Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars by Atchadeou Yranawa Katatchambo, Şinasi Bingöl

“…The model performance of the methods was compared using root mean square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE) performance statistics. …”
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Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka by W.M.S Bandara, W.A.R. De Mel

“…These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with the aid of root mean square error. The mean absolute percentage error (MAPE) was used to compare the performance of the different SMLMs. …”
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Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…The models were evaluated for their prediction accuracy and reliability using mean absolute error (MAE), mean square error (MSE) and R2 score metrics. …”
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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.…”
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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 algorithms tested were REPTree, M5P, random forest, logistic regression, artificial neural networks and support vector machine, and the inputs tested were viability, vigor and vigor + viability (tetrazolium test) data. …”
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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

“…The spectrum was obtained by Fourier transform near-infrared spectroscopy (FT-NIR), and the spectrum pretreatment and wavelength screening were performed, the regression prediction model was established based on the principal components, and the model of gathering distillate according to the quality was constructed by random forest (RF). Results showed that 17 principal components were selected by PCA. …”
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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. …”
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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. …”
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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). …”
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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%. …”
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Prediction of coal and gas outbursts based on physics informed neural networks and traditional machine learning models by Lei Wang, Baoshan Jia, Guorui Su

“…Using actual data from a coal mine, this study compares the performance of the PINN model with traditional machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Backpropagation Neural Network (BPNN). …”
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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. …”
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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. …”
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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. …”
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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). …”
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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

“…Experimental results show that the proposed transfer model consistently outperforms other machine learning models, including the Random Forest (RF), standard CNN, and Multi-Task CNN (MTCNN) models. …”
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A quantum inspired machine learning approach for multimodal Parkinson’s disease screening by Diya Vatsavai, Anya Iyer, Ashwin A. Nair

“…From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. …”
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Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition. by Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun

“…Experiments show that the proposed model achieves an accuracy rate of 95.23% in identifying interference signals and an anti-interference accuracy rate of 85.47%, significantly outperforming random forest and deep Q-network models. The study not only clarifies the limitations of existing solutions but also precisely defines the goals of the new model, which are to reduce error rates and improve adaptability in dynamic environments. …”
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A Bi-Temporal Airborne Lidar Shrub-to-Tree Aboveground Biomass Model for the Taiga of Western Canada: Un Modèle Bitemporal de Biomasse Aérienne D’arbuste à D’arbre Pour le Lidar Aé... by Linda Flade, Chris Hopkinson, Laura Chasmer

“…These results are important for quantifying climate change effects on forest to peatland ecotones.…”
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Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization by Mohammed Alqarni, Ali Alqarni

“…Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance. …”
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