Special Issue "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: 30 September 2023 | Viewed by 2316

Special Issue Editors

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
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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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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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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 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 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

  • 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 (3 papers)

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Research

Article
Deep Learning Recommendations of E-Education Based on Clustering and Sequence
Electronics 2023, 12(4), 809; https://doi.org/10.3390/electronics12040809 - 06 Feb 2023
Cited by 1 | Viewed by 520
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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Article
A Linear Quadratic Regression-Based Synchronised Health Monitoring System (SHMS) for IoT Applications
Electronics 2023, 12(2), 309; https://doi.org/10.3390/electronics12020309 - 06 Jan 2023
Viewed by 703
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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Article
Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders
Electronics 2022, 11(23), 4047; https://doi.org/10.3390/electronics11234047 - 06 Dec 2022
Cited by 3 | Viewed by 756
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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