Enhancing peak performance forecasting in steam power plants through innovative AI-driven exergy-energy analysis by Muhammad Ali Ijaz Malik, Adeel Ikram, Sadaf Zeeshan, Muhammad Naqvi, Syed Qasim Raza Zahidi, Fayaz Hussain, Hayati Yassin, Atika Qazi

“…To enhance predictive accuracy, a random forest regression model is employed to forecast various performance indicators of the steam power plant. …”
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Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions by Silviu-Ionut BABTAN

“…This article examines several AI methods – Regression Models, Decision Trees, Random Forests, Artificial Neural Networks, and XGBoost – and explores their applications for improving property valuation accuracy and efficiency, with implications for other professions involved, e.g. audit. …”
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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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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…Effective dose was also estimated using DLP and k-factors, and with multiple linear regression. Mean absolute error (MAE, mean absolute percentage error (MAPE), and R² were used to evaluate predictions in the test set and in an external dataset of 3800 acquisitions. …”
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An Empirical Comparison of Urban Road Travel Time Prediction Methods—Deep Learning, Ensemble Strategies and Performance Evaluation by Yizhe Wang, Yangdong Liu, Xiaoguang Yang

“…The experimental results demonstrate that: (1) deep learning models generally outperform shallow learning models in terms of Mean Absolute Percentage Error (MAPE), particularly the LSTM-DNN model which achieves the best MAPE values across all prediction scenarios with 30 min sliding time windows; (2) in terms of Root Mean Square Error (RMSE), shallow learning models such as random forest perform better in most scenarios; (3) ensemble learning models show certain advantages in some prediction scenarios, but the improvement effects are limited and scenario-dependent. …”
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AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models by Faruk Dikmen, Ahmet Demir, Bestami Özkaya, Muhammad Owais Raza, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai

“…The study leveraged ensemble learning models, including XGBoost, Random Forest, Gradient Boosting, and LightGBM, and compared them with Decision Tree models. …”
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A Hybrid Machine Learning Approach for High-Accuracy Energy Consumption Prediction Using Indoor Environmental Quality Sensors by Bibars Amangeldy, Nurdaulet Tasmurzayev, Timur Imankulov, Baglan Imanbek, Waldemar Wójcik, Yedil Nurakhov

“…The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. …”
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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

“…Results obtained evidence that random forest obtained the strongest accuracy, and generalizability from the high coefficient of determination, and lowest error scores. …”
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Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis by Jong-Ho Kim, Hye-Sook Kim, Jong-Hee Sohn, Sung-Mi Hwang, Jae-Jun Lee, Young-Suk Kwon

“…Frequent analgesic medication emerged as a significant predictor of poorer life quality (Headache Impact Test-6, root mean squared error = 7.656) and increased depression (Patient Health Questionnaire-9, root mean squared error = 5.07) and anxiety (Generalized Anxiety Disorder-7, root mean squared error = 4.899) in the Random Forest model. …”
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Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches by Muhammad Waqas, Shahid Noor Jan, Basir Ullah, Afed Ullah Khan, Ateeq Ur Rauf, Bakht Niaz Khan

“…Each model's performance was assessed by using statistical indicators including coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE). …”
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AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports by Musab Aloudah, Mahdi Alajmi, Alaa Sagheer, Abdulelah Algosaibi, Badr Almarri, Eid Albelwi

“…A suite of machine learning models, including LSTM, Transformer variants, Ensemble Stacking, XGBRegressor, and Random Forest, was applied to historical export and GDP data. …”
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A systematic study on PM2.5 and PM10 concentration prediction in air pollution using machine learning and deep learning model by Pranshu Patel, Swara Patel, Kanish Shah, Kedar Desai, Samrat Patel, Manan Shah, Samir Patel

“…In contrast, other models, including Random Forest, Decision Tree, and XGBoost, exhibited R2 scores between 0.15 and 0.96, indicating lower predictive accuracy. …”
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The Spatial Distribution of Tree Dieback Affected by Mistletoe in Relation to their Crown Characteristics by Erfan Boshkar, Ehsan Sayad, Shayeste Gholami

“…1- Introduction One of the main problems in The Zagros Forests is oak trees dieback. What is really certain and important is the fact that the tree dieback crisis caused the declining of oak in oak forest of Zagros. …”
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Development and Evaluation of Solar Radiation Sensor Using Cost-Effective Light Sensors and Machine Learning Techniques by Jesús Antonio Nava-Pintor, Uriel E. Alcalá-Rodríguez, Héctor A. Guerrero-Osuna, Marcela E. Mata-Romero, Emmanuel Lopez-Neri, Fabián García-Vázquez, Luis Octavio Solís-Sánchez, Rocío Carrasco-Navarro, Luis F. Luque-Vega

“…Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². …”
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A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

“…As a dominant terrestrial ecosystem, forests play a pivotal role, which is substantially challenged by climate extremes. …”
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Missing value interpolation algorithm for long-term temperature observation data based on data augmentation multiple interpolation method by Xiaolin Liu, Bo Wang, Shuanglong Jin, Zongpeng Song

“…Fully considering the characteristics of time series, the temperature data status is continuously updated through the recursive correction process. Taking a certain forest ecological station as the research area, the missing value interpolation performance of the temperature observation dataset of a certain forest ecological station with a data time span from May 1st to May 10th, 2023 was verified. …”
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Regionalization Analysis of Environmental Drivers of CONUS Grazing Land Biomass by Jisung Geba Chang, Feng Gao, Martha C. Anderson, Richard Cirone, Haoteng Zhao

“…Investigating the performance of several machine learning approaches in reproducing RAP biomass, the random forest model performed best, with a mean absolute error of 373 lb/acre and a coefficient of determination (<italic>R</italic>²) of 0.66. …”
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Spatial analysis of drought severity, duration and frequency using different drought indices (Case study: Fars Province, Iran) by Mona Massoudi, Massoud Goodarzi, Abolfazl Moeini, Baharak Motamedvaziri

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Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds by Sıtkı Ermiş, Uğur Ercan, Aylin Kabaş, Önder Kabaş, Georgiana Moiceanu

“…This study employs machine learning models—Random Forest (RF), LightGBM, and k-Nearest Neighbors (KNN)—to classify ornamental pumpkin seeds based on their morphological (mass, elongation, width, thickness) and colorimetric characteristics (L*, a*, b* values from CIELAB color space). …”
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Estimation of grassland use intensity based on the generated high-resolution daily land surface reflectance dataset with GF-6 WFV and HLS by Hanwen Cui, Xiaosong Li, Bin Sun, Guangbin Yang, Ziyu Yang, Chaochao Chen, Licheng Zhao

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