Resource Time Series Analysis and Forecasting in Large-Scale Virtual Clusters by Yue Lin, Jiamin Wen, Xudong Zhang, Yan Liang, Jianjiang Li

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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…It achieved a Mean Absolute Error (MAE) of 0.0218, Mean Squared Error (MSE) of 0.0008, and Root Mean Squared Error (RMSE) of 0.0290, along with an R² score of 0.9625, MAPE of 11.97%, and an Explained Variance Score (EVS) of 0.9521. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…Key performance metrics—namely, mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²)—were employed to assess the efficacy of each model. …”
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Leveraging dense layer hybrid graph neural networks for managing overvoltage in PV-dominated distribution systems by Asif Gulraiz, Syed Sajjad Haider Zaidi, Haseeb Gulraiz, Bilal Muhammad Khan, Majid Ali, Baseem Khan

“…Comparative analysis across multiple deep learning models reveals the HGNN-DL method achieved remarkable predictive accuracy, with the lowest Mean Absolute Error (0.15000), Mean Squared Error (0.00250), and Root Mean Square Error (0.00550), coupled with an exceptional R² value of 0.97000. …”
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Identification of Individual Texas Horned Lizards (Phrynosoma cornutum) Using Genotypes and Ventral Spot Patterns by Daniella Biffi, Mary R. Tucker, Alexis Ackel, Dean A. Williams

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Predicting the Tensile Strength of Plant Leaves Based on GA-SVM by Wei Chang, Meihong Liu, Yayu Huang, Junjie Lei, Kai Wu

“…A comparative analysis with other predictive algorithms demonstrates that the GA-SVM model achieves the lowest prediction error and highest accuracy, with mean absolute error and root mean squared error values of 0.0774 and 0.0745, respectively. …”
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A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness by Somit Jain, Shobhit Agrawal, Eshaan Mohapatra, Kathiravan Srinivasan

“…Conclusions The proposed ARIMA‐LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA‐ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. …”
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Utilization of Unmanned Aerial Vehicle (UAV) for Topographic Survey Using Ground Control Points (GCP) from Geodetic GNSS by Nizamuddin Nizamuddin, Freddy Sapta Wirandha, Ardiansyah Ardiansyah

“…Aerial photos that previously had an error rate of 2-7 meters, after being bound with GCP points, the error rate decreased to below 1 meter. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…The model performances were evaluated based on mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). …”
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Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction by Mahmood Shahabi, Mohammad Ali Ghorbani, Sujay Raghavendra Naganna, Sungwon Kim, Sinan Jasim Hadi, Samed Inyurt, Aitazaz Ahsan Farooque, Zaher Mundher Yaseen

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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…The findings are assessed based on the coefficient of correlation (r), mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MPE), and are contrasted with the Hargreaves-Samani, Jensen-Haise, Linacre, Benavides & Lopez, and Hamon methods, along with the Multilayer Perceptron (MLP) neural network, which is conventionally trained and employs hyperparameter tuning techniques such as Grid Search (MLP-GRID) and Random Search (MLP-RD). …”
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Unveiling the future: Wavelet- ARIMAX analysis of climate and diarrhea dynamics in Bangladesh’s Urban centers by Md. Waliullah, Md. Jamal Hossain, Md. Raqibul Hasan, Abdul Hannan, Mohammad Mafizur Rahman

“…Based on climatic variables, Wavelet-ARIMAX can accurately predict diarrheal occurrence, as indicated by the mean absolute error (MAE), root mean squared error (RMSE), and root mean squared logarithmic error (RMSLE). …”
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Predicting calorific value through proximate analysis of municipal solid waste using soft computing system by Saptarshi Mondal, Islam M. Rafizul

“…Statistical parameters were determined to compare model accuracy, with ANFIS exhibiting the top performance, followed by ANN, and then MLP, which had the lowest values of mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE) at 8.704E-07, 0.00019, 0.00016, and 1.295E-05 respectively. …”
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Forecasting Ionospheric TEC Changes Associated with the December 2019 and June 2020 Solar Eclipses: A Comparative Analysis of OKSM, FFNN, and DeepAR Models by R. Mukesh, Sarat C. Dass, Negash Lemma Gurmu, M. Vijay, S. Kiruthiga, S. Mythili, D. Venkata Ratnam, V. B. S. Srilatha Indira Dutt

“…The reliability of the forecasted results is evaluated using numerical factors like Normalized Root Mean Square Error (NRMSE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. …”
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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring by Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan

“…The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. …”
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SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis by Annisa Mufidah Sopian, Ridwan Ilyas, Fatan Kasyidi, Asep Id Hadiana

“…In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. …”
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Calibration of parameters in microscopic traffic flow simulation models considering micro-meteorological information. by Jian Ma, Yuchen Zhang, Liyan Zhang, Zongwei Gao, Keyi Cao, Qianlong Fu, Zheng Qian

“…While there are existing following models under various weather conditions, research on the specific impact of micro-meteorological factors is insufficient. …”
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An innovative method to determine the stress-dependency of Poisson’s ratio of granitic rocks by Samad Narimani, László Kovács, Balázs Vásárhelyi

“…Additionally, the Poisson’s rate follows a linear increase with stress, up to the point of unstable crack propagation stress. The research demonstrated that the proposed equations provide competent values for the root mean squared error value (ranging from 0 to 0.04), the mean absolute percentage error (ranging from 0.6% to 18%) and the mean absolute error (ranging from 0 to 0.04). …”
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Dybkov model for the estimation of boron diffusion in the FeB/Fe2B bilayer on AISI 316 steel by Naima Hadjadj, Bendaoud Mebarek, Yassine El Guerri, Mourad Keddam

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