Applied Machine Learning in Intelligent Systems

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 1135

Special Issue Editors


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Guest Editor
Department of AI and Software, Gachon University, Seongnam-si 13120, Republic of Korea
Interests: machine learning; immersive media; AI healthcare; AR/VR/MR/metaverse; 360-degree videos; intelligent systems

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Guest Editor
Department of Computer Science, Maynooth University, W23 F2K8 Maynooth, Ireland
Interests: machine learning; intelligent system; remote sensing; sustainability

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Guest Editor
Department of Electrical Engineering (ESAT), KU Leuven, 3000 Leuven, Belgium
Interests: quality of experience; artificial intelligence; virtual reality; computer vision; immersive media delivery

Special Issue Information

Dear Colleagues,

Currently, applications of machine learning technologies in intelligent systems are rapidly expanding in our daily life. It refers to the utilization of machine learning techniques and algorithms to develop intelligent systems that perform specific tasks, learn, adapt, and make intelligent decisions based on data. These systems have the potential to revolutionize various industries by automating tasks, optimizing processes, and enabling new levels of personalization and efficiency. This Special Issue demonstrates the application of machine learning in human–computer interactions, the virtual reality/metaverse, images and videos, AI healthcare, and human–robotic interactions. The aim is to showcase the transformative potential of machine learning techniques in augmenting the intelligence, adaptability, and interaction capabilities of a wide array of systems across these critical domains.

This Special Issue aims to provide an interdisciplinary discussion to share the recent advancements in different areas of machine learning in intelligent systems to publish high-quality research papers with an emphasis on new approaches and techniques for machine learning applications.

The Special Issue invites researchers, practitioners, and experts to contribute their original research articles and reviews on the following topics of interest, among others.

Human-Computer Interaction (HCI):  Emotion recognition and sentiment analysis for personalized HCI experiences, user behaviour modelling and prediction in interactive systems and adaptive interfaces, as well as intelligent user experience design.

Virtual Reality/Metaverse:  Machine-learning-driven content creation and adaptation in virtual and metaverse environments, gesture and motion recognition for immersive interactions, user-centric adaptation and real-time content delivery in virtual realms, as well as AI-enhanced simulations and training within virtual contexts.

Images and Videos:  Deep learning for image and video classification, segmentation, and recognition; object detection and tracking in complex visual scenes; content-based retrieval using machine learning techniques; and real-time image and video processing for intelligent systems.

AI Healthcare:  Diagnosis and prognosis using AI-powered medical imaging analysis, personalized treatment recommendations based on patient data, health monitoring, and wearable device integration for AI-driven healthcare.

Human-Robotic Interaction:  Collaborative human–robot teamwork and coordination, multimodal interaction for seamless human–robot engagement, gesture and speech recognition for intuitive robot control, and socially aware robots that adapt to human preferences and behaviour.

Dr. Muhammad Shahid Anwar
Dr. Muhammad Salman Pathan
Dr. Maria Torres Vega
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 healthcare
  • AI integration in VR/AR/MR/metaverse
  • natural language processing
  • image/video processing
  • HCI
  • immersive interaction
  • IoT and wearable devices
  • machine learning
  • applied AI
  • intelligent systems

Published Papers (1 paper)

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Research

19 pages, 2212 KiB  
Article
Design and Development of Multi-Agent Reinforcement Learning Intelligence on the Robotarium Platform for Embedded System Applications
by Lorenzo Canese, Gian Carlo Cardarilli, Mohammad Mahdi Dehghan Pir, Luca Di Nunzio and Sergio Spanò
Electronics 2024, 13(10), 1819; https://doi.org/10.3390/electronics13101819 - 8 May 2024
Viewed by 407
Abstract
This research explores the use of Q-Learning for real-time swarm (Q-RTS) multi-agent reinforcement learning (MARL) algorithm for robotic applications. This study investigates the efficacy of Q-RTS in the reducing convergence time to a satisfactory movement policy through the successful implementation of four and [...] Read more.
This research explores the use of Q-Learning for real-time swarm (Q-RTS) multi-agent reinforcement learning (MARL) algorithm for robotic applications. This study investigates the efficacy of Q-RTS in the reducing convergence time to a satisfactory movement policy through the successful implementation of four and eight trained agents. Q-RTS has been shown to significantly reduce search time in terms of training iterations, from almost a million iterations with one agent to 650,000 iterations with four agents and 500,000 iterations with eight agents. The scalability of the algorithm was addressed by testing it on several agents’ configurations. A central focus was placed on the design of a sophisticated reward function, considering various postures of the agents and their critical role in optimizing the Q-learning algorithm. Additionally, this study delved into the robustness of trained agents, revealing their ability to adapt to dynamic environmental changes. The findings have broad implications for improving the efficiency and adaptability of robotic systems in various applications such as IoT and embedded systems. The algorithm was tested and implemented using the Georgia Tech Robotarium platform, showing its feasibility for the above-mentioned applications. Full article
(This article belongs to the Special Issue Applied Machine Learning in Intelligent Systems)
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