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Using Artificial Intelligence and Machine learning to Improve Data Quality in Cloud Native Data Pipelines

Introduction: Why Data Quality Is Harder Than Ever Data quality has always been important, but in today’s world of ...
EurekAlert!

A review on multi-view learning

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified ...
Scientific Research Publishing

Amin, F. and Mahmoud, M. (2022) Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial. Journal of Engineering Research, 6, Article No. 1.

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...
Frontiers

Migraine triggers, phases, and classification using machine learning models

1 San Juan Bautista School of Medicine, Caguas, Puerto Rico, United States 2 Independent Researcher, Monmouth County, NJ, United States Background: In many countries, patients with headache disorders ...
C&EN

Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Microplastics persist as ubiquitous environmental contaminants, and efficient methods ...
Scientific Research Publishing

Credit Score Classification Using Advanced Machine Learning: A Comprehensive Approach ()

1 Department of Computer Science, Rutgers University, New Brunswick, NJ, USA. 2 Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA. This paper presents a ...
IEEE

An Overview and Classification of Machine Learning Approaches for Radar Signal Deinterleaving

Abstract: Electronic Warfare (EW) receivers are passive systems that are designed to detect and identify active radar emitters in the environment. The radar pulses emitted by multiple sources are ...
IEEE

Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals

Abstract: Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve ...
Frontiers

Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review

Background and aim: Neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s) lead to neuronal loss; neurocognitive disorders (e.g., delirium, dementia) show cognitive decline. Early detection is ...