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Electronics
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AI Technologies and Smart City
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AI Technologies and Smart City

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

Deadline for manuscript submissions: 16 September 2024 | Viewed by 14251

Special Issue Editors

Prof. Dr. Young Im Cho

E-Mail Website
Guest Editor
Department of Computer Engineering, Gachon University, Seongnam 13415, Republic of 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

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eric Matson

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Khalid Saeed

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demand for AI technologies has 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;
  • 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;
  • AI standardization;
  • Brain–computer interfaces.

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 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

  • AI technologies (agents, modeling, etc.)
  • deep learning technologies
  • anticipation
  • expectations
  • AI applications
  • explainable AI
  • smart city (theory, model, platform, etc.)
  • smart city technologies inside
  • energy, traffic, and many applications in smart city
  • amart city applications
  • AI industrial applications
  • intelligent monitoring system
  • AI standardization
  • brain–computer interfaces

Published Papers (9 papers)

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Research

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20 pages, 2965 KiB  
Article
Predicting People’s Concentration and Movements in a Smart City
by Joao C. Ferreira, Bruno Francisco, Luis Elvas, Miguel Nunes and Jose A. Afonso
Electronics 2024, 13(1), 96; https://doi.org/10.3390/electronics13010096 - 25 Dec 2023
Viewed by 623
Abstract
With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within [...] Read more.
With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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24 pages, 10518 KiB  
Article
Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model
by Zhiwei Li, Anyu Zhang, Fangfang Han, Junchao Zhu and Yawen Wang
Electronics 2023, 12(13), 2915; https://doi.org/10.3390/electronics12132915 - 03 Jul 2023
Cited by 1 | Viewed by 992
Abstract
In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls [...] Read more.
In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls and lays out abnormal behaviors based on human skeletal key points and a spatio-temporal graph convolutional network (ST-GCN). Skeleton extraction of the human body in video sequences was performed using Alphapose. To resolve the problem of graph convolutional networks not being effective enough for skeletal key points feature aggregation, we propose an NAM-STGCN model that incorporates a normalized attention mechanism. By using the activation function PReLU to optimize the model structure, the improved ST-GCN model can more effectively extract skeletal key points action features in the spatio-temporal dimension for the purposes of abnormal behavior recognition. The experimental results show that our optimized model achieves a 96.72% accuracy for recognition on the self-built dataset, which is 4.92% better than the original model; the model loss value converges below 0.2. Tests were performed on the KTH and Le2i datasets, which are both better than typical classification recognition networks. The model can precisely identify abnormal human behaviors, facilitating the detection of abnormalities and rescue in a timely manner and offering novel ideas for smart site construction. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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13 pages, 4992 KiB  
Article
Efficient Face Region Occlusion Repair Based on T-GANs
by Qiaoyue Man and Young-Im Cho
Electronics 2023, 12(10), 2162; https://doi.org/10.3390/electronics12102162 - 09 May 2023
Viewed by 982
Abstract
In the image restoration task, the generative adversarial network (GAN) demonstrates excellent performance. However, there remain significant challenges concerning the task of generative face region inpainting. Traditional model approaches are ineffective in maintaining global consistency among facial components and recovering fine facial details. [...] Read more.
In the image restoration task, the generative adversarial network (GAN) demonstrates excellent performance. However, there remain significant challenges concerning the task of generative face region inpainting. Traditional model approaches are ineffective in maintaining global consistency among facial components and recovering fine facial details. To address this challenge, this study proposes a facial restoration generation network combined a transformer module and GAN to accurately detect the missing feature parts of the face and perform effective and fine-grained restoration generation. We validate the proposed model using different image quality evaluation methods and several open-source face datasets and experimentally demonstrate that our model outperforms other current state-of-the-art network models in terms of generated image quality and the coherent naturalness of facial features in face image restoration generation tasks. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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14 pages, 2300 KiB  
Article
Energy-Constrained UAV Data Acquisition in Wireless Sensor Networks with the Age of Information
by Jinxuan Xiong, Zhimin Li, Hongzhi Li, Lin Tang and Shaohong Zhong
Electronics 2023, 12(7), 1739; https://doi.org/10.3390/electronics12071739 - 06 Apr 2023
Cited by 2 | Viewed by 1064
Abstract
This paper considers a wireless sensor network (WSN) assisted by the unmanned aerial vehicle (UAV) in the Internet of Things (IoT). The UAV departs from the data center to the ground node to collect sensor node data as a relay. Under the constraints [...] Read more.
This paper considers a wireless sensor network (WSN) assisted by the unmanned aerial vehicle (UAV) in the Internet of Things (IoT). The UAV departs from the data center to the ground node to collect sensor node data as a relay. Under the constraints of battery energy, the UAV will travel to and from the data center repeatedly and transmit the collected sensor node data. The freshness of the node data received by the data center is measured by the Age of Information (AoI) as a performance metric. A genetic algorithm is used to plan the flight trajectory of the UAV. To ensure the data’s integrity and accuracy in a single sensor node, the UAV continuously collects sensor node data when the distance from the sensor node is less than the minimum acquisition distance. Through simulation experiments, we analyzed the influence of changing acquisition distance, the initial battery capacity, acquisition success probability, and transmission power on the peak age of information and the average age of information. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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14 pages, 2422 KiB  
