Estimation of state of health for lithium-ion batteries using advanced data-driven techniques by Smitanjali Rout, Sudhansu Kumar Samal, Demissie Jobir Gelmecha, Satyasis Mishra

“…A comprehensive comparison using performance metrics such as root mean squared error, mean absolute error, and R2 scores highlights the LSTM model’s superiority while evaluating the suitability of other approaches. …”
Get full text

Predicting laboratory solution kit accuracy using artificial intelligence: a data-driven approach by Abdulwahab AL-Deib, Eshraq Alsherif, Husameddin Abuzgaia, Amel Rabti, Ruwaida Rtemi, Salem Elfard, Sheren Njaim

“…The study highlights AI’s transformative role in laboratory standardization, particularly through machine learning (ML) techniques like linear regression and random forests. Challenges like data quality, understanding the model, and following regulations were tackled using explainable AI (XAI) and strict validation methods. …”
Get full text

A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…For performance validation, forecasting accuracy metrics were constructed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), R-squared (R2), and cross-validated root mean squared error (CVRMSE). …”
Get full text

Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye... by Yasemin Ayaz Atalan, Hasan Şahin, Abdulkadir Keskin, Abdulkadir Atalan

“…The RF algorithm performed best with the lowest mean absolute percentage error (MAPE, 0.084%), mean absolute error (MAE, 0.035), root mean square error (RMSE, 0.063), and mean squared error (MSE, 0.004) values in the test dataset. …”
Get full text

Electric vehicle charging station demand prediction model deploying data slotting by A.V. Sreekumar, R.R. Lekshmi

“…The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. …”
Get full text

Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…Model fit statistics, including coefficient of determination (R2), adjusted R2, root of mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), Mean Absolution Error (MAE) and Relative Absolution Error (RAE), were used to evaluate the prediction abilities of the fitted models. …”
Get full text

Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model by Songsong Wang, Bo Peng, Ouguan Xu, Yuntao Zhang, Jun Wang

“…The traditional hydrodynamic and manual forecasting methods have high error rates for hourly forecasting. In order to improve the accuracy and real-time of water level forecasting in small watershed, we extract effective disaster-causing information, integrate multi-dimensional disaster-causing factors (such as hydrology, meteorology, geography, etc.), use a short-term prediction window and loop multi-step input method to improve the Machine Learning (ML) regression models’ accuracy, which can reduce the ML model’s process error. …”
Get full text

A framework based on mechanistic modelling and machine learning for soil moisture estimation by Sabri Kanzari, Sana Ben Mariem, Samir Ghannem, Safouane Mouelhi, Hiba Ghazouani, Bechir Ben Nouna

“…These values were used as inputs to the Hydrus 1D model to generate soil water content profiles over a 27-year period. 109 profiles were calculated using machine learning algorithms (regression trees; random forest; support vector machine) to predict soil moisture content from daily values of rainfall and evaporation. …”
Get full text

Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy by Chengfeng Zhou, Wei Jiang, Qingzheng Cheng, Brian K. Via

Get full text

Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
Get full text

Ensemble machine learning model for forecasting wind farm generation by A. G. Rusina, Osgonbaatar Tuvshin, P. V. Matrenin, N. N. Sergeev

“…This study is carried out by ensemble algorithms, such as Random Forest, AdaBoost and XGBoost, which are one of the machine learning approaches. …”
Get full text

Implementation of Machine Learning in Flat Die Extrusion of Polymers by Nickolas D. Polychronopoulos, Ioannis Sarris, John Vlachopoulos

“…The dataset was used to train and evaluate the following three powerful machine learning (ML) algorithms: Random Forest (RF), XGBoost, and Support Vector Regression (SVR). …”
Get full text

Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…Among these, XGBoost demonstrated the highest overall accuracy, achieving an R² value of 0.9926, with low root mean square error (RMSE) and mean absolute error (MAE) of 0.0655 and 0.0191, respectively. …”
Get full text

Machine learning approach for optimizing usability of healthcare websites by Amandeep Kaur, Jaswinder Singh, Satinder Kaur

“…Key metrics such as R-square, Mean Square Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) confirmed the model’s predictive accuracy. …”
Get full text

Applying Machine Learning on Big Data With Apache Spark by Elias Dritsas, Maria Trigka

“…The study employed key performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for regression and Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) for classification. …”
Get full text

Machine learning analysis of breast cancer treatment protocols and cycle counts: A case study at Mohammed vi hospital, Morocco by Houda AIT BRAHIM, Salah EL-HADAJ, Abdelmoutalib METRANE

“…The second model, based on Random Forest Regressor algorithm, which integrates the results of the first model during the training, predicted the treatment cycle of patients with a Root Mean Square Error (RMSE) score of 0.050 and a Mean Absolute Percentage Error (MAPE) score of 0.020. …”
Get full text

MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…Hyperparameters for the XGBoost model were fine-tuned using grid search techniques, resulting in optimal settings that significantly enhanced predictive accuracy with Mean Absolute Error (MAE) ranging from 0.016 – 0.757m and Root Mean Square Error (RMSE) ranging from 0.051 - 2.859m. …”
Get full text

On Predictive Modeling for the Al2O3 Data Using a New Statistical Model and Machine Learning Approach by Mahmoud El-Morshedy, Zahra Almaspoor, Gadde Srinivasa Rao, Muhammad Ilyas, Afrah Al-Bossly

“…Using the same data set, we implement various machine learning approaches including the support vector machine (SVR), group method of data handling (GMDH), and random forest (RF). To evaluate their forecasting performances, three statistical measures of accuracy, namely, root-mean-square error (RMSE), mean absolute error (MAE), and Akaike information criterion (AIC) are computed.…”
Get full text

Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete by Xiao Wu, Daoyong Zhu, Qin Yan

“…Also, the models’ results indicate that RFORF outperforms another model, with −34% lower values in symmetric mean absolute percentage error (SMAPE) and a significant −80% reduction in mean squared logarithmic error (MSLE). …”
Get full text

Construction of a NOx Emission Prediction Model for Hybrid Electric Buses Based on Two-Layer Stacking Ensemble Learning by Jiangyan Qi, Xionghui Zou, Ren He

“…The evaluation metrics of the proposed model—mean absolute error, root mean square error, and coefficient of determination—are 0.0068, 0.0283, and 0.9559, respectively, demonstrating a significant advantage compared to other benchmark models.…”
Get full text