Li, Lei Shi, Mingyu Pei, Wan Chen, Yutao Tang, Guozheng Qiu, Xibin Xu, Liwen Lyu

“…The DeepSeek AI framework was employed to analyze the data, utilizing clustering analysis, principal component analysis (PCA), and random forest models. Descriptive statistics, error rates, and correlation analyses were performed using R software (version 4.1.2). …”
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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera, Lenganji Simwanda

“…Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. …”
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Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset by Shengdong Ju, Guangzhao Ou, Tao Peng, Yanning Wang, Quanlin Song, Peng Guan

“…The results indicate that: (1) The constructed BO-XGBoost model exhibited exceptionally high predictive accuracy on the test set, with a root mean square error (RMSE) of 7.5603, mean absolute error (MAE) of 3.2940, mean absolute percentage error (MAPE) of 4.51%, and coefficient of determination (R2) of 0.9755; (2) Compared to the predictive performance of support vector mechine (SVR), decision tree (DT), and random forest (RF) models, the BO-XGBoost model demonstrates the highest R2 values and the smallest prediction error; (3) The input feature importance yielded by SHAP is groundwater level (h) > water-producing characteristics (W) > tunnel burial depth (H) > rock mass quality index (RQD). …”
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Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis by G. Uday Kiran, G. Nakkeeran, Dipankar Roy, Sumant Nivarutti Shinde, George Uwadiegwu Alaneme

“…The outcomes from both the training and testing phases demonstrated the strong predictive power of RSM, SVM, GB, ANN, and RF with a criterion used Root Mean square error (RMSE), Mean square error (MSE), Mean Absolute Error (MAE) and correlation coefficient (R). …”
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Plane tree risk assessment in urban space using Artificail Neural Network by Mozdeh Nafian, Mohsen Bahmani, Elham Ghehsareh Ardestani, Ali Soltani

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Prediction of the Characteristics of Concrete Containing Crushed Brick Aggregate by Marijana Hadzima-Nyarko, Miljan Kovačević, Ivanka Netinger Grubeša, Silva Lozančić

“…By testing various minimum leaf sizes and ensemble methods such as Random Forest and TreeBagger, the study evaluates metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). …”
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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

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