Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems

Article

Pandey, H., Bessis, N., Das, S., Windridge, D. and Chaudhary, A. 2020. Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems. Neural Computing and Applications. 32 (12), pp. 7743-7745. https://doi.org/10.1007/s00521-020-04903-w
TypeArticle
TitleEditorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems
AuthorsPandey, H., Bessis, N., Das, S., Windridge, D. and Chaudhary, A.
Abstract

Artificial intelligence (AI) has grown widely across domains. The current trends of AI and machine learning (ML) techniques are centered toward hybridization to improve performance of the systems. Our society will shortly become populated with intelligent systems that are able to perform tasks without human intervention and communicate with their surroundings, which totally transforming the century-old traditional human-based system. Despite the immense growth of the various AI and ML techniques, there are many challenges and threats which limit the performance of these techniques. However, due to the increasing demands, we have noticed the evolution of the intelligent systems which are empowered by AI and ML techniques, some of them are hybrid metaheuristic algorithms, classification, clustering, Bayesian hierarchical modeling, deep learning and re-enforcements learning. There exist several scientific projects in progress for the development/enhancement of intelligent systems. We also noticed that few on-going scientific projects are based on simulation hypothesis where biological inspired dream simulation mechanism has been incorporated to handle off-line simulation.

PublisherSpringer, London
JournalNeural Computing and Applications
ISSN0941-0643
Electronic1433-3058
Publication dates
Online18 Apr 2020
Print30 Jun 2020
Publication process dates
Deposited14 May 2021
Accepted31 Mar 2020
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1007/s00521-020-04903-w
Web of Science identifierWOS:000527905700001
LanguageEnglish
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