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Electronics
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Application of Machine Learning Technologies in Smart Cities
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Application of Machine Learning Technologies in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 7245

Special Issue Editors

Dr. Ana De-Las-Heras

E-Mail Website
Guest Editor
Higher Polytechnic School, Department of Engineering Design, University of Seville, 41011 Seville, Spain
Interests: engineering projects; intelligent connected product; Industry 4.0; sustainability; smart city; machine learning
Dr. Alejandro Carrasco Muñoz

E-Mail Website
Guest Editor
Department of Electronics Technology, School of Computer Engineering, University of Seville, 41011 Seville, Spain
Interests: intelligent systems in distributed industrial environments; cyber–physical systems security and privacy (IoT, IIoT, I4.0); smart city
Dr. Francisco Zamora-Polo

E-Mail Website1 Website2
Guest Editor
Higher Polytechnic School, Department of Engineering Design, University of Seville, 41011 Seville, Spain
Interests: engineering projects; SDGs; sustainability; machine learning; smart cities; product design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, urban growth has increased exponentially, but urban planning and management have not undergone the necessary changes to generate and transform urban spaces into safer, more efficient, transformable, inclusive, and sustainable spaces. Smart cities represent an evolution towards the inclusion of digitalization within cities, enabling the application of improvement development techniques that increase the possibility of solving environmental problems. Furthermore, humanity is facing a global pandemic, COVID-19, which makes it even more difficult to develop improvements given this health crisis and its relationship with all aspects of city life. 

Therefore, given the opportunity for data management, processing, and interpretation that machine learning-based technologies possess, we are committed to their inclusion in the smart city in a global manner, allowing us to address sustainability from the conception of digital solutions that pave the way for efficient and transformable spaces in accordance with the Sustainable Development Goals (SGDs) and the guidelines set forth by the European Union's Agenda 2030. 

This Special Issue will include studies investigating the reliable implementation of machine learning technologies in smart cities to improve sustainability. The topics of interest include but are not limited to the following:

  • machine learning technologies·      
  • data and information processing·      
  • smart city-oriented data management systems·      
  • prediction and simulation of city areas to improve sustainability (transport, parking, public systems, etc.)

Dr. Ana De-Las-Heras
Dr. Alejandro Carrasco Muñoz
Dr. Francisco Zamora-Polo
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. Electronics 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

  • smart city
  • machine learning
  • data processing
  • prediction and simulation
  • sustainability
  • smart city—cybersecurity and privacy
  • SDGs
  • Internet of Things
  • product design

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Published Papers (2 papers)

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Research

13 pages, 3343 KiB  
Article
Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model
by Arfat Ahmad Khan, Muhammad Asif Nauman, Muhammad Shoaib, Rashid Jahangir, Roobaea Alroobaea, Majed Alsafyani, Ahmed Binmahfoudh and Chitapong Wechtaisong
Electronics 2022, 11(19), 3105; https://doi.org/10.3390/electronics11193105 - 28 Sep 2022
Cited by 17 | Viewed by 3859
Abstract
This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights [...] Read more.
This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights were adjusted through the backpropagation process. The first two CLs are followed by max-pool layer and batch normalization. The CLs produced features that are utilized to detect the anomaly in the image frame. The proposed model was evaluated using two parameters—Area Under the Curve (AUC) using Receiver Operator Characteristic (ROC) curve and overall accuracy. Three benchmark datasets comprised of numerous video frames with various abnormal and normal actions were used to evaluate the performance. Experimental results revealed that the proposed model outperformed other baseline studies on all three datasets and achieved 98% AUC using the ROC curve. Moreover, the proposed model achieved 95.6%, 98%, and 97% AUC on the CUHK Avenue, UCSD Ped-1, and UCSD Ped-2 datasets, respectively. Full article
(This article belongs to the Special Issue Application of Machine Learning Technologies in Smart Cities)
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30 pages, 3471 KiB  
Article
DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images
by Ahmed I. Shahin and Sultan Almotairi
Electronics 2021, 10(23), 2970; https://doi.org/10.3390/electronics10232970 - 29 Nov 2021
Cited by 4 | Viewed by 2416
Abstract
Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning [...] Read more.
Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images. Full article
(This article belongs to the Special Issue Application of Machine Learning Technologies in Smart Cities)
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