Harnessing machine learning for transmembrane pressure prediction in MBR systems during textile wastewater treatment by Onaira Zahoor, Sher Jamal Khan, Muhammad Usama, Henry J. Tanudjaja, Noreddine Ghaffour, Muhammad Saqib Nawaz

“…A total of 60 datasets were compiled for training and testing of the models. The random forest model attained an R2 of 0.95 and 0.86 and root mean square error values of 1.75 kPa and 3.3 kPa for the training and test datasets, respectively, demonstrating the best predictive accuracy. …”
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Comparative Study on Soil Infiltration Characteristics of Different Land Use Types in Horqin Sandy Land by Yin Jiawang, Ala Musa, Su Yuhang, Jiang Shaoyan

“…The Horton model had the highest coefficient of determination and the smallest relative error, and could accurately reflect the actual situation of soil infiltration in Horqin sandy land. …”
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Comparison of Machine Learning Methods for Calories Burn Prediction by Alfred Tan Jing Sheng, Zarina Che Embi, Noramiza Hashim

“…Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed system has a good potential in assisting users in reaching their fitness objectives by offering precise and tailored advice.…”
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Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection.MethodsThis study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. …”
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An overview of AI in Biofunctional Materials by Dazhou Li

“…., mechanical strength, degradation rate) with > 90% accuracy, dramatically reducing trial-and-error in scaffold and nanoparticle fabrication. AI-driven platforms accelerate surface functionalization strategies to enhance cell adhesion and drug loading, while generative models design stimuli-responsive hydrogels and smart polymers that mimic tissue mechanics. …”
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COMPARISON OF CENTROID METHOD AND TREE STANDARD FORMULA FOR ESTIMATION LOG VOLUME by Mohammed Al-allaf

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Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision by Shuxin Zhong, Li Cheng, Haiwen Yuan, Xuan Li

“…To improve the accuracy of Wi-Fi positioning, a random forest algorithm with added region restriction is proposed. …”
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Hybrid retrieval of grass biophysical variables based-on radiative transfer, active learning and regression methods using Sentinel-2 data in Marakele National Park by Philemon Tsele, Abel Ramoelo

“…Results show the most accurate grass LAI and LCC retrievals had lower relative root mean squared errors (RRMSEs) of 39.87% and 16.58% respectively. …”
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Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach by Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros

“…The study evaluated the performance of five methods for predicting the leaf area of sweet potato cultivars, including simple linear regression, artificial neural networks, support vector regression, adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The coefficient of determination (R2), relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE) were used as criteria for choosing the best methods. …”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

“…Then, the thermal compression flow behavior of the as-cast composite was comparatively researched using a traditional Arrhenius model and advanced machine learning methods (Linear Regression, AdaBoost, Random Forest, and XGBoost). The flow stresses were predicted under various thermal operating conditions, and the performance of all models was assessed using root mean square error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). …”
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Integrating machine learning and spatial clustering for malaria case prediction in Brazil’s Legal Amazon by Kayo Henrique de Carvalho Monteiro, Élisson da Silva Rocha, Luis Augusto Morais, Elton Gino Santos, Sebastião Rogerio da S. Neto, Vanderson Sampaio, Patricia Takako Endo

“…The results demonstrate that the RF model consistently outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values in most cases, such as in cluster 02 of the state of Acre, with RMSE of 0.00203 and MAE of 0.00133. …”
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Prediction and sensitivity analysis of pressure pulsation in a LDI combustor based on a fully connected neural network by Qian Yao, Shize Tian, Wei Pan, Wu Jin, Jianzhong Li, Li Yuan

“…A fully connected neural network (FCNN) is utilized to predict these pressure parameters, and it demonstrates superior performance compared to support vector regression (SVR) and random forest (RF) models. For test data, the FCNN achieves an average relative error (MRE) of 2.54 % for dominant frequency and a mean absolute error (MAE) of 0.64 % for pressure amplitude, suggesting high accuracy and generalization ability. …”
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A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot by Yan Li, Xuerui Qi, Yucheng Cai, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaohu Zhang

“…The model based on the LightGBM regression algorithm has the most improvement in accuracy, with a coefficient of determination (R²) of 0.892, a root mean square error (RMSE) of 0.270, and a mean absolute error (MAE) of 0.160. …”
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Predicting the thickness of shallow landslides in Switzerland using machine learning by C. Schaller, C. Schaller, L. Dorren, M. Schwarz, C. Moos, A. C. Seijmonsbergen, E. E. van Loon

“…Our results show that the ML models consistently outperformed the simple models by reducing the mean absolute error by at least 20 %. The RF models produced a mean absolute error of 0.25 m for the HMDB and 0.20 m for the KtBE data. …”
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Forecasting and Feature Analysis of Ship Fuel Consumption by Explainable Machine Learning Approaches by Pham Nguyen Dang Khoa, Dinh Gia Huy, Nguyen Canh Lam, Dang Hai Quoc, Pham Hoang Thai, Nguyen Quyen Tat, Tran Minh Cong

“…It was found that, with an R² of 1, zero mean squared error (MSE), and a negligible mean absolute percentage error (MAPE), the DT model suited the training set perfectly, while R² was 0.8657, the MSE was 56.80, and the MAPE was 16.37% for the DT model testing. …”
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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…The models were assessed using evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. …”
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Voice as a Health Indicator: The Use of Sound Analysis and AI for Monitoring Respiratory Function by Nicki Lentz-Nielsen, Lars Maaløe, Pascal Madeleine, Stig Nikolaj Blomberg

“…Despite an error range of −1252 to 1435 mL/s, most predictions fell within the LoA, indicating good performance in estimating the FEV1. …”
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AI-Powered Forecasting of Environmental Impacts and Construction Costs to Enhance Project Management in Highway Projects by Joon-Soo Kim

“…The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. …”
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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X50) were adopted to train and validate the predictive models. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2) were used as the evaluation metrics. …”
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Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

“…The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. …”
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