Special Issue "Neural Networks and Their Applications"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 31 October 2022 | Viewed by 143

Special Issue Editor

Prof. Dr. Mario Muñoz Organero
E-Mail Website
Guest Editor
Telecommunications Engineering, Carlos III University of Madrid, 28911 Leganes, Spain
Interests: neural networks; artificial intelligence; computer supported learning; sensor-based applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neural-network-based models have experienced continuous growth in complexity over the last few decades. The combination of high-performance computing resources (incorporating FPGAs and GPUs), distributed architectures and cloud computing, the availability of big data sources and datasets, and the increasing interest in the research community have created an unprecedented ecosystem to train complex models and apply them to solve many different real-life problems. From personal health recommenders, autonomous vehicles, and market sentiment analyses to natural language recognition or image recognition, different neural-network-based models are able to solve complex classification and regression problems. Deep neural networks have been developed to increase the ability to learn patterns from all kinds of data sources. Attention mechanisms have been able to go further in accuracy in combination with deep neural network based machine learning models.

This Special Issue aims to collect publications that will showcase the power and diversity of novel neural networks and how they can be applied to solve real cases. The application of neural networks in different domains will open the door for new scenarios and encourage their adoption and use by both the research community and the industry. The Special Issue welcomes high-quality papers both from a theoretical perspective and from a practical and experimental approach. The major challenges linked to the use of artificial neural networks to solve real problems will be tackled, and papers are expected to propose solutions to them using neural-network-based models.

Prof. Dr. Mario Muñoz Organero
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Neural networks
  • High-performance computing
  • Complexity
  • Complex models
  • Machine learning
  • Big data

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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