Which Machine Learning Models Are Most Used In Crypto Signal Generation?

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 ...

Machine learning workflow enables faster, more reliable organic crystal structure prediction

Prediction of crystal structures of organic molecules is a critical task in many industries, especially in pharmaceuticals ...
Frontiers

Artificial intelligence and deep learning for neural data analysis

Artificial intelligence and deep learning have revolutionized the field of neural data analysis in recent years. The explosion of complex, high-dimensional ...
BMJ Open

Analysis of risk factors associated with metabolic-associated fatty liver disease from Suzhong Region, China: a cross-sectional study

Objectives Metabolic-associated fatty liver disease (MAFLD) is becoming increasingly prevalent worldwide, however, early ...
IEEE

A Comprehensive Machine Learning Approach for Wild Fire Detection and Classification Using Squeezenet and Ridge Regularized Logistic Regression

Abstract: Wild fire is a serious environmental and socioeconomic menace and therefore any effort aimed at the early and accurate detection of wild fire has to be very useful. This paper proposes a ...
The American Journal of Managed Care

Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes

A machine learning–driven web tool based on 13 standard patient metrics demonstrates strong predictive performance for MASLD, ...
IEEE

Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media

Abstract: The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape which poses detrimental effects on individuals and broader societal frameworks. A viable approach ...