IEEE Access Special Section Editorial: Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications

Author(s)

Peter Peer, University of Ljubljana
Carlos M. Travieso-Gonzalez, University of Las Palmas de Gran Canaria
Vijayan K. Asari, University of DaytonFollow
Malay Kishore Dutta, Dr. A.P.J. Abdul Kalam Technical University

Document Type

Article

Publication Date

2021

Publication Source

IEEE Access

Abstract

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

Inclusive pages

86657-86660

ISBN/ISSN

2169-3536

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/ACCESS.2021.3088621

Publisher

IEEE-Inst Electrical Electronics Engineers Inc

Volume

9

Peer Reviewed

yes

eCommons Citation

Peer, Peter; Travieso-Gonzalez, Carlos M.; Asari, Vijayan K.; and Dutta, Malay Kishore, "IEEE Access Special Section Editorial: Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications" (2021). Electrical and Computer Engineering Faculty Publications. 467.
https://ecommons.udayton.edu/ece_fac_pub/467