Special Issue "Modern Artificial Generative Intelligence: Multi-Agent Technologies and Deep Learning"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 1016

Special Issue Editors

1. Faculty of Mathematics and Mechanics, Science Educational Center “Mathematical Robotics and Artificial Intelligence”, Saint Petersburg State University, 199034 St. Petersburg, Russia
2. Software Engineering Department, ORT Braude College, Karmiel 21982, Israel
Interests: data mining; text mining; computational biology; patter recognition; probability theory
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science, Samara State Technical University, Samara, Russia
Interests: complex adaptive systems; autonomous systems; artificial intelligence; multi-agent technology; ontologies
Special Issues, Collections and Topics in MDPI journals
Department of Mathematics and Mechanics, Saint-Petersburg State University, Saint Petersburg, Russia
Interests: adaptive control; optimization; randomized algorithms; multiagent systems; Artificial Intelligence

Special Issue Information

Dear Colleagues,

Combinations of ontologies, multiagent technologies, and deep learning appear to be fruitful future directions for modern science. We are holding a conference to promote research in artificial intelligence and foster scientific exchange between researchers, practitioners, scientists, students, and engineers in artificial intelligence and its affiliated disciplines and it offers an outstanding opportunity for academic and industrial societies to discuss new challenges and spread modern approaches. The program is supposed to contain plenary lectures and regular sessions. The best papers will be recommended for publication in a Special Issue of the journal “Symmetry” MDPI. 

The Preliminary Organizing Committee includes: 

Conference Chair: Prof. Petr Skobelev (Samara State Technical University, Samara, Russia);

Program Committee Chair: Prof. Zeev Volkovich (ORT Braude College, Software Engineering Department, Karmiel, Israel).

Program Committee Members: 

  • Yves Demazeau (University of Grenoble, France);
  • Oleg Granichin (The Computer Science Department of Mathematics and Mechanics Faculty of Saint Petersburg State University, St. Petersburg, Russia); 
  • Vladimir Marik (Czech Institute of Cybernetics, Informatics, and Robotics, Prague);
  • Paolo Leitao (Braganca University, Portugal); 
  • Jörg P. Müller (Technical University, Clausthal, Germany); 
  • George Rzevski (Open University, UK and Rzevski Research Ltd., UK);   
  • Miroslav Svitek (Czech Technical University, Prague);  
  • Damien Trentesaux (Université Polytechnique Hauts-de-France).  

Prof. Dr. Zeev Vladimir Volkovich
Prof. Dr. Petr Skobelev
Prof. Dr. Oleg N. Granichin
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. Symmetry is an international peer-reviewed open access monthly 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 2000 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.

Published Papers (1 paper)

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Research

Article
A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks
Symmetry 2023, 15(2), 358; https://doi.org/10.3390/sym15020358 - 29 Jan 2023
Cited by 1 | Viewed by 621
Abstract
Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways [...] Read more.
Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%). Full article
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