Machine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study by Sun Q, Liu Z, Ding T, Shi C, Hou N, Sun C

“…Feature selection was performed using the Lasso algorithm and Pearson correlation coefficient, and a random forest regression model was constructed. Model performance was evaluated using mean square error (MSE), coefficient of determination (R²), Pearson linear correlation coefficient (PLCC), Spearman rank correlation coefficient (SRCC), and Kendall rank correlation coefficient (KRCC).Results: After feature selection, three key features were retained: main pulmonary artery CT value, ascending aorta CT value, and the difference in noise values between the left and right main pulmonary arteries. …”
Get full text

Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…The applicability of backpropagation (BP) neural network, random forest (RF), convolutional neural network (CNN), and CNN-RF models for remote sensing inversion of PP concentration is assessed through model comparison. …”
Get full text

Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…This performance significantly exceeded that of LSTM, Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR), with GRU reducing the root mean squared error (RMSE) by 12.3 %–27.5 % compared to LSTM in long-term predictions. …”
Get full text

Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morpholog... by Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic

“…Within this set of models, GPR model has a lower Mean Absolute Error of 0.3177, Root Mean Square Error of 0.6704 and higher R2 value of 0.9686, resulting a prediction accuracy of 96.86%. …”
Get full text

A framework based on mechanistic modelling and machine learning for soil moisture estimation by Sabri Kanzari, Sana Ben Mariem, Samir Ghannem, Safouane Mouelhi, Hiba Ghazouani, Bechir Ben Nouna

“…The statistical performance indices of prediction, root mean square error and correlation coefficient indicate the superiority of regression trees over other methods for all soil layers and during both calibration and validation processes and reproducing the seasonal variation of soil moisture.…”
Get full text

Multivariate Calibration and Model Integrity for Wood Chemistry Using Fourier Transform Infrared Spectroscopy by Chengfeng Zhou, Wei Jiang, Qingzheng Cheng, Brian K. Via

Get full text

Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach by Tirunagaru V. Sarathkumar, Arup Kumar Goswami, Baseem Khan, Kamel A. Shoush, Sherif S. M. Ghoneim, Ramy N. R. Ghaly

“…Notably, the introduced AOLSTM approach demonstrates minimal error metrics compared to conventional methods such as persistence, Gradient Boost, and Random Forest.…”
Get full text

Predicting soybean seed germination using the tetrazolium test and computer intelligence by Marcio Alves Fernandes, Izabela Cristina de Oliveira, Marcio Dias Pereira, Breno Zaratin Alves, Alan Mario Zuffo, Charline Zaratin Alves

“…The data analysis used the correlation coefficient and mean absolute error as accuracy parameters of the algorithms. The results highlighted the support vector machine as the most effective algorithm for predicting germination, with the viability and vigor + viability inputs showing the best results. …”
Get full text

Preliminary exploration and application research on the model of gathering distillate according to the quality based on Fourier transform near infrared spectroscopy by LIAO Li, ZHANG Guiyu, ZOU Yongfang, ZHU Xuemei, PENG Houbo, ZHANG Wei, LI Yan

“…Multiplicative scatter correction (MSC), competitive adaptive reweighting algorithms sampling (CARS) and support vector regression (SVR) were better methods to construct the regression prediction model, with coefficient of determination R² and root mean square error (RMSE) mean values of 0.8951 and 0.03, respectively. …”
Get full text

A Weakly Supervised Multimodal Deep Learning Approach for Large-Scale Tree Classification: A Case Study in Cyprus by Arslan Amin, Andreas Kamilaris, Savvas Karatsiolis

“…Forest ecosystems play an essential role in ecological balance, supporting biodiversity and climate change mitigation. …”
Get full text

Prediction of R1234yf flow boiling behavior in horizontal, vertical, and inclined tubes using machine learning techniques by Farzaneh Abolhasani, Behrang Sajadi, Mohammad Ali Akhavan-Behabadi

“…According to the results obtained in the prediction of the heat transfer coefficient, AdaBoost model performs the best with the mean absolute percentage error (MAPE) of 5.73 % and correlation coefficient (R) of 0.979 on the test dataset. …”
Get full text

A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). …”
Get full text

Monthly Load Forecasting in a Region Experiencing Demand Growth: A Case Study of Texas by Jeong-Hee Hong, Geun-Cheol Lee

“…Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. …”
Get full text

Prediction of coal and gas outbursts based on physics informed neural networks and traditional machine learning models by Lei Wang, Baoshan Jia, Guorui Su

“…The results show that the PINN model achieves a coefficient of determination (R2) of 0.966 and a root mean square error (RMSE) of 6.452, outperforming the traditional models in both prediction accuracy and generalization ability. …”
Get full text

Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…This work compares five supervised machine learning algorithms, including artificial neural networks, support vector regression, kernel ridge regression, random forest, and Lasso regression. These models are evaluated based on the coefficient of determination and the mean squared error. …”
Get full text

A machine learning‐based approach for wait‐time estimation in healthcare facilities with multi‐stage queues by Amjed Al‐Mousa, Hamza Al‐Zubaidi, Mohammad Al‐Dweik

“…The work employs feature engineering and compares several machine learning‐based algorithms to predict patients' waiting times for single‐stage and multi‐stage services. The Random Forest algorithm achieved the lowest root mean squared error (RMSE) value of 6.69 min among all machine learning algorithms. …”
Get full text

Evaluating a hierarchy of bias correction methods for ERA5-Land SWE across Canada by Neha Kanda, Christopher G Fletcher

“…To correct these biases, we applied four correction methods: Mean Bias Subtraction (MBS), Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Random Forest (RF). RF exhibited the highest performance, reducing the Root Mean Square Error (RMSE) by 67% and minimizing the annual mean bias from −15 mm to 0.18 mm. …”
Get full text

Big data-driven corporate financial forecasting and decision support: a study of CNN-LSTM machine learning models by Aixiang Yang

“…Experimental results demonstrate the superior performance of the proposed model, achieving a Mean Squared Error (MSE) of 0.020 and an R2 score of 0.411, significantly outperforming benchmark models (ARIMA, Random Forest, XGBoost, and standalone LSTM). …”
Get full text

A soil organic carbon mapping method based on transfer learning without the use of exogenous data by Jingfeng Han, Mujie Wu, Yanlong Qi, Xiaoning Li, Xiao Chen, Jing Wang, Jinlong Zhu, Qingliang Li

“…The transfer model achieves a coefficient of determination (R2) of 0.374 and a root mean square error (RMSE) of 2.937%, indicating superior performance. …”
Get full text

A quantum inspired machine learning approach for multimodal Parkinson’s disease screening by Diya Vatsavai, Anya Iyer, Ashwin A. Nair

“…Although machine-learning-based detection has shown promise for detecting Parkinson’s disease, most studies rely on a single feature for classification and can be error-prone due to the variability of symptoms between patients. …”
Get full text