Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula by Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

“…The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. …”
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Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques by Harjeet Kaur, Srimanta Gupta, Surya Parkash, Raju Thapa, Arindam Gupta, G. C. Khanal

“…The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…Our model achieved a Mean Absolute Error (MAE) of 0.028993, an <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> score of 0.999490, and training, validation, and testing times of 81.64 seconds, 0.15 seconds, and 0.18 seconds, respectively. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…Three standalone ML models—K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained, with RF achieving R² = 0.963 and XGB achieving R² = 0.946 on the test set. …”
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Estimation of Cylinder Grasping Contraction Force of Forearm Muscle in Home-Based Rehabilitation Using a Stretch-Sensor Glove by Adhe Rahmatullah Sugiharto Suwito P, Ayumi Ohnishi, Tsutomu Terada, Masahiko Tsukamoto

“…The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. …”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…MBAUNet also effectively distinguished aquatic vegetation, turbid waters, clouds, and low-density CyanoHABs, maintaining a low error rate of 5.8% across varied environments. When applied to other lakes, MBAUNet consistently delivered over 90% precision. …”
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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

“…(b) In general, with an increase in SD, the mean absolute error of SD retrievals has increased in all SD products/models. …”
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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

“…The RF regression model outperformed other ML models, achieving the lowest mean square error (MSE = 0.038) on internal validation. The most significant predictors of VA were postoperative MH closure status (variable importance = 43.078) and MH area index (21.328). …”
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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

“…The various sets of selected environmental factors were able to capture the main spatial patterns of the disease risk, but the jittering increased the prediction error. The parameter estimates of the effects of socio-economic factors and intervention indicators were relatively stable in the simulated data. …”
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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

“…The evaluation metrics suggest that polynomial regression provides the highest accuracy among all the tested models, with the lowest mean squared error (MSE) value of 0.0001 and a perfect <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> score. …”
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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

“…The ANN proposed model achieves a high coefficient of determination (R2) of 0.999 and a low root-mean-square error (RMSE) of 0.0063 on the validation dataset. …”
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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

“…Abstract Background Major Depressive Disorder (MDD) is a leading cause of global disability often treated through a trial-and-error approach, yet treatment response to antidepressants remains highly variable, with remission rates below 50% after initial treatment. …”
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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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