Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective

Abubakar, Adamu and Ya’u Gital, Abdulsalam and Chiroma, Haruna and Rana, Nadim and Abdulhamid, Shafi’i and Muhammad, Amina Nuhu and Umar, Aishatu Yahaya (2019) Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective. In: Computer Vision Conference (CVC) 2019, 25-26 April 2019, Las Vegas, Nevada.

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Abstract

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The application of deep learning in computer vision has recently gain popularity. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. The application areas of the hybrid of natured inspired algorithms and deep learning architecture includes: machine vision and learning, image processing, data science, autonomous vehicles, medical image analysis, biometrics, etc. In this paper, we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	7132/74259
Uncontrolled Keywords:	deep learning, deep belief network, cuckoo search algorithm Convolutional neural network � Firefly algorithm � Nature inspired algorithms
Subjects:	Q Science > Q Science (General) > Q300 Cybernetics > Q350 Information theory
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Adamu Abubakar
Date Deposited:	24 Oct 2019 13:19
Last Modified:	17 Aug 2020 12:02
URI:	http://irep.iium.edu.my/id/eprint/74259

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