Special Issue "State-of-the-Art of Embedding AI Techniques for Designing and Building IoT Systems"

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

Deadline for manuscript submissions: 30 November 2022 | Viewed by 1550

Special Issue Editors

Dr. Vasile-Daniel Pavaloaia
E-Mail Website
Guest Editor
Department of Accounting, Business Information Systems and Statistics, Alexandru Ioan Cuza University of Iasi, Iași, Romania
Interests: neural networks; machine learning; deep learning; sentiment analysis; IoT systems; information systems for management; enterprise resource planning
Dr. Dragan Pamucar
E-Mail Website
Guest Editor
Department of logistics, University of Defence, Belgrade, Pavla Jurišića Šturma 33, 11000 Belgrade, Serbia
Interests: multi-criteria decision making problems; computational intelligence; sustainability neuro-fuzzy systems; fuzzy; rough and intuitionistic fuzzy set theory; neutrosophic theory
Special Issues, Collections and Topics in MDPI journals
Dr. Ionela Bacain
E-Mail Website
Guest Editor
Humber College Institute of Technology and Advanced Learning, Toronto, ON, Canada
Interests: information systems; artificial intelligence; IoT
Prof. Dr. Rodrigo Martin-Rojas
E-Mail Website
Guest Editor
University of Granada, Granada, Spain
Interests: IoT; information systems; innovation; quantitative research

Special Issue Information

Dear Colleagues,

In today’s high-tech environment, the natural symbiosis between Artificial Intelligence (AI) and the Internet of Things (IoT) is enriching people’s lives. Reaching the full potential of combining these two important domains, AI and IoT, requires a significant amount of research efforts from both academia and practice in order to identify the existing gaps and to develop new architectures, solutions, and technologies.

The Internet of Things can be perceived as the nexus of physical objects (i.e., things) equipped with different electronic devices and specialized software applications that facilitates the communication between them and the human user. Embedding AI into the software of regular electronic IoT devices transforms them into “intelligent” ones. The fortunate association of the two leads to enhanced equipment for the benefits of humankind.

This Special Issue of Electronics, entitled “State-of-the-Art of Embedding AI Techniques for Designing and Building IoT Systems” intends, as a main objective, to reunite in a single volume the most recent advances in the form of original research manuscripts and also reviews on relevant topics (for this Special Issue). Therefore, topics of interest for this Special Issue can include but are not limited to:

  • Intelligent electronic solutions for the applications of the future;
  • IoT smart devices;
  • Smart cities, smart offices, smart homes, smart electronics;
  • Machine learning techniques for intelligent software development;
  • Advanced features of power systems;
  • Fuzzy systems;
  • Neural networks.

Dr. Vasile-Daniel Pavaloaia
Prof. Dr. Dragan Pamucar
Dr. Ionela Bacain
Prof. Dr. Rodrigo Martin-Rojas
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

  • Intelligent electronic solutions for the applications of the future
  • IoT smart devices
  • Smart cities, smart offices, smart homes, smart electronics
  • Machine learning techniques for intelligent software development
  • Advanced features of power systems
  • Fuzzy systems
  • Neural networks

Published Papers (1 paper)

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Systematic Review
Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review
Electronics 2021, 10(23), 2997; https://doi.org/10.3390/electronics10232997 - 01 Dec 2021
Cited by 1 | Viewed by 859
Abstract
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making [...] Read more.
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects. Full article
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