A Meta-Analytical Review of Deep Learning Prediction Models for Big Data

View Sample PDF

Author(s): Parag Verma (Chitkara University Institute of Engineering and Technology, Chitkara University, India), Vaibhav Chaudhari (Nutanix Technologies India Pvt. Ltd., Bengaluru, India), Ankur Dumka (Women Institute of Technology, Dehradun, India & Graphic Era University (Deemed), Dehradun, India)and Raksh Pal Singh Gangwar (Women Institute of Technology, India)
Copyright: 2023
Pages: 26
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch023

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

Purchase

View A Meta-Analytical Review of Deep Learning Prediction Models for Big Data on the publisher's website for pricing and purchasing information.

Abstract

The article presents an introductory review of various approaches of deep learning including convolutional neural networks (CNNs), deep belief networks (DBNs), and auto-encoders (AEs). Each of these deep learning models is currently being used effectively in various fields such as medical application with healthcare systems, clinical trials, pharmacy industry, finance, agribusiness, energy industries, etc., and these models and all these models are extremely essential for any data scientist's toolbox. These deep learning models must build classes that should be flexibly designed, which can be useful in building new oriented application structure designs. Subsequently, for future development in the artificial intelligence-based technological world, it is important to have a necessary understanding of these deep learning models, which have been attempted to be refined through this systematic meta-analysis.

The IRMA Community

Research IRM

A Meta-Analytical Review of Deep Learning Prediction Models for Big Data

Purchase

Abstract

Related Content

IRMA Sponsors