Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...

Being invited to present research at an international academic conference is an honor for any seasoned professional. But for ...

Stephen Whitelam, a researcher whose work spans thermodynamic theory and machine learning, has described a framework for ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

Fragments of DNA from long-extinct human relatives still circulate in modern genomes, and in some cases they do more than ...

This project implements an advanced Virtual Machine Placement (VMP) optimization system that leverages multi-objective genetic algorithms, machine learning predictions, blockchain technology, and ...

The compressive strength (CS) is the most important parameter in the design codes of reinforced concrete structures. The development of simple mathematical equations for the prediction of CS of ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

This project focuses on detecting cyber attacks using machine learning techniques. It employs various algorithms to analyze network traffic and identify potential threats in real-time.

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...