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

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

Parag Verma, Vaibhav Chaudhari, Ankur Dumka, Raksh Pal Singh Gangwar
Copyright: © 2023 |Pages: 26
ISBN13: 9781799892205|ISBN10: 1799892204|EISBN13: 9781799892212
DOI: 10.4018/978-1-7998-9220-5.ch023
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MLA

Verma, Parag, et al. "A Meta-Analytical Review of Deep Learning Prediction Models for Big Data." Encyclopedia of Data Science and Machine Learning, edited by John Wang, IGI Global, 2023, pp. 356-381. https://doi.org/10.4018/978-1-7998-9220-5.ch023

APA

Verma, P., Chaudhari, V., Dumka, A., & Gangwar, R. P. (2023). A Meta-Analytical Review of Deep Learning Prediction Models for Big Data. In J. Wang (Ed.), Encyclopedia of Data Science and Machine Learning (pp. 356-381). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch023

Chicago

Verma, Parag, et al. "A Meta-Analytical Review of Deep Learning Prediction Models for Big Data." In Encyclopedia of Data Science and Machine Learning, edited by John Wang, 356-381. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-7998-9220-5.ch023

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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.

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