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Applied Sciences
Special Issues
Neural Computing: Theory, Methods and Applications
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Neural Computing: Theory, Methods and Applications

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 July 2022) | Viewed by 2533

Special Issue Editors

Prof. Dr. Piotr A. Kowalski

E-Mail Website
Guest Editor
1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Cracow, Poland
2. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Interests: data mining; artificial intelligence; computational intelligence; neural networks; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Szymon Lukasik

E-Mail Website
Guest Editor
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Interests: exploratory data analysis; parallel processing; metaheuristics; natural computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to contribute to a Special Issue of the journal Applied Sciences, entitled “Neural Computing: Theory, Methods and Applications”. This issue aims to present recent developments in the field of the theory and application of neural networks techniques.

Computational intelligence, as a subset of artificial intelligence, is one of the most dynamically developing fields of computer science. This is due to the need to analyze increasingly larger data sets, which each time require the use of more efficient computer equipment and the use of more advanced methods of data analysis and mining. One of the most powerful computational intelligence tools is artificial neural networks. These are structures inspired by the biological neural networks that make up the brain. A structure of this type learns to perform tasks thanks to given examples. At the present time, there is extensive interest in intelligent methods in application and theoretical fields. The above is visible by witnessing the continuous introduction of newer topological structures and teaching algorithms into this scientific domain. In particular, this dynamic is visible in the field of deep learning structures and procedures. Due to their specific plasticity and scalability, a tool called “artificial neural networks” can be found to be employed in a very wide range of applications.

We thus invite you to submit your research on these topics—in the form of original research papers, mini-reviews and perspective articles.

Prof. Dr. Piotr A. Kowalski
Prof. Dr. Szymon Lukasik
Guest Editors

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. Applied Sciences 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 2400 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

  • data science
  • machine learning
  • neural computing
  • artificial neural network—theory and practice
  • deep learning algorithms
  • learning system and procedures
  • neural image analysis
  • hybrid intelligent systems
  • intelligent agents
  • neural control systems
  • neural diagnostics
  • neural forecasting
  • natural language processing

Published Papers (1 paper)

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Research

25 pages, 1261 KiB  
Article
Discriminating and Clustering Ordered Permutations Using Artificial Neural Networks: A Potential Application in ANN-Guided Genetic Algorithms
by Syeda M. Tahsien and Fantahun M. Defersha
Appl. Sci. 2022, 12(15), 7784; https://doi.org/10.3390/app12157784 - 02 Aug 2022
Cited by 1 | Viewed by 1391
Abstract
Traveling salesman, linear ordering, quadratic assignment, and flow shop scheduling are typical examples of permutation-based combinatorial optimization problems with real-life applications. These problems naturally represent solutions as an ordered permutation of objects. However, as the number of objects increases, finding optimal permutations is [...] Read more.
Traveling salesman, linear ordering, quadratic assignment, and flow shop scheduling are typical examples of permutation-based combinatorial optimization problems with real-life applications. These problems naturally represent solutions as an ordered permutation of objects. However, as the number of objects increases, finding optimal permutations is extremely difficult when using exact optimization methods. In those circumstances, approximate algorithms such as metaheuristics are a plausible way of finding acceptable solutions within a reasonable computational time. In this paper, we present a technique for clustering and discriminating ordered permutations with potential applications in developing neural network-guided metaheuristics to solve this class of problems. In this endeavor, we developed two different techniques to convert ordered permutations to binary-vectors and considered Adaptive Resonate Theory (ART) neural networks for clustering the resulting binary vectors. The proposed binary conversion techniques and two neural networks (ART-1 and Improved ART-1) are examined under various performance indicators. Numerical examples show that one of the binary conversion methods provides better results than the other, and Improved ART-1 is superior to ART-1. Additionally, we apply the proposed clustering and discriminating technique to develop a neural-network-guided Genetic Algorithm (GA) to solve a flow-shop scheduling problem. The investigation shows that the neural network-guided GA outperforms pure GA. Full article
(This article belongs to the Special Issue Neural Computing: Theory, Methods and Applications)
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