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Electronics
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Advanced AR/VR Technologies and Machine Learning Applications in Smart Environments
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Advanced AR/VR Technologies and Machine Learning Applications in Smart Environments

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 2251

Special Issue Editor

Prof. Dr. Tae-Sun Chung

E-Mail Website
Guest Editor
Department of Software, Ajou University, Suwon 16499, Republic of Korea
Interests: database systems; data mining; machine learning; VR/AR system; flash memory storage; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

VR/AR technology has presented new challenges in many application fields, such as metaverse systems, cyber education, and artificial intelligence. In particular, due to COVID-19, VR/AR-based non-face-to-face systems have become more important technologies. In addition, VR/AR technology is closely related to artificial intelligence systems.

In this special issue, we invite original research papers and review articles that consider VR/AR technology and machine learning systems. New system architectures for VR/AR applications and machine learning systems can be addressed from a systems point of view. In particular, the main challenge in this field is related to how VR/AR and machine learning applications might be performed in a limited resource environment, such as a smartphone. From an application perspective, it can handle a wide variety of new applications. For example, you might create virtual humans, avatars, and psychologists, based on AR/VR technology.

Potential topics include, but are not limited, to the following: 

 Topic

  • VR/AR applications
  • Machine learning system
  • New VR/AR applications
  • System support for VR/AR systems
  • Performance analysis for VR/AR system
  • Natural language processing
  • Speech recognition system
  • Intelligent virtual human/robot system

Prof. Dr. Tae-Sun Chung
Guest Editor

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

  • VR/AR
  • machine learning
  • virtual human/robot
  • natural language processing
  • speech recognition
  • VR/AR psychologist

Published Papers (2 papers)

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Research

17 pages, 2471 KiB  
Article
Immersive Emotion Analysis in VR Environments: A Sensor-Based Approach to Prevent Distortion
by Jae-Hong Joo, Seung-Hyun Han, Inyoung Park and Tae-Sun Chung
Electronics 2024, 13(8), 1494; https://doi.org/10.3390/electronics13081494 - 14 Apr 2024
Viewed by 431
Abstract
As virtual reality (VR) technology advances, research has focused on enhancing VR content for a more realistic user experience. Traditional emotion analysis relies on surveys, but they suffer from delayed responses and decreased immersion, leading to distorted results. To overcome these limitations, we [...] Read more.
As virtual reality (VR) technology advances, research has focused on enhancing VR content for a more realistic user experience. Traditional emotion analysis relies on surveys, but they suffer from delayed responses and decreased immersion, leading to distorted results. To overcome these limitations, we propose an emotion analysis method using sensor data in the VR environment. Our approach can take advantage of the user’s immediate response and not reduce immersion. Linear regression, classification analysis, and tree-based methods were applied to electrocardiogram and galvanic skin response (GSR) sensor data to measure valence and arousal values. We introduced a novel emotional dimension model by analyzing correlations between emotions and the valence and arousal values. Experimental results demonstrated the highest accuracy of 77% and 92.3% for valence and arousal prediction, respectively, using GSR sensor data. Furthermore, an accuracy of 80.25% was achieved in predicting valence and arousal using nine emotions. Our proposed model improves VR content through more accurate emotion analysis in a VR environment, which can be useful for targeting customers in various industries, such as marketing, gaming, education, and healthcare. Full article
(This article belongs to the Special Issue Advanced AR/VR Technologies and Machine Learning Applications in Smart Environments)
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14 pages, 1686 KiB  
Article
Dataset Bias Prediction for Few-Shot Image Classification
by Jang Wook Kim, So Yeon Kim and Kyung-Ah Sohn
Electronics 2023, 12(11), 2470; https://doi.org/10.3390/electronics12112470 - 30 May 2023
Cited by 1 | Viewed by 1430
Abstract
Dataset bias is a significant obstacle that negatively affects image classification performance, especially in few-shot learning, where datasets have limited samples per class. However, few studies have focused on this issue. To address this, we propose a bias prediction network that recovers biases [...] Read more.
Dataset bias is a significant obstacle that negatively affects image classification performance, especially in few-shot learning, where datasets have limited samples per class. However, few studies have focused on this issue. To address this, we propose a bias prediction network that recovers biases such as color from the extracted features of image data, resulting in performance improvement in few-shot image classification. If the network can easily recover the bias, the extracted features may contain the bias. Therefore, the whole framework is trained to extract features that are difficult for the bias prediction network to recover. We evaluate our method by integrating it with several existing few-shot learning models across multiple benchmark datasets. The results show that the proposed network can improve the performance in different scenarios. The proposed approach effectively reduces the negative effect of the dataset bias, resulting in the performance improvements in few-shot image classification. The proposed bias prediction model is easily compatible with other few-shot learning models, and applicable to various real-world applications where biased samples are prevalent, such as VR/AR systems and computer vision applications. Full article
(This article belongs to the Special Issue Advanced AR/VR Technologies and Machine Learning Applications in Smart Environments)
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