Abstract: Machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a ...

Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...

Mînzu, V. and Arama, I. (2025) A New Method to Predict the Mechanical Behavior for a Family of Composite Materials. Journal ...

Department of Chemistry, University of Houston, Houston, Texas 77204, United States Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, United States Department of ...

Debate continues over the role of artificial intelligence in treating mental health conditions, but new research shows that machine learning models can help predict whether a person might benefit from ...

A research team led by the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) has built and successfully demonstrated an automated experimentation platform to optimize the ...

State Key Laboratory of Bioinspired Interfacial Materials Science, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China ...

A collection of core Machine Learning algorithms implemented from scratch using only NumPy. This project focuses on understanding the inner workings of ML models without relying on libraries like ...

This study evaluates the predictive performance of traditional and machine learning-based models in forecasting NFL team winning percentages over a 21-season dataset (2003–2023). Specifically, we ...

Abstract: The study presents an advisorial system on crops using Machine Learning (ML) for the extreme challenge which is the selection of suitable crops for cultivation depending on the varying ...