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Electronics
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Human-Computer Interaction in Intelligent Systems, 2nd Edition
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Human-Computer Interaction in Intelligent Systems, 2nd Edition

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4354

Special Issue Editors

Dr. Mohammad Jafari

E-Mail Website
Guest Editor
Robotics Engineering Program, Columbus State University, Columbus, GA 31907, USA
Interests: real-time learning-based control; machine learning; multi-agent systems; control & systems theory; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Rania Hodhod

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: intelligent systems; computationnel intelligence; machine learning; serious games; computer science education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human–robot/computer interactions have brought enormous benefits to society and made people and communities more connected. There has been a great deal of interest in the recent past over the utilization of various techniques in human–robot/computer interactions to enhance their applications in intelligent systems. The design of these systems involves advanced techniques including optimization, machine learning, virtual reality (VR), augmented reality (AR), mixed reality (MR), interactive games and simulations, serious games and simulations, and emotion and mood analysis.

This Special Issue of Electronics is devoted to studying and analyzing human–computer interaction (HCI) in intelligent systems (IS). We welcome authors to submit their research on topics including, but not limited to:

  • Human–robot interaction;
  • HCI and BCI in a brain-controlled UAV;
  • Interactive games and simulations in HCI;
  • Serious games and simulations in HCI;
  • EEG in HCI;
  • Deep learning in HCI/IS;
  • Reinforcement learning in HCI/IS;
  • Emotion and mood analysis;
  • HCI and human–computer collaboration;
  • Privacy issues and HCI;
  • Virtual reality (VR), augmented reality (AR), and mixed reality (MR) in HCI;
  • Multimodal interaction in HCI;
  • Embodied and wearable computing.

Dr. Mohammad Jafari
Dr. Rania Hodhod
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

  • human-robot interaction
  • HCI
  • BCI

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Related Special Issue

Published Papers (3 papers)

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Research

24 pages, 1715 KiB  
Article
Multimodal Guidance for Enhancing Cyclist Road Awareness
by Gang Ren, Zhihuang Huang, Wenshuo Lin, Ning Miao, Tianyang Huang, Gang Wang and Jee-Hang Lee
Electronics 2025, 14(7), 1363; https://doi.org/10.3390/electronics14071363 - 28 Mar 2025
Viewed by 540
Abstract
Road safety for vulnerable road users, particularly cyclists, remains a critical global issue. This study explores the potential of multimodal visual and haptic interaction technologies to improve cyclists’ perception of and responsiveness to their surroundings. Through a systematic evaluation of various visual displays [...] Read more.
Road safety for vulnerable road users, particularly cyclists, remains a critical global issue. This study explores the potential of multimodal visual and haptic interaction technologies to improve cyclists’ perception of and responsiveness to their surroundings. Through a systematic evaluation of various visual displays and Haptic Feedback mechanisms, this research aims to identify effective strategies for recognizing and localizing potential traffic hazards. Study 1 examines the design and effectiveness of Visual Feedback, focusing on factors such as feedback type, traffic scenarios, and target locations. Study 2 investigates the integration of Haptic Feedback through wearable vests to enhance cyclists’ awareness of peripheral vehicular activities. By conducting experiments in realistic traffic conditions, this research seeks to develop safety systems that are intuitive, cognitively efficient, and tailored to the needs of diverse user groups. This work advances multimodal interaction design for road safety and aims to contribute to a global reduction in traffic incidents involving vulnerable road users. The findings offer empirical insights for designing effective assistance systems for cyclists and other non-motorized vehicle users, thereby ensuring their safety within complex traffic environments. Full article
(This article belongs to the Special Issue Human-Computer Interaction in Intelligent Systems, 2nd Edition)
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38 pages, 4101 KiB  
Article
AI-Powered VR for Enhanced Learning Compared to Traditional Methods
by Omer Emin Cinar, Karen Rafferty, David Cutting and Hui Wang
Electronics 2024, 13(23), 4787; https://doi.org/10.3390/electronics13234787 - 4 Dec 2024
Cited by 2 | Viewed by 2479
Abstract
This paper evaluates a VR (Virtual Reality) application aimed at enhancing the learning of Python collection data types and structures for electrical and electronic engineering students. By incorporating gamification and personalisation features, the application provides an immersive environment where students can interact with [...] Read more.
This paper evaluates a VR (Virtual Reality) application aimed at enhancing the learning of Python collection data types and structures for electrical and electronic engineering students. By incorporating gamification and personalisation features, the application provides an immersive environment where students can interact with virtual representations of complex programming concepts. To further enhance interactivity and engagement, the application integrates a virtual assistant and example generator, developed using Meta Voice SDK (Software Development Kit) and wit.ai. These AI (Artificial Intelligence)-NLP (Natural Language Processing) tools create personalised learning paths and generate dynamic examples based on individual learning progress. A user study was conducted with a total of 48 participants. During the user study, participants were divided into two equal groups of 24, both wearing EEG (Electroencephalography) headsets: one group engaged with the VR application, while the other read the traditional booklet, allowing for the recording and analysis of attention and engagement levels. These measures of engagement and attention were then compared to those extracted from a benchmark cohort of students whose learning experience was through more traditional booklets. The results indicated a statistically significant improvement in understanding Python collections among VR users compared to their baseline scores, highlighting the benefits of interactive and tailored learning environments. Additionally, EEG data analysis showed that VR users exhibited higher average levels of attention and engagement compared to those using the paper-based method, demonstrating the effectiveness of immersive technologies in sustaining learner interest and focus, particularly in enhancing learning for software development. Full article
(This article belongs to the Special Issue Human-Computer Interaction in Intelligent Systems, 2nd Edition)
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16 pages, 625 KiB  
Article
InsightForge: Deriving Entrepreneurial Insights from Open-Ended and Unstructured Survey Data Using NLP Techniques
by Md. Nurullah, Rania Hodhod, Helle Friis, Walker Smith and Kirk Heriot
Electronics 2024, 13(23), 4725; https://doi.org/10.3390/electronics13234725 - 29 Nov 2024
Viewed by 1234
Abstract
Entrepreneurship has long been recognized as a key driver of economic development, traditionally centered on business creation and the strategic actions of individuals aiming to realize their entrepreneurial visions. Central to this process has been the business plan, often viewed as a critical [...] Read more.
Entrepreneurship has long been recognized as a key driver of economic development, traditionally centered on business creation and the strategic actions of individuals aiming to realize their entrepreneurial visions. Central to this process has been the business plan, often viewed as a critical blueprint that outlines the vision, strategies, and operations of new ventures. However, recent studies and observations have raised concerns regarding the continued relevance of business plans, suggesting a shift in entrepreneurial behavior. In response, this study adopts a novel approach by using artificial intelligence (AI) to explore entrepreneurial practices more rigorously. Written feedback from 150 entrepreneurs was analyzed using natural language processing (NLP) techniques, allowing us to move beyond subjective assessments and anecdotal evidence. Our study utilized a structured twenty-question survey to capture the experiences and insights of entrepreneurs. By applying AI, we were able to process large amounts of textual data, uncover nuanced patterns, and identify correlations that might otherwise go unnoticed. The proposed approach enabled a comprehensive analysis of entrepreneurial decision making and provided a robust framework for future research. The findings from this work offer valuable insights into the evolving landscape of entrepreneurship, with significant implications for education, policy, and practice in today’s dynamic business environment. Full article
(This article belongs to the Special Issue Human-Computer Interaction in Intelligent Systems, 2nd Edition)
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