Exploring Machine Learning's Potential for Estimating High Resolution Daily Snow Depth in Western Himalaya Using Passive Microwave Remote Sensing Data Sets by Srinivasarao Tanniru, Dhiraj Kumar Singh, Kamal Kant Singh, RAAJ Ramsankaran

“…Different machine learning (ML) methods viz. support vector machine, random forest, and Extremely Randomized Trees (ERT) were tested for estimating SD. …”
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Machine learning prediction of pKa of organic acids by Juda Baikété, Alhadji Malloum, Jeanet Conradie

“…The four models, Random Forest (RF), Extra Trees (ExTr), Histogram Gradient Boosting (HGBoost), and Gradient Boosting (GBoost), were trained on an experimental pKa dataset and tested on SAMPL6 and SAMPL7, two external datasets. …”
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Predicting mortality risk in Alzheimer’s disease using machine learning based on lifestyle and physical activity by Ruihong Tang, Liang Tan, Xia Chen, Zhengwu Shan, Zhao Zhang, Panpan Cheng

“…Model performance was evaluated using the integrated area under the curve (iAUC), integrated Brier score/prediction error (iBS/PE), and concordance index (C-index). …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machine learning approaches within the hdPS framework, focusing on key metrics such as bias, standard error (SE), and coverage, under various exposure and outcome prevalence scenarios.…”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Six supervised ML models—ANCOVA, Random Forest (RF) regression, K-Nearest Neighbor, Support Vector Machine, Extreme Gradient Boosting, and Lasso regression—were trained using an 80:20 training-to-testing split. …”
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The influence of jittering DHS cluster locations on geostatistical model-based estimates of malaria risk in Cameroon by Salomon G. Massoda Tonye, Romain Wounang, Celestin Kouambeng, Penelope Vounatsou

“…Among the important predictors identified in the true data, distance to water bodies and presence of forest were mostly influenced by the jittering. Altitude and vegetation index were the least affected predictors. …”
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Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model by Ngoc-Kim-Khanh Nguyen, Quang Nguyen, Hai-Ha Pham, Thi-Trang Le, Tuan-Minh Nguyen, Davide Cassi, Francesco Scotognella, Roberto Alfieri, Michele Bellingeri

“…We found that the RF model can predict network robustness with a mean squared error (RMSE) of 0.03 and is 30% better than the MLR model. …”
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Data‐driven designing on mechanical properties of biodegradable wrought zinc alloys by Zongqing Hu, Shaojie Li, Jianfeng Jin, Yuping Ren, Rui Hou, Lei Yang, Gaowu Qin

“…Machine learning models were applied to predict mechanical properties, in which random forest (RF) model exhibited the best performance and further validated by a new experimental sample of Zn‐0.05Mg‐0.5Mn, with the mean absolute percentage error (MAPE) less than 10%. …”
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Diagnosis and Classification of Two Common Potato Leaf Diseases (Early Blight and Late Blight) Using Image Processing and Machine Learning by H. Koroshi Talab, D. Mohammad Zamani, M. Gholami Parashkoohi

“…Traditional methods of visual assessment by human observers are time-consuming, costly, and error-prone, making accurate diagnosis and differentiation between various diseases difficult. …”
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Finite Element and Machine Learning-Based Prediction of Buckling Strength in Additively Manufactured Lattice Stiffened Panels by Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Szabolcs Szávai, Gyula Varga

“…Finally, the data samples collected from numerical outcomes were utilized to train four different machine learning models, namely multi-variable linear regression, polynomial regression, the random forest regressor and the K-nearest neighbor regressor. …”
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Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs by Sayed Gomaa, Ahmed Ashraf Soliman, Mohamed Mansour, Fares Ashraf El Salamony, Khalaf G. Salem

“…In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. …”
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Evaluating the impact of machine learning models on adult major depressive disorder using conventional treatment strategies: a systematic review approach by Nishant Yadav, Anamika Gulati, Varun Gulati, Prashant Yadav

“…The analysis included models such as Support Vector Machines (SVM), Random Forest (RF), Ensemble Models, Deep Learning, and Graph Neural Networks. …”
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Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m², while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m². …”
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Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…Climate change causes shifts in biodiversity and impacts agriculture, forest ecosystems, and water resources at a regional scale. …”
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Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

“…We assessed the performance of six widely used empirical models (Hargreaves Samani, modified Hargreaves Samani, Romanenko, Schendel, Priestley-Taylor, and Makkink) and four ML models (random forest, extreme gradient boosting, deep neural network, and long short-term memory) to estimate ET0 with limited climatic variables in Nepal. …”
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Multi-Airport Capacity Decoupling Analysis Using Hybrid and Integrated Surface–Airspace Traffic Modeling by Lei Yang, Yilong Wang, Sichen Liu, Mengfei Wang, Shuce Wang, Yumeng Ren

“…In the surface model, we utilize linear regression and random forest regression to model unimpeded taxiing time and taxiway network delays due to sparsity of ground traffic. …”
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Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems by Nail Beisekenov, Wiyao Banakinaou, Ayomikun David Ajayi, Hideo Hasegawa, Aoda Tadao

“…Other models, including Random Forest (RF) and Support Vector Machine (SVM), showed competitive but slightly lower performance. …”
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Polyomaviruses and the risk of oral cancer: a systematic review and meta-analysis by Tahoora Mousavi, Fatemeh Shokoohy, Mahmood Moosazadeh

“…In addition, a random effects model was used to determine the risk difference (RD), and a forest plot diagram was used to present the results with 95% confidence intervals. …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…We used AI algorithms like random forest regressor (RFR), extreme gradient boost regressor (XGBR), and one-dimensional convolutional neural network (1D-CNN) to predict the binding affinity. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. …”
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