Special Issue "Advances in Computing, Communication & Security"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2022 | Viewed by 2001

Special Issue Editors

Dr. Amjad Gawanmeh
E-Mail Website
Guest Editor
1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
2. College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates
Interests: electrical and computer engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Vishal Kumar
E-Mail Website
Guest Editor
Bipin Tripathi Kumaon Institute of Technology, Dwarahat 263653, India
Interests: soft computing in data mining; information security with reference to privacy, security, spam filtration; data scheduling approaches in vehicular ad hoc networks from the points of view of hardware, computational paradigms, and computing applications

Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the 6th International Conference on Computing, Communication & Security (ICCCS-2021) & 7th International Conference on Advances in Computing and Communication Engineering. Initiated in 2015, the annual ICCCS conference is for the computer, communication and signal processing fields. ICCCS 2021 is to be held in Las Vegas, NV, USA during October 4-6, 2021. Initiated in 2014, ICACCE will provide a forum for researchers and engineers in both academia and industry to exchange the latest innovations and research advancements in Innovative Computing, Communication and Engineering. The conference aims at bringing together researchers and practitioners in the world working on computing, communications and security aspects of communication, networks, and signal processing, providing a forum to present and discuss emerging ideas and trends in this highly challenging research field. Several pioneer researchers including IEEE fellows and industrialist persons will be present for delivering future research directions.

However, for ICCCS 2021, we would like to put an emphasis on deep learning, as it has been used successfully in many applications, and is currently considered one of the most cutting-edge machine learning and AI techniques. For ICACCE 2021, we would like to put an emphasis on identifying the applications of deep learning, machine learning and artificial intelligence on emerging research topics, as well as the future development directions in the field of computing and communication engineering.

Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Dr. Amjad Gawanmeh
Dr. Vishal Kumar
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. Information 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 1400 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

  • deep learning
  • machine learning
  • data mining
  • natural language processing
  • planning
  • knowledge representation
  • multi-agent systems
  • robotics
  • image processing

Published Papers (3 papers)

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Research

Article
LoRaWAN Based Indoor Localization Using Random Neural Networks
Information 2022, 13(6), 303; https://doi.org/10.3390/info13060303 - 16 Jun 2022
Viewed by 308
Abstract
Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the [...] Read more.
Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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Article
An Accurate Detection Approach for IoT Botnet Attacks Using Interpolation Reasoning Method
Information 2022, 13(6), 300; https://doi.org/10.3390/info13060300 - 14 Jun 2022
Viewed by 355
Abstract
Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, [...] Read more.
Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, cost-effective operations, better business opportunities, etc. However, there are many challenges facing implementing an IoT smart environment. The most critical challenge is protecting the IoT smart environment from different attacks. The IoT Botnet attacks are considered a serious challenge. The danger of this attack lies in that it could be used for several threatening commands. Therefore, the Botnet attacks could be implemented to perform the DDoS attacks, phishing attacks, spamming, and other attack scenarios. This paper has introduced a detection approach against the IoT Botnet attacks using the interpolation reasoning method. The suggested detection approach was implemented using the interpolation reasoning method instead of the classical reasoning methods to handle the knowledge base issues and reduce the size of the detection fuzzy rules. The suggested detection approach was designed, tested, and evaluated using an open-source benchmark IoT Botnet attacks dataset. The implemented experiments show that the suggested detection approach was able to detect the IoT Botnet attacks effectively with a 96.4% detection rate. Furthermore, the obtained results were compared with other literature results; the accomplished comparison showed that the suggested method is a rivalry with other methods, and it effectively reduced the false positive rate and interpolated the IoT Botnet attacks alerts even in case of a sparse rule base. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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Article
A Comparison of Machine Learning Techniques for the Quality Classification of Molded Products
Information 2022, 13(6), 272; https://doi.org/10.3390/info13060272 - 26 May 2022
Viewed by 500
Abstract
The developments in the internet of things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS) are paving the way to the implementation of smart factories in what is commonly recognized as the fourth industrial revolution. In the manufacturing sector, these technological advancements are [...] Read more.
The developments in the internet of things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS) are paving the way to the implementation of smart factories in what is commonly recognized as the fourth industrial revolution. In the manufacturing sector, these technological advancements are making Industry 4.0 a reality, with data-driven methodologies based on machine learning (ML) that are capable of extracting knowledge from the data collected by sensors placed on production machines. This is particularly relevant in plastic injection molding, with the objective of monitoring the quality of molded products from the parameters of the production process. In this regard, the main contribution of this paper is the systematic comparison of ML techniques to predict the quality classes of plastic molded products, using real data collected during the production process. Specifically, we compare six different classifiers on the data coming from the production of plastic road lenses. To run the comparison, we collected a dataset composed of the process parameters of 1451 road lenses. On such samples, we tested a multi-class classification, providing a statistical analysis of the results as well as of the importance of the input features. Among the tested classifiers, the ensembles of decision trees, i.e., random forest and gradient-boosted trees (GBT), achieved 95% accuracy in predicting the quality classes of molded products, showing the viability of the use of ML-based techniques for this purpose. The collected dataset and the source code of the experiments are available in a public, open-access repository, making the presented research fully reproducible. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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