The method will be a combination of a number of ML algorithms, and the method ... to identify the valid ones. Real-life models, e.g., logistic regression, K-nearest neighbors, decision trees, random ...

Abstract: Flooding is a significant and recurrent issue that poses severe risks to communities, economies, and environments. Traditional flood prediction methods, which often rely on fixed thresholds, ...

In a proud moment for Assam and the entire Northeast, 16-year-old Huma Abia Kanta, a class 12 student of Royal Global School, ...

Critical concerns regarding the security and privacy of information transmitted within Internet of Medical Things systems have increased greatly ...

As climate change produces ever more heat waves, how many homes in the U.S. lack adequate cooling? Who's most vulnerable to ...

As climate change produces ever more heat waves, how many homes in the U.S. lack adequate cooling? Who's most vulnerable to lethal ...

Machine learning is transforming how crypto traders create and understand signals. From supervised models such as Random Forests and Gradient Boosting Machines to sophisticated deep learning hybrids ...

Wang, Z. (2025) Research on Prediction of Air Quality CO Concentration Based on Python Machine Learning. Advances in Internet ...

Abstract: This study convincingly demonstrates the immense potential of Random Forest algorithms to significantly enhance usability in edge computing environments by concentrating on critical ...

Advanced UAV sensor integration and machine learning may improve corn AGB predictions, providing scalable solutions for ...

Demand forecasting remains one of the most complex challenges in retail management. As consumer behavior evolves rapidly, ...