Computer and Engineering Science and Symmetry: Review Papers

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11208

Special Issue Editor

Institute for Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: computer-aided surgery; multimode image segmentation; registration and reconstruction; computer-aided diagnosis; machine learning; deep learning
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Special Issue Information

Dear Colleagues,

As Associate Section Editor-in-Chief for “Computer and Engineering Science and Symmetry”, I am happy to announce the latest Special Issue, “Computer and Engineering Science and Symmetry: Review Papers”. This Special Issue aims to cover recent advances in computer and engineering science. The most up-to-date issues of mathematical modeling in control systems, information security, IoT, robotics, Big Data, machine learning, artificial intelligence, automated systems for data processing and control, nanoelectronics, optoelectronics and nanophotonics, plasma emission electronics, intelligent power electronics, and other areas of interest associated with computer
science and engineering are welcome.

There is a lot of work involved in applying symmetry and asymmetry in computer science and engineering problems. We would like to call for review papers in all related aspects of symmetry.

Prof. Dr. Guoyan Zheng
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. 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 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.

Published Papers (1 paper)

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Review

30 pages, 2425 KiB  
Review
Artificial Intelligence Methodologies for Data Management
by Joel Serey, Luis Quezada, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Rodrigo Ternero, Jorge Sabattin, Claudia Duran and Sebastian Gutierrez
Symmetry 2021, 13(11), 2040; https://doi.org/10.3390/sym13112040 - 29 Oct 2021
Cited by 12 | Viewed by 10698
Abstract
This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main [...] Read more.
This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning. Full article
(This article belongs to the Special Issue Computer and Engineering Science and Symmetry: Review Papers)
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