Tracing anglers in the Gulf Coast: New machine learning tools reveal when, where and how anglers fish

The Gulf Coast is recognized worldwide for its exceptional fishing opportunities, offering anglers a wide variety of species ...
SciELO

Obesity classification: a comparative study of machine learning models excluding weight and height data

OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
IEEE

Binary and Ternary Human Gait Phase Classification Using Machine Learning Algorithms

Abstract: Accurately identifying gait phases offers vital in-sights into medical diagnostics, requiring precise biomechanical assessment. Despite various machine learning (ML) methods, achieving high ...
Scientific Research Publishing

Research on Fault Detection and Classification of Industrial Equipment Based on Machine Learning ()

In the context of the rapid development of intelligent manufacturing, the stable operation of mechanical equipment is crucial for maintaining industrial production continuity and achieving economic ...
GitHub

sql-server-linux-setup-machine-learning-docker.md

This article explains how to install [SQL Server Machine Learning Services](../machine-learning/sql-server-machine-learning-services.md) on Docker. You can use ...
Frontiers

DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding

Cysteine S-carboxyethylation, a novel post-translational modification (PTM), plays a critical role in the pathogenesis of autoimmune diseases, particularly ankylosing spondylitis. Accurate ...
IEEE

Continual Learning for Object Classification: Integrating AutoML for Binary Classification Tasks Within a Modular Dynamic Architecture

Abstract: For humans it is quite easy to identify a new object after learning to identify existing ones, but not for a machine. Deep neural networks (DNN) are the foundation of the current ...