Comparison of Neural Network, Ordinary Kriging, and Inverse Distance Weighting Algorithms for Seismic and Well-Derived Depth Data: A Case Study in the Bjelovar Subdepression, Croat... by Ana Brcković, Tomislav Malvić, Jasna Orešković, Josipa Kapuralić

“…The obtained maps were compared based on square error (using k-fold cross-validation) and the visual interpretation of isopaches. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. The classifier performance (accuracy, error, F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values were given at the conclusion section of the research. …”
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A comparative ensemble approach to bedload prediction using metaheuristic machine learning by Ajaz Ahmad Mir, Mahesh Patel, Fahad Albalawi, Mohit Bajaj, Milkias Berhanu Tuka

“…The coefficient of determination (R 2) and root mean square error (RMSE) values vary between various models; however, XGB showed R 2 = 0.99 and RMSE = 0.11. …”
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Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors by Ana González-Castro, José Alberto Benítez-Andrades, Rubén González-González, Camino Prada-García, Raquel Leirós-Rodríguez

“…Methods We applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. …”
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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. …”
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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…Finally, we compare the performance of our proposed DNN model with other machine learning algorithms, such as Random Forest and Support Vector Machines, demonstrating superior predictive accuracy. …”
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Enhancing soil total nitrogen prediction in rice fields using advanced Geo-AI integration of remote sensing data and environmental covariates by Novandi Rizky Prasetya, Aditya Nugraha Putra, Mochtar Lutfi Rayes, Sri Rahayu Utami

“…Recently, advanced Geospatial-Artificial Intelligence (Geo-AI) techniques such as the random forest (RF) algorithm have been developed to increase the accuracy and spatial representativeness of STN prediction. …”
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Plastic to apparel: an analysis of sustainable purchasing intention using a machine learning ensemble by Carmella Andrea L. Cabrera, Ardvin Kester S. Ong, John Francis T. Diaz, Maela Madel L. Cahigas, Ma. Janice J. Gumasing

“…To analyze the data, the study utilized machine learning methods, such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). …”
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Prognosis of air quality index and air pollution using machine learning techniques by Mostafa M. Abdelmalek, Hatem Mahmoud, Hassan Shokry

“…The comparative analysis revealed that the GPR model outperformed the other ML models with a minimum Root Mean Square Error (RMSE) of 0.87 and 1.219 during the training and testing, respectively. …”
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Corrosion Rate Prediction of Buried Oil and Gas Pipelines: A New Deep Learning Method Based on RF and IBWO-Optimized BiLSTM–GRU Combined Model by Jiong Wang, Zhi Kong, Jinrong Shan, Chuanjia Du, Chengjun Wang

“…The method is based on the combination of an improved Beluga Optimization algorithm (IBWO) and Random Forest (RF) optimization with BiLSTM and gated cycle unit (GRU), which are used to classify corrosion rates as high or low. …”
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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…In contrast, the random forest model outperformed others on the downscaled daily data, reaching an accuracy of $$95.8\%$$ 95.8 % , thereby supporting the efficacy of data downscaling for ML applications in epidemiology. …”
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Modeling and Performance Analysis of MDM−WDM FSO Link Using DP-QPSK Modulation Under Real Weather Conditions by Tanmeet Kaur, Sanmukh Kaur, Muhammad Ijaz

“…Minimum received power and SNR values of −52 dBm and −33 dB have been obtained over the observed transmission range as a result of multiple impairments. Random forest (RF), k-nearest neighbors (KNN), multi-layer perceptron (MLP), gradient boosting (GB), and machine learning (ML) techniques have also been employed for estimating the SNR of the received signal. …”
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Improving time upscaling of instantaneous evapotranspiration based on machine learning models by Danni Yang, Shanshan Yang, Jiaojiao Huang, Shuyu Zhang, Sha Zhang, Jiahua Zhang, Yun Bai

“…To resolve this issue, this study aimed to assess four machine learning (ML) algorithms—XGBoost, LightGBM, AdaBoost, and Random Forest—to integrate meteorological and remote sensing data for upscaling [Formula: see text] across 88 global flux sites. …”
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Car Price Prediction and Recognition Using Deep Learning and Computer Vision Algorithms by Hira Farman, Saad Ahmed, Muhammad Hussain Mughal, Qurat -ul-ain Mastoi, Govari Shankar Lalwani

“…In this study the main source of information for the forecasts is the Kaggle website. Random forest demonstrated a high degree of accuracy and a low root mean square error in all other investigated models. …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Notably, KRR exhibited exceptional performance, achieving an R² of 0.997 on the training set and 0.992 on the test set, with a mean squared error (MSE) of 0.0004. In comparison, K-ELM and QR achieved lower R² values of 0.923 and 0.817 on the test set, respectively. …”
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Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform by Ran Xiong, Xuri Huang, Liang Guo, Xuan Zou, Haonan Tian

“…In this process, the seismic amplitude, spectrum data and Gabor attributes are used as sample data for the support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) models and deep residual shrinkage network (DRSN) for comparison. …”
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Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia by Mathias Tesfaye, Lutz Breuer

“…We assess Gradient Tree Boosting (GTB), Support Vector Machines (SVM), and Random Forest (RF) running on the Google Earth Engine (GEE) using Landsat for surface water monitoring at four sites in Ethiopia from 1986 to 2023. …”
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Chemometric and meta-heuristic algorithms to find optimal wavelengths and predict ‘Red Delicious’ apples traits using Vis-NIR by Mahsa S. Razavi, Vali Rasouli Sharabiani, Mohammad Tahmasebi, Silvia Grassi, Mariusz Szymanek

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Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. by Varun Tiwari, Kelly Thorp, Mirela G Tulbure, Joshua Gray, Mohammad Kamruzzaman, Timothy J Krupnik, A Sankarasubramanian, Marcelo Ardon

“…This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. …”
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