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Symmetry
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Symmetry in Artificial Intelligence and Edge Computing
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Symmetry in Artificial Intelligence and Edge Computing

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 8085

Special Issue Editor

Dr. Sahraoui Dhelim

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
Interests: social computing; IoT; machine learning; blockchain; edge computing; VANET
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of mobile edge computing (MEC) and artificial intelligence (AI) has contributed many opportunities and challenges to research and industries. Many IoT applications can rely on AI, specifically machine learning and deep learning models, to perform various edge computing tasks, such as task offloading, distributed caching and quality of service optimization, with the coupling of MEC and AI mitigating the drawbacks of traditional cloud computing models, taking full advantage of the unexploited computing resources available in edge devices.

Edge-enabled AI algorithms can be leveraged to identify the presence and level of symmetry in various interdisciplinary applications, for instance, in the execution of AI algorithms in edge devices for symmetry detection in virtual reality scenes being a promising application. This Special Issue titled “Symmetry in Artificial Intelligence and Edge Computing” will focus on AI applications in edge computing.

The potential research topics include, but are not limited to, the following areas:

  • Edge-enable AI for symmetry/asymmetry applications;
  • Deep learning application for task offloading in mobile edge computing;
  • Distributed caching for mobile edge computing (MEC);
  • Artificial intelligence-enabled fog and edge computing;
  • Vehicular edge computing and vehicular cloud applications;
  • Blockchain caching for fog and edge computing;
  • Machine learning for quality of service (QoS) optimization in Internet of Things (IoT);
  • Artificial intelligence for distributed social networks;
  • Security and privacy in edge computing applications.

All papers submitted to the Special Issue will be thoroughly reviewed by at least two independent experts.

Submit your paper and select the Journal “Symmetry” and the Special Issue “Symmetry in Artificial Intelligence and Edge Computing” via: MDPI submission system. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Dr. Sahraou Dhelim
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.

Keywords

  • artificial intelligence
  • edge computing
  • fog computing
  • deep learning
  • cloud computing
  • machine learning
  • internet of things
  • task offloading
  • distributed caching

Published Papers (2 papers)

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Research

19 pages, 4093 KiB  
Article
FFireNet: Deep Learning Based Forest Fire Classification and Detection in Smart Cities
by Somaiya Khan and Ali Khan
Symmetry 2022, 14(10), 2155; https://doi.org/10.3390/sym14102155 - 14 Oct 2022
Cited by 35 | Viewed by 3921
Abstract
Forests are a vital natural resource that directly influences the ecosystem. Recently, forest fire has been a serious issue due to natural and man-made climate effects. For early forest fire detection, an artificial intelligence-based forest fire detection method in smart city application is [...] Read more.
Forests are a vital natural resource that directly influences the ecosystem. Recently, forest fire has been a serious issue due to natural and man-made climate effects. For early forest fire detection, an artificial intelligence-based forest fire detection method in smart city application is presented to avoid major disasters. This research presents a review of the vision-based forest fire localization and classification methods. Furthermore, this work makes use of the forest fire detection dataset, which solves the classification problem of discriminating fire and no-fire images. This work proposes a deep learning method named FFireNet, by leveraging the pre-trained convolutional base of the MobileNetV2 model and adding fully connected layers to solve the new task, that is, the forest fire recognition problem, which helps in classifying images as forest fires based on extracted features which are symmetrical. The performance of the proposed solution for classifying fire and no-fire was evaluated using different performance metrics and compared with other CNN models. The results show that the proposed approach achieves 98.42% accuracy, 1.58% error rate, 99.47% recall, and 97.42% precision in classifying the fire and no-fire images. The outcomes of the proposed approach are promising for the forest fire classification problem considering the unique forest fire detection dataset. Full article
(This article belongs to the Special Issue Symmetry in Artificial Intelligence and Edge Computing)
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19 pages, 9130 KiB  
Article
AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning
by Anas Bilal, Liucun Zhu, Anan Deng, Huihui Lu and Ning Wu
Symmetry 2022, 14(7), 1427; https://doi.org/10.3390/sym14071427 - 12 Jul 2022
Cited by 62 | Viewed by 7202
Abstract
Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR [...] Read more.
Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease is one of the world’s most common leading causes of blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit the early screening and treatment of DR and prevent vision loss caused by it. Researchers have proposed several systems to detect abnormalities in retinal images in the past few years. However, Diabetic Retinopathy automatic detection methods have traditionally been based on hand-crafted feature extraction from the retinal images and using a classifier to obtain the final classification. DNN (Deep neural networks) have made several changes in the previous few years to assist overcome the problem mentioned above. We suggested a two-stage novel approach for automated DR classification in this research. Due to the low fraction of positive instances in the asymmetric Optic Disk (OD) and blood vessels (BV) detection system, preprocessing and data augmentation techniques are used to enhance the image quality and quantity. The first step uses two independent U-Net models for OD (optic disc) and BV (blood vessel) segmentation. In the second stage, the symmetric hybrid CNN-SVD model was created after preprocessing to extract and choose the most discriminant features following OD and BV extraction using Inception-V3 based on transfer learning, and detects DR by recognizing retinal biomarkers such as MA (microaneurysms), HM (hemorrhages), and exudates (EX). On EyePACS-1, Messidor-2, and DIARETDB0, the proposed methodology demonstrated state-of-the-art performance, with an average accuracy of 97.92%, 94.59%, and 93.52%, respectively. Extensive testing and comparisons with baseline approaches indicate the efficacy of the suggested methodology. Full article
(This article belongs to the Special Issue Symmetry in Artificial Intelligence and Edge Computing)
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