Development and calibration of roundabout safety performance functions using machine learning: a case study from Amman, Jordan by Diana Al-Nabulsi, Aya Hassouneh

“…Comparative modeling was conducted using ordinary least squares regression and Random Forest Regressor algorithms. The linear regression model yielded an R 2 of 0.542 with a high sum of squared errors (SSE = 3750.38), underscoring its limited capacity to capture non-linear relationships. …”
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Opportunistic access control scheme for enhancing IoT-enabled healthcare security using blockchain and machine learning by Mohd Anjum, Naoufel Kraiem, Hong Min, Ashit Kumar Dutta, Yousef Ibrahim Daradkeh, Sana Shahab

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Predictive modeling of oil rate for wells under gas lift using machine learning by Famin Ma, Farag M. A. Altalbawy, Pinank Patel, R. Manjunatha, Rishiv Kalia, Shoira Formanova, P. Raja Naveen, Kamal Kant Joshi, Aashna Sinha, Abdolali Yarahmadi Kandahari, Taqi Mohammed Khattab Al-Rubaye, Mohammad Mahtab Alam

“…The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. …”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…A hybrid model of random forest and prophet was also tested. The role of the hybrid model was to combine the forecasting strengths of the Prophet model with the predictive power of the Random Forest model to better capture complex temporal patterns in the data. …”
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Climate Change Analysis in Malaysia Using Machine Learning by Anishalache Subramanian, Naveen Palanichamy, Kok-Why Ng, Sandhya Aneja

“…Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). …”
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Improving Indoor WiFi Localization by Using Machine Learning Techniques by Hanieh Esmaeili Gorjan, Víctor P. Gil Jiménez

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Predictive modeling of coagulant dosing in drilling wastewater treatment using artificial neural networks by Mahyar Kalhormohammadi, Sanaz Khoramipour

“…After conducting sensitivity analysis to select relevant input-output parameters, predictive models were developed using Recurrent Neural Networks (RNN), a hybrid PSO-RNN model, Extreme Learning Machines (ELMs), and Random Forest (RF). Each model was trained, tested, and validated, and their performance was evaluated using correlation coefficient (R) and root mean square error (RMSE). …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…The model performance was evaluated based on coefficient of determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) statistical indices. …”
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High accuracy prediction of Thai rice glycemic index using machine learning by Yusuf Durmus

“…Three models, XGBoost, CatBoost and RandomForest, were employed on a dataset comprising various starch properties. …”
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Cutting-edge approaches to specific energy prediction in TBM disc cutters: Integrating COSSA-RF model with three interpretative techniques by Jian Zhou, Zijian Liu, Chuanqi Li, Kun Du, Haiqing Yang

“…In particular, the Chebyshev map-SSA-RF (CHSSA-RF) model achieves the most satisfactory prediction accuracy among all models, resulting in the highest coefficient of determination R2 and dynamic variance-weighted global performance indicator values (0.9756 and 0.0814) and the lowest values of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) (6.4742, 4.0003, and 20.41%). …”
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Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR by FENG Yu, WU Yunxing, GU Wenjing, PANG Qiong, GU Yanchang, CHEN Siyu

“…The monitoring model of dam deformation is built by using the random forest optimized by whale algorithm for an actual project, and the coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) are introduced to evaluate and compare the excellent performance of the proposed models. …”
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Application of DE-RF and Fuzzy Model in Camber Control of Hot Rolled Strip by WEI Zhipeng, CUI Guimei, PI Lixiang, LI Tianhao

“… In order to solve the problem of slab bending affecting strip quality in 2250mm hot strip rolling process, a method based on data fusion and expert experience is proposed to apply to slab bending control system.Firstly, the differential evolution algorithm is established to optimize the stochastic forest regression model to solve the problem of insufficient accuracy of slab detection lag prediction.The model can effectively predict the bending value of the slab at the exit of the third pass roughing mill, and the estimated error is 96.3% of the slab within the allowable range.Then a fuzzy model is established according to the expert experience and data to solve the manual operation uncertainty problem.The model gets the roll slit tilt value twice respectively, and the experimental results show that the calculated value of the fuzzy model has a small error compared to the actual value and it can provide the roll gap tilt value reliably.Finally, the two roll gap tilt values are added together as the second roll gap tilt value. …”
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SEMP-TA: A Novel Stock Market Prediction Approach Based on Stacking Ensemble Machine Learning for Effective Trend Analysis by Chanda Raj Kumar, S. Manikandan

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Intelligent Damage Prediction During Vehicle Collisions Based on Simulation Datasets by Sheng Liu, Conghao Liu, Xunan An, Xin Liu, Liang Hao

“…The root-mean-square error of the proposed model was 33.94, whereas that of the SVMR model was 68.16. …”
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A web-based machine learning framework for building energy efficiency prediction by B.S.S.V. Ramana, S. Chanikya Kumar, N. Bharath Kumar, Attuluri R. Vijay Babu

“…Visualizations of prediction error distributions further support model interpretability and sensitivity analysis. …”
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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

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Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates by Manzar Masud, Aamir Mubashar, Shahid Iqbal, Hassan Ejaz, Saad Abdul Raheem

“…Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). The performance metrics used to assess and compare the efficiency were the coefficient of determination (R²), mean square error (MSE), and mean absolute error (MAE). …”
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Machine learning approaches for imputing missing meteorological data in Senegal by Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow

“…This study presents the first comprehensive evaluation in West Africa of four imputation methods, Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Ordinary Kriging (OK), applied to six core meteorological variables across Senegal over a ten-year period (2015–2024). …”
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HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data by Tanya Golubchik, Lucie Abeler-Dörner, Matthew Hall, Chris Wymant, David Bonsall, George Macintyre-Cockett, Laura Thomson, Jared M. Baeten, Connie L. Celum, Ronald M. Galiwango, Barry Kosloff, Mohammed Limbada, Andrew Mujugira, Nelly R. Mugo, Astrid Gall, François Blanquart, Margreet Bakker, Daniela Bezemer, Swee Hoe Ong, Jan Albert, Norbert Bannert, Jacques Fellay, Barbara Gunsenheimer-Bartmeyer, Huldrych F. Günthard, Pia Kivelä, Roger D. Kouyos, Laurence Meyer, Kholoud Porter, Ard van Sighem, Mark van der Valk, Ben Berkhout, Paul Kellam, Marion Cornelissen, Peter Reiss, Helen Ayles, David N. Burns, Sarah Fidler, Mary Kate Grabowski, Richard Hayes, Joshua T. Herbeck, Joseph Kagaayi, Pontiano Kaleebu, Jairam R. Lingappa, Deogratius Ssemwanga, Susan H. Eshleman, Myron S. Cohen, Oliver Ratmann, Oliver Laeyendecker, Christophe Fraser, the HPTN 071 (PopART) Phylogenetics protocol team, the BEEHIVE consortium and the PANGEA consortium

“…Results We developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly from viral deep-sequencing data, with no need for additional variables. …”
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SMART HYBRID MODELS FOR IMPROVED BREAST CANCER DETECTION by Nageswara Rao Gali, Panduranga Vital Terlapu, Yasaswini Mandavakuriti, Sai Manoj Somu, Madhavi Varanasi, Vijay Telugu, Maheswara Rao V V R

“…The conventional method for BC detection primarily relies on biopsy; this might be time-consuming and error prone. The substantial lives lost due to BC underscores its significant threat. …”
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