Deep Learning: Hope or Hype

Mohiuddin Ahmed () and A. K. M. Najmul Islam ()
Additional contact information
Mohiuddin Ahmed: Edith Cowan University
A. K. M. Najmul Islam: University of Turku

Annals of Data Science, 2020, vol. 7, issue 3, No 4, 427-432

Abstract: Abstract In this paper, we investigate the literature around deep learning to identify its usefulness in different application domains. Our paper identifies that the effectiveness of deep learning is highly visible in the medical imaging area. Other application domains are yet to make any significant progress using deep learning. Therefore, we conclude that deep learning is a good solution for medical imaging analysis. However, its benefits are yet to be realized in other domains and researchers are pursuing to explore its effectiveness to solve problems in these domains. Our initial critical evaluation suggests that deep learning may be a hype in most domains. In order to probe this further, we call for a deeper engagement with prior literature in different application domains of deep learning.

Keywords: Artificial intelligence; Applications; Deep learning; Machine learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s40745-019-00237-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-019-00237-0

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-019-00237-0

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().