Special Issue "Advanced Applications of Computer Science and AI in Smart Cities and Societies"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 May 2021) | Viewed by 2349

Special Issue Editors

Prof. Dr. Pierluigi Siano
grade E-Mail Website
Guest Editor
Department of Management and Innovation Systems, University of Salerno, 84084 Salerno, Italy
Interests: smart grids; energy management; power systems; demand response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite submissions to a Special Issue of Applied Sciences on new paradigms in smart cities and societies entitled “Advanced Applications of Computer Science and AI in Smart Cities and Societies”. The penetration of data in the world is significantly increasing due to the need to apply different devices and applications to human activities in modern life. In order to effectively manage and control daily activities, such data should be considered appropriately in decision-making procedures to find optimum solutions. To achieve these purposes, computer science is used to help modern society in all aspects of building smart cities. Actually, implementing computer science applications in human societies is at the heart of modern smart cities, in which any disturbance might lead to unsuitable consequences, abnormal situations, security issues, and even disorder and chaos. In order to cope with ever-increasing data, complexity, and security in modern smart cities, new architectures, concepts, algorithms, and procedures are essential. This Special Issue aims to encourage researchers to address the technical issues and research gaps in applying computer science applications in smart cities to improve daily life in human societies. The topics of interest of this Special Issue include, but are not limited to:

  • Smart grids protection and security;
  • Smart grids stability;
  • Data-mining approaches in smart cities;
  • Issues in data analytics;
  • Advanced teaching using computer technologies;
  • Modern banking based on data analysis;
  • Machine learning applications in smart city hospitals;
  • Technical issues in learning algorithms;
  • Advances in optimization methods;
  • Data-mining architecture;
  • Artificial intelligence applications to improve smart city performance;
  • New paradigms for electricity infrastructures in smart cities;
  • Systems concepts;
  • Methodologies and applications of modern methods for image processing and pattern recognition;
  • New trends in communication links and data transfer;
  • Design, modeling, and management of networks;
  • Developing heuristic optimization algorithms;
  • New approaches in managing smart cities based on data analytics approach;
  • Communication issues in smart cities;
  • Monitoring, control, operation, stability, and security in smart cities;
  • Optimization of losses in sensor, electricity, and communication networks;
  • Managing water distribution in smart cities;
  • Modern electrical transportations, electrical vehicles, smart navigations, and map routing in modern smart cities;
  • Data-mining approaches in modern societies;
  • Cloud computing.

Prof. Dr. Pierluigi Siano
Dr. Hassan Haes Alhelou
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 2300 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 (2 papers)

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Research

Article
Automatic Handgun Detection with Deep Learning in Video Surveillance Images
Appl. Sci. 2021, 11(13), 6085; https://doi.org/10.3390/app11136085 - 30 Jun 2021
Cited by 6 | Viewed by 1138
Abstract
There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper [...] Read more.
There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training. Full article
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Article
Hybridization of the Stockwell Transform and Wigner Distribution Function to Design a Transmission Line Protection Scheme
Appl. Sci. 2020, 10(22), 7985; https://doi.org/10.3390/app10227985 - 11 Nov 2020
Cited by 6 | Viewed by 757
Abstract
The complexity of power system networks is increasing continuously due to the addition of high capacity transmission lines. Faults on these lines may deteriorate the power flow pattern in the network. This can be avoided by the use of effective protection schemes. This [...] Read more.
The complexity of power system networks is increasing continuously due to the addition of high capacity transmission lines. Faults on these lines may deteriorate the power flow pattern in the network. This can be avoided by the use of effective protection schemes. This paper presents an algorithm for detecting and classifying faults on the transmission network. Fault detection is achieved by utilizing the fault index, which depends on a combination of characteristics extracted from the current signal by the application of the Stockwell transform and Wigner distribution function (WDF). Various faults are categorized using the quantity of phases with a faulty nature. The fault events like phase to-ground (L-G), two phases (LL), two phases to-ground (LL-G), and three phases to-ground (LLL-G) are investigated in this study. The performance of the algorithm designed for the protection scheme is tested for the variations in the impedance during the fault event, variations in the angle of the fault incidence, different fault locations, the condition of the power flow in the reverse direction, the availability of noise, and the fault on the hybrid line consisting of two sections of underground cable and the overhead line. The algorithm is also analyzed for discriminating switching incidents from fault cases. A comparative study is used to establish the superiority of the proposed technique as compared to the Wavelet transform (WT) based protection scheme. The performance of the protection technique is established in MATLAB/Simulink software using a test network of the transmission line with two terminals. Full article
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