FORECASTING AUTOMOBILE DEMAND AND SALES IN THE NIGERIAN MARKET: A MACHINE LEARNING APPROACH TO URBAN MOBILITY, MARKET COMPETITION, AND POLICY INSIGHTS by Emmanuel Imuede Oyasor

“…In contrast, the MARS model underperformed, displaying the highest error rates and limited predictive capacity (R² = 0.43). …”
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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…We found a strong correlation (r = 0.93) between the sensitivity of ET estimates to machine-learned parameters and model error (root-mean-square error; RMSE), indicating that reduced sensitivity minimizes error propagation and improves performance. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…Meanwhile, accuracy assessments indicated that Support Vector Regression had the most accurate predictions accompanied by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, and Coefficient Correlation (CC) at 1.366, 0.947, 1.866, 0.948 and 0.982 respectively. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R 2. …”
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Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment by JianWun Lai

“…With a Root Mean Absolute Error (RMSE) of 4.76 mg/L for 24-h horizons and a Mean Absolute Error (MAE) of 0.85 mg/L for 1-h predictions, the proposed model outperforms conventional methods in terms of prediction accuracy. …”
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Analysis and prediction of storability and storage quality of pear fruit based on clustering and time series analysis by ZHOU Hui-juan, YE Zheng-wen, LI Rong-hua, WANG Xiao-qing, LUO Jun

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Endoscopic management of hemorrhagic pancreatic fluid collections: A propensity‐matched analysis by Rishi Pawa, Robert Dorrell, Greg Russell, Madison Nguyen, Clancy Clark, Girish Mishra, Swati Pawa

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Evaluation of Topographic Effect Parameterizations in Weather Research and Forecasting Model over Complex Mountainous Terrain in Wildfire-Prone Regions by Yong Han Jo, Seung Hee Kim, Yun Gon Lee, Chang Ki Kim, Jinkyu Hong, Junhong Lee, Keunchang Jang

“…The model performance was evaluated over the mountainous region in Gangwon-do, South Korea’s most significant forest area. The simulation results of the wildfire case in 2019 show that subgrid-scale orographic parameterization considerably improves model performance regarding wind speed, with a lower root mean square error (RMSE) and bias by 53% and 57%, respectively. …”
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Inside and outside bark volume models for jack pine (Pinus banksiana) and black spruce (Picea mariana) plantations in Ontario, Canada by Mahadev Sharma

“…Accurate estimates of tree stemwood volume are a fundamental requirement for sustainable forest management. As jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana Mill. …”
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Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, Ahmed M. Ebid, Janneth Alejandra Viñan Villagran, Raúl Gregorio Martínez Pérez, Fausto Ulpiano Caicedo Benavides, Paul Awoyera, Siva Avudaiappan

“…AdaBoost and KNN demonstrate the lowest errors (MAE: 0.65 MPa, RMSE: 0.75 MPa), indicating precise performance, minimizing overdesign or underperformance risks, and optimizing material usage. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…Researchers often rely on trial-and-error approaches to enhance membrane hemocompatibility by reducing these interactions. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…The feature selection process was optimized using Gini importance metrics, with model performance evaluated through mean squared errors and mean absolute errors.…”
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Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance by Ziyuan Zhong, Junyang Zhou

“…Even with larger classification errors, it remains comparable to that of state-of-the-art models. …”
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Predicting Type 2 diabetes onset age using machine learning: A case study in KSA. by Faten Al-Hussein, Laleh Tafakori, Mali Abdollahian, Khalid Al-Shali, Ahmed Al-Hejin

“…The MLR and RF models provided the best fit, achieving R2 values of 0.90 and 0.89, root mean square errors (RMSE) of 0.07 and 0.01, and mean absolute errors (MAE) of 0.05 and 0.13, respectively, using the logarithmic transformation of the onset age. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

“…Model performance validation analysis was evaluated via R-squared (R2), root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) statistical models. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…Among the various predictive models evaluated, the random forest model emerged as the most accurate tool, according to the performance metrics of R -squared, mean square error, and average absolute relative error (%), with numerical values of 0.958, 3.718, and 2.545%, respectively, for the total datapoints. …”
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Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems by Siow Jat Shern, Md Tanjil Sarker, Mohammed Hussein Saleh Mohammed Haram, Gobbi Ramasamy, Siva Priya Thiagarajah, Fahmid Al Farid

“…Among the models, XGBoost demonstrated superior predictive performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), making it the most effective for real-time user demand prediction in smart charging scenarios. …”
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Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM by Bingzeng Song, Guangzhao Yue, Dong Guo, Hanming Wu, Yonghai Sun, Yuhua Li, Bin Zhou

“…The root mean square error decreased by 72%, 59%, 70%, and 54%, and the mean absolute percent error decreased by 75%, 65%, 71%, and 58%, respectively. …”
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Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning by L. T. Keetz, K. Aalstad, R. A. Fisher, C. Poppe Terán, B. Naz, N. Pirk, Y. A. Yilmaz, O. Skarpaas

“…Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM‐FATES, and/or unknown errors in the data that are not accounted for.…”
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