Article
Transformer-Based User Alignment Model across Social Networks
by Tianliang Lei, Lixin Ji, Gengrun Wang, Shuxin Liu, Lan Wu and Fei Pan
Electronics 2023, 12(7), 1686; https://doi.org/10.3390/electronics12071686 - 03 Apr 2023
Cited by 2 | Viewed by 1468
Abstract
Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from [...] Read more.
Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from neighboring nodes through graph convolution, and embed social networks into the low-dimensional vector space. However, as the network depth increases, the effect of the model will decrease. Therefore, in order to better obtain the network embedding representation, a Transformer-based user alignment model (TUAM) across social networks is proposed. This model converts the node information and network structure information from the graph data form into sequence data through a specific encoding method. Then, it inputs the data to the proposed model to learn the low-dimensional vector representation of the user. Finally, it maps the two social networks to the same feature space for alignment. Experiments on real datasets show that compared with GAT, TUAM improved ACC@10 indicators by 11.61% and 16.53% on Facebook–Twitter and Weibo–Douban datasets, respectively. This illustrates that the proposed model has a better performance compared to other user alignment models. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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14 pages, 1932 KiB  
Article
Deep Learning Recommendations of E-Education Based on Clustering and Sequence
by Furkat Safarov, Alpamis Kutlimuratov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov and Young-Im Cho
Electronics 2023, 12(4), 809; https://doi.org/10.3390/electronics12040809 - 06 Feb 2023
Cited by 13 | Viewed by 2593
Abstract
Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational resources. Therefore, we propose a novel DNN [...] Read more.
Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational resources. Therefore, we propose a novel DNN method that combines synchronous sequences and heterogeneous features to more accurately generate candidates in e-learning platforms that face an exponential increase in the number of available online educational courses and learners. Mitigating the learners’ cold-start problem was also taken into consideration during the modeling. Grouping learners in the first phase, and combining sequence and heterogeneous data as embeddings into recommendations using deep neural networks, are the main concepts of the proposed approach. Empirical results confirmed the proposed solution’s potential. In particular, the precision rates were equal to 0.626 and 0.492 in the cases of Top-1 and Top-5 courses, respectively. Learners’ cold-start errors were 0.618 and 0.697 for 25 and 50 new learners. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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16 pages, 4442 KiB  
Article
A Linear Quadratic Regression-Based Synchronised Health Monitoring System (SHMS) for IoT Applications
by Divya Upadhyay, Puneet Garg, Sultan Mesfer Aldossary, Jana Shafi and Sachin Kumar
Electronics 2023, 12(2), 309; https://doi.org/10.3390/electronics12020309 - 06 Jan 2023
Cited by 7 | Viewed by 1657
Abstract
In recent days, the IoT along with wireless sensor networks (WSNs), have been widely deployed for various healthcare applications. Nowadays, healthcare industries use electronic sensors to reduce human errors while analysing illness more accurately and effectively. This paper proposes an IoT-based health monitoring [...] Read more.
In recent days, the IoT along with wireless sensor networks (WSNs), have been widely deployed for various healthcare applications. Nowadays, healthcare industries use electronic sensors to reduce human errors while analysing illness more accurately and effectively. This paper proposes an IoT-based health monitoring system to investigate body weight, temperature, blood pressure, respiration and heart rate, room temperature, humidity, and ambient light along with the synchronised clock model. The system is divided into two phases. In the first phase, the system compares the observed parameters. It generates advisory to parents or guardians through SMS or e-mails. This cost-effective and easy-to-deploy system provides timely intimation to the associated medical practitioner about the patient’s health and reduces the effort of the medical practitioner. The data collected using the proposed system were accurate. In the second phase, the proposed system was also synchronised using a linear quadratic regression clock synchronisation technique to maintain a high synchronisation between sensors and an alarm system. The observation made in this paper is that the synchronised technology improved the performance of the proposed health monitoring system by reducing the root mean square error to 0.379% and the R-square error by 0.71%. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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14 pages, 2160 KiB  
Article
Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders
by Fazliddin Makhmudov, Alpamis Kutlimuratov, Farkhod Akhmedov, Mohamed S. Abdallah and Young-Im Cho
Electronics 2022, 11(23), 4047; https://doi.org/10.3390/electronics11234047 - 06 Dec 2022
Cited by 17 | Viewed by 2275
Abstract
Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsibilities in terms of modeling to improve performance. Therefore, in this [...] Read more.
Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsibilities in terms of modeling to improve performance. Therefore, in this study, we developed a novel SER model via attention-oriented parallel convolutional neural network (CNN) encoders that parallelly acquire important features that are used for emotion classification. Particularly, MFCC, paralinguistic, and speech spectrogram features were derived and encoded by designing different CNN architectures individually for the features, and the encoded features were fed to attention mechanisms for further representation, and then classified. Empirical veracity executed on EMO-DB and IEMOCAP open datasets, and the results showed that the proposed model is more efficient than the baseline models. Especially, weighted accuracy (WA) and unweighted accuracy (UA) of the proposed model were equal to 71.8% and 70.9% in EMO-DB dataset scenario, respectively. Moreover, WA and UA rates were 72.4% and 71.1% with the IEMOCAP dataset. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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Review

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20 pages, 1988 KiB  
Review
Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications
by Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara and Qinghe Zheng
Electronics 2023, 12(24), 4925; https://doi.org/10.3390/electronics12244925 - 07 Dec 2023
Cited by 1 | Viewed by 1726
Abstract
The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed [...] Read more.
The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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