Feature Review Papers in "Computing and Artificial Intelligence" Section

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2715

Special Issue Editors

Department of Computer Science, University of Salerno, 84084 Fisciano Salerno, Italy
Interests: design and analysis of algorithms; combinatorics; information theory
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi'an 710049, China
Interests: high-dimensional data analysis; statistical modeling; pattern classification; image denoising; image inpainting; seismic data analysis; first-arrival picking; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue on “Feature Review Papers in Section Computing and Artificial Intelligence” will collect review papers in all areas of interest covered by “Computing and Artificial Intelligence” (https://www.mdpi.com/journal/applsci/sections/computing_artificial_intelligence).

In recent years, various artificial intelligence and deep learning techniques have witnessed significant achievements in a large variety of fields, such as medical diagnosis, image processing, etc. To attain a good performance, it is a prerequisite to develop high-performance computing tools and technologies. The purpose of this Special Issue is to publish a set of high-quality review papers that present the state of the art in relevant sub-areas in the general fields of computing and artificial intelligence and that individuate and promote important directions for future research.

We welcome multidisciplinary research in the following fields:

  • Database, information systems, and security;
  • Multi-agent systems and pervasive computing;
  • Audio, speech, and music processing;
  • Computer vision, machine learning, and pattern recognition.

Manuscripts on computing and artificial intelligence are all welcome.

We expect these papers to be widely read and highly influential within the field. We also plan for all papers included in this Special Issue to be published in the form of a print edition book and widely promoted within the scientific community.

Prof. Dr. Ugo Vaccaro
Prof. Dr. Chun-Xia Zhang
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

  • social network analysis
  • data security
  • data compression and protection
  • sequential and distributed computing
  • deep learning
  • uncertainty quantification
  • machine learning
  • high-performance computing
  • reinforcement learning
  • variational inference

Published Papers (1 paper)

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Review

16 pages, 2442 KiB  
Review
Soft Computing Techniques Aware Clustering-Based Routing Protocols in Vehicular Ad Hoc Networks: A Review
by Manoj Sindhwani, Shippu Sachdeva, Krishan Arora, Taehyun Yoon, Daeseung Yoo, Gyanendra Prasad Joshi and Woong Cho
Appl. Sci. 2022, 12(15), 7922; https://doi.org/10.3390/app12157922 - 07 Aug 2022
Cited by 8 | Viewed by 2007
Abstract
The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several [...] Read more.
The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several recent studies have attempted to develop optimal clustering algorithms to improve network performance metrics using soft computing techniques. Although sufficient work on soft computing techniques has been carried out, it seems less commonplace to find an analysis of various algorithms’ network parameters together. This paper provides a systematic analysis of the clustering-based routing protocols used in vehicular networks that are aware of soft computing techniques. The categorization is performed according to various soft computing techniques: particle swarm optimization, k-means, neural networks, artificial bee colony, genetic algorithm, firefly algorithm, and fuzzy logic. A comparative study of soft computing strategies is also provided in the survey with a focus on their objectives, along with their strengths and limitations. This survey makes it easier for researchers to pick the required soft computing technique used in vehicular networks in order to improve metrics such as packet delivery ratio, end-to-end delay, throughput, cluster lifetime, and message overhead. Full article
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