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Intelligent Systems for Multidisciplinary Applications in Era of Artificial Intelligence
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Intelligent Systems for Multidisciplinary Applications in Era of Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 1922

Special Issue Editors

Dr. Adnane Cabani

E-Mail Website
Guest Editor
ESIGELEC-IRSEEM, Normandie Université, 76000 Rouen, France
Interests: ITS; logistics; smart health; artificial intelligence; tracking
Dr. Kévin Bouchard

E-Mail Website
Guest Editor
Department of Computer Sciences and Mathematics, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
Interests: artificial intelligence; ambient intelligence; tracking; data science; spatial reasoning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the Era of Artificial Intelligence, intelligent systems are transforming various domains of our lives. The performance of intelligent systems is improved by providing more sophisticated reasoning skills, as well as the ability to learn and adapt to new information. Different application domains have evolved through AI, such as:

  • Autonomous vehicles that navigate roads, avoid obstacles, and make decisions in real time;
  • The use of surgical robots and AI to perform surgery with unrivalled precision and accuracy;
  • In the industrial sector, automating tasks, improving decision-making, optimizing processes, and increasing collaboration between humans and machines allows for the boosting of efficiency, productivity, sustainability, and adaptability;
  • Automation of many repetitive and time-consuming tasks, such as inventory management, route planning, and order tracking, with the aim of improving overall supply chain efficiency.

This call is focused on the latest research in intelligent systems in the following areas (but is not limited to):

  • Intelligent Transportation Systems;
  • Smart Healthcare;
  • Industry 4.0 and 5.0;
  • Logistics.

Dr. Adnane Cabani
Dr. Kévin Bouchard
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. Applied Sciences 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

  • artificial intelligence
  • intelligent systems
  • smart agriculture
  • smart city
  • smart transportation
  • smart mobility

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Published Papers (2 papers)

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Research

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22 pages, 2195 KiB  
Article
AtomGID: An Atomic Gesture Identifier for Qualitative Spatial Reasoning
by Kevin Bouchard and Bruno Bouchard
Appl. Sci. 2024, 14(12), 5301; https://doi.org/10.3390/app14125301 - 19 Jun 2024
Viewed by 448
Abstract
In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, [...] Read more.
In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements. Full article
(This article belongs to the Special Issue Intelligent Systems for Multidisciplinary Applications in Era of Artificial Intelligence)
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Review

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23 pages, 800 KiB  
Review
Generative Adversarial Networks in Business and Social Science
by Africa Ruiz-Gándara and Luis Gonzalez-Abril
Appl. Sci. 2024, 14(17), 7438; https://doi.org/10.3390/app14177438 - 23 Aug 2024
Viewed by 530
Abstract
Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only [...] Read more.
Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics. Full article
(This article belongs to the Special Issue Intelligent Systems for Multidisciplinary Applications in Era of Artificial Intelligence)
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