Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey

Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems cl...

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Main Authors: Talpur, N., Abdulkadir, S.J., Alhussian, H., Hasan, M.H., Aziz, N., Bamhdi, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.33262 /
Published: Springer Science and Business Media B.V. 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127983093&doi=10.1007%2fs10462-022-10188-3&partnerID=40&md5=675debd3bbac085540d2fc06d150a8d9
http://eprints.utp.edu.my/33262/
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spelling utp-eprints.332622022-07-26T06:31:44Z Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey Talpur, N. Abdulkadir, S.J. Alhussian, H. Hasan, M.H. Aziz, N. Bamhdi, A. Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future. © 2022, The Author(s), under exclusive licence to Springer Nature B.V. Springer Science and Business Media B.V. 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127983093&doi=10.1007%2fs10462-022-10188-3&partnerID=40&md5=675debd3bbac085540d2fc06d150a8d9 Talpur, N. and Abdulkadir, S.J. and Alhussian, H. and Hasan, M.H. and Aziz, N. and Bamhdi, A. (2022) Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey. Artificial Intelligence Review . http://eprints.utp.edu.my/33262/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
format Article
author Talpur, N.
Abdulkadir, S.J.
Alhussian, H.
Hasan, M.H.
Aziz, N.
Bamhdi, A.
spellingShingle Talpur, N.
Abdulkadir, S.J.
Alhussian, H.
Hasan, M.H.
Aziz, N.
Bamhdi, A.
Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
author_sort Talpur, N.
title Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
title_short Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
title_full Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
title_fullStr Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
title_full_unstemmed Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
title_sort deep neuro-fuzzy system application trends, challenges, and future perspectives: a systematic survey
publisher Springer Science and Business Media B.V.
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127983093&doi=10.1007%2fs10462-022-10188-3&partnerID=40&md5=675debd3bbac085540d2fc06d150a8d9
http://eprints.utp.edu.my/33262/
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score 11.62408

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