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Special Issue "AI and Smart City Technologies"

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 11392

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Special Issue Editors

Prof. Dr. Young Im Cho

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Guest Editor

Department of Computer Engineering, Gachon University, Seongnam-daero 1342, Korea
Interests: artificial intelligence; cloud service robot; intelligent data manipulation for service robots; machine learning; AI data standardization for service robot; robot agents; M2M; swarm intelligence for robots; cloud server system for robotics; robot intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Agostino Cortesi

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Guest Editor

Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari, Via Torino 155, 30170 Venice, Italy
Interests: static program analysis; software engineering; abstract interpretation; information flow security
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Prof. Dr. Eric Matson

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Guest Editor

Department of Computer and Information Technology, Purdue University, 401 North Grant Street, West Lafayette, IN 47907-2121, USA
Interests: multiagent systems and agent organizations; autonomous robotics and intelligent systems
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Prof. Dr. Khalid Saeed

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Guest Editor

1. Head of Department of Digital Media and Computer Graphics, Bialystok University of Technology, 15 351 Bialystok, Poland
2. Department of Computer Science and Electronics, Universidad de La Costa, Barranquilla 080002, Colombia
Interests: information theory and information technology; image processing
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Special Issue Information

Dear Colleagues,

The demand for AI technologies increased over the last decade. AI continues to emerge with new technologies. It is increasingly evolving into an Artificial General Intelligence (AGI) technology and has been applied to several domains. Smart City continues to develop in many areas as AI technology is applied. Research on applications as well as AI and smart city technologies is needed.

This Special Issue is focused on Artificial Intelligence and Smart City. It will include novel research results about technologies such as deep learning, anticipation, sensors, AGI, smart city applications etc. Attention will also be paid to their various industry applications.

The topics of interest include, but are not limited to, the following:

AI technologies (agents, modeling etc.);
Deep learning technologies;
Anticipation;
Expectations;
AI applications;
AGI (Artificial General Intelligence);
Explainable AI
Smart City (theory, model, platform etc.);
Smart City technologies inside;
Energy, traffic and many applications in Smart City;
Smart City applications;
AI industrial applications
Intelligent monitoring system.

Prof. Dr. Young Im Cho
Prof. Dr. Agostino Cortesi
Prof. Dr. Eric Matson
Prof. Dr. Khalid Saeed
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 2000 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
Smart City
AI applications
Regression
Anticipation

Published Papers (7 papers)

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Research

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Open AccessArticle

Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms

Faheem Khan

Ilhan Tarimer

Hathal Salamah Alwageed

Buse Cennet Karadağ

Muhammad Fayaz

Akmalbek Bobomirzaevich Abdusalomov

and

Young-Im Cho

Electronics 2022, 11(21), 3518; https://doi.org/10.3390/electronics11213518 - 29 Oct 2022

Cited by 9 | Viewed by 983

Abstract

This research aims to analyze the effect of feature selection on the accuracy of music popularity classification using machine learning algorithms. The data of Spotify, the most used music listening platform today, was used in the research. In the feature selection stage, features with low correlation were removed from the dataset using the filter feature selection method. Machine learning algorithms using all features produced 95.15% accuracy, while machine learning algorithms using features selected by feature selection produced 95.14% accuracy. The features selected by feature selection were sufficient for classification of popularity in established algorithms. In addition, this dataset contains fewer features, so the computation time is shorter. The reason why Big O time complexity is lower than models constructed without feature selection is that the number of features, which is the most important parameter in time complexity, is low. The statistical analysis was performed on the pre-processed data and meaningful information was produced from the data using machine learning algorithms. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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Open AccessArticle

Virtual Hairstyle Service Using GANs & Segmentation Mask (Hairstyle Transfer System)

Mohamed S. Abdallah

and

Young-Im Cho

Electronics 2022, 11(20), 3299; https://doi.org/10.3390/electronics11203299 - 13 Oct 2022

Cited by 1 | Viewed by 894

Abstract

The virtual hair styling service, which now is necessary for cosmetics companies and beauty centers, requires significant improvement efforts. In the existing technologies, the result is unnatural as the hairstyle image is serviced in the form of a ‘composite’ on the face image, image, extracts and synthesizing simple hair images. Because of complicated interactions in illumination, geometrical, and occlusions, that generate pairing among distinct areas of an image, blending features from numerous photos is extremely difficult. To compensate for the shortcomings of the current state of the art, based on GAN-Style, we address and propose an approach to image blending, specifically for the issue of visual hairstyling to increase accuracy and reproducibility, increase user convenience, increase accessibility, and minimize unnaturalness. Based on the extracted real customer image, we provide a virtual hairstyling service (Live Try-On service) that presents a new approach for image blending with maintaining details and mixing spatial features, as well as a new embedding approach-based GAN that can gradually adjust images to fit a segmentation mask, thereby proposing optimal styling and differentiated beauty tech service to users. The visual features from many images, including precise details, can be extracted using our system representation, which also enables image blending and the creation of consistent images. The Flickr-Faces-HQ Dataset (FFHQ) and the CelebA-HQ datasets, which are highly diversified, high quality datasets of human faces images, are both used by our system. In terms of the image evaluation metrics FID, PSNR, and SSIM, our system significantly outperforms the existing state of the art. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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Open AccessArticle

MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition

and

Electronics 2022, 11(19), 3228; https://doi.org/10.3390/electronics11193228 - 08 Oct 2022

Cited by 6 | Viewed by 1451

Abstract

Communication for hearing-impaired communities is an exceedingly challenging task, which is why dynamic sign language was developed. Hand gestures and body movements are used to represent vocabulary in dynamic sign language. However, dynamic sign language faces some challenges, such as recognizing complicated hand gestures and low recognition accuracy, in addition to each vocabulary’s reliance on a series of frames. This paper used MediaPipe in conjunction with RNN models to address dynamic sign language recognition issues. MediaPipe was used to determine the location, shape, and orientation by extracting keypoints of the hands, body, and face. RNN models such as GRU, LSTM, and Bi-directional LSTM address the issue of frame dependency in sign movement. Due to the lack of video-based datasets for sign language, the DSL10-Dataset was created. DSL10-Dataset contains ten vocabularies that were repeated 75 times by five signers providing the guiding steps for creating such one. Two experiments are carried out on our dataset (DSL10-Dataset) using RNN models to compare the accuracy of dynamic sign language recognition with and without the use of face keypoints. Experiments revealed that our model had an accuracy of more than 99%. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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Open AccessArticle

Transformer-Based GAN for New Hairstyle Generative Networks

and

Electronics 2022, 11(13), 2106; https://doi.org/10.3390/electronics11132106 - 05 Jul 2022

Viewed by 1122

Abstract

Traditional GAN-based image generation networks cannot accurately and naturally fuse surrounding features in local image generation tasks, especially in hairstyle generation tasks. To this end, we propose a novel transformer-based GAN for new hairstyle generation networks. The network framework comprises two modules: Face segmentation (F) and Transformer Generative Hairstyle (TGH) modules. The F module is used for the detection of facial and hairstyle features and the extraction of global feature masks and facial feature maps. In the TGH module, we design a transformer-based GAN to generate hairstyles and fix the details of the fusion part of faces and hairstyles in the new hairstyle generation process. To verify the effectiveness of our model, CelebA-HQ (Large-scale CelebFaces Attribute) and FFHQ (Flickr-Faces-HQ) are adopted to train and test our proposed model. In the image evaluation test used, FID, PSNR, and SSIM image evaluation methods are used to test our model and compare it with other excellent image generation networks. Our proposed model is more robust in terms of test scores and real image generation. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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Open AccessFeature PaperArticle

Development of a Hairstyle Conversion System Based on Mask R-CNN

Seong-Geun Jang

Qiaoyue Man

and

Young-Im Cho

Electronics 2022, 11(12), 1887; https://doi.org/10.3390/electronics11121887 - 15 Jun 2022

Viewed by 1076

Abstract

Interest in hairstyling, which is a means of expressing oneself, has increased, as has the number of people who are attempting to change their hairstyles. A considerable amount of time is required for women to change their hair back from a style that does not suit them, or for women to regrow their long hair after changing their hair to a short hairstyle that they do not like. In this paper, we propose a model combining Mask R-CNN and a generative adversarial network as a method of overlaying a new hairstyle on one’s face. Through Mask R-CNN, hairstyles and faces are more accurately separated, and new hairstyles and faces are synthesized naturally through the use of a generative adversarial network. Training was performed over a dataset that we constructed, following which the hairstyle conversion results were extracted. Thus, it is possible to determine in advance whether the hairstyle matches the face and image combined with the desired hairstyle. Experiments and evaluations using multiple metrics demonstrated that the proposed method exhibits superiority, with high-quality results, compared to other hairstyle synthesis models. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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Open AccessArticle

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Kuldoshbay Avazov

Mukhriddin Mukhiddinov

Fazliddin Makhmudov

and

Young Im Cho

Electronics 2022, 11(1), 73; https://doi.org/10.3390/electronics11010073 - 27 Dec 2021

Cited by 16 | Viewed by 4157

Abstract

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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Review

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Open AccessReview

Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions

Liliana Calderon-Benavides

Carlos Henriquez

and

Andres Mendoza

Electronics 2022, 11(23), 4015; https://doi.org/10.3390/electronics11234015 - 03 Dec 2022

Viewed by 542

Abstract

Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants. Full article

(This article belongs to the Special Issue AI and Smart City Technologies)

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