A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

“…The proposed model is evaluated and compared to other base-line models, Linear Regression (LR), Logistic Regression (LogR), and K Nearest Neighbor (KNN) regression in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Adjusted R-Squared (AR²). …”
Get full text

Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Moreover, over the growing season, when corn exhibits high chlorophyll content and increased resilience to environmental stressors, the correlation between canopy spectrum and root zone soil moisture weakens. Error analysis revealed the lowest relative estimation errors in non-irrigated plots at a 30 cm depth, aligning with periods of elevated water stress at shallower levels, which drove deeper root growth and strengthened the canopy reflectance relationship. …”
Get full text

Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters by Raed H. Allawi, Watheq J. Al-Mudhafar, Mohammed A. Abbas, David A. Wood

“…The test subgroup's measured and predicted ROP mismatch was assessed using root mean square error (RMSE) and coefficient of correlation (R2). …”
Get full text

Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

“…Nevertheless, current prediction models suffer from low accuracy and high prediction errors. Therefore, this paper utilizes a two-step procedure. …”
Get full text

Integrating machine learning-based classification and regression models for solvent regeneration prediction in post-combustion carbon capture: An absorption-based case by Farzin Hosseinifard, Mostafa Setak, Majid Amidpour

“…The Random Forest model, optimized via Grid Search Cross-Validation, delivered the most accurate results, achieving an R² of 0.942, a Mean Absolute Error (MAE) of 0.028, and a Mean Squared Error (MSE) of 0.004. …”
Get full text

Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

“…In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. …”
Get full text

Calibration of Low-cost Gas Sensors for Air Quality Monitoring by Dimitris Margaritis, Christos Keramydas, Ioannis Papachristos, Dimitra Lambropoulou

“…The three alternative methodologies had similar calibration performance overall. The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants.…”
Get full text

Meat attachment or health awareness? Understanding the factors influencing the consumption of local red meat in Italy by Riccardo Testa, Francesco Vella, Giuseppina Rizzo, Giorgio Schifani, Giuseppina Migliore

Get full text

An Improved Tree Crown Delineation Method Based on a Gradient Feature-Driven Expansion Process Using Airborne LiDAR Data by Jiaxuan Jia, Lei Zhang, Kai Yin, Uwe Sörgel

“…The erroneous situation of “match but over-grow” is significantly reduced, regardless of forest conditions. Compared to other methods, our method demonstrates a notable increase in the precisely matched rate across different plot types, with an average increase of 25% in broadleaf plots, 18% in coniferous plots, 23% in mixed plots, 15% in high-density plots, and 32% in medium-density plots, without increasing over- and under- segmentation errors. …”
Get full text

Data driven decisions in education using a comprehensive machine learning framework for student performance prediction by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Zeeshan Babar, Abeer Aljohani, Muhammad Arif

“…Model evaluation was conducted using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), demonstrating the robustness of the proposed approach. …”
Get full text

Fuzzy One-Class Classification Model Using Contamination Neighborhoods by Lev V. Utkin

Get full text

Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow by Arash Rasaizadi, Seyedehsan Seyedabrishami

“…The results show that for both short-term and mid-term models, the least prediction error is obtained by the XGBoost model. In mid-term models, the root mean square error of the XGBoost for the Saveh to Tehran direction and Tehran to Saveh direction is 521 and 607 (veh/hr), respectively. …”
Get full text

Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Eight supervised ML algorithms were evaluated: Linear Regression, Ridge, Lasso, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost. Model performance was assessed using R², Mean Absolute Error (MAE), and Mean Squared Error (MSE). …”
Get full text

FOLU-Net: A novel framework using long short-term memory networks to predict future forestry and other land use by Sanchali Banerjee, Paige T. Williams, Randolph H. Wynne

“…The objective of this study is to predict future tropical forest cover presence and types using multitemporal imaging spectroscopy data. …”
Get full text

Design and Initial Validation of an Infrared Beam-Break Fish Counter (‘Fish Tracker’) for Fish Passage Monitoring by Juan Francisco Fuentes-Pérez, Marina Martínez-Miguel, Ana García-Vega, Francisco Javier Bravo-Córdoba, Francisco Javier Sanz-Ronda

Get full text

Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques by Sahas V. Swamy, Bijay Mihir Kunar, Karra Ram Chandar, Mamdooh Alwetaishi, Shashikumar Krishnan, Sudhakar Reddy

“…Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). …”
Get full text

The Correlation of Microscopic Particle Components and Prediction of the Compressive Strength of Fly-Ash-Based Bubble Lightweight Soil by Yaqiang Shi, Hao Li, Hongzhao Li, Zhiming Yuan, Wenjun Zhang, Like Niu, Xu Zhang

“…The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. …”
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

Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…The proposed GBR model accurately predicts 1847 experimental datasets, showcasing mean squared error, mean absolute error, root mean squared error, relative absolute error percent, and regressing coefficient, of 0.06, 0.15, 0.24, 6.46%, and 0.9961 respectively. …”
Get full text