Special Issue "Applied Artificial Neural Networks"
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 24504
Special Issue Editor

Interests: evolutionary computation; artificial neural networks; artificial intelligence; feature selection; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the years, there have been many attempts to understand, and subsequently imitate, the way humans try to solve problems, in order to help achieve the same kind of intelligent behavior.
Among these attempts, one of them has been especially successful: artificial neural networks, which simplify the functioning of one of the most complex organs in Nature: the brain. Through the interconnection of nodes and a learning process from examples, these networks provide excellent solutions in a diverse range of fields of research.
After overcoming a small bump in recent years, they have been revived under the name of Deep Neural Networks, which have the same basis and take advantage of the emergence of new learning algorithms and greater computational capabilities.
This Special Issue aims to accommodate, on the one hand, the latest theoretical advances in this field, such as new learning paradigms or new architectures, and, on the other hand, those more recent works in the scientific field where the authors have used any of the many types of available neural networks to reach the best results in their area(s): image or video processing, pattern recognition, forecasting, time-series processing, real-time decision systems, etc.
We kindly invite researchers and investigators to contribute their original research or review articles to this Special Issue.
Dr. Marcos Gestal
Guest Editor
Manuscript Submission Information
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Keywords
- artficial neural networks
- deep neural networks
- deep learning
- machine learning
- pattern matching
- artificial intelligence
- learning algorithms
- applications