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Advances in Artificial Intelligence for Computer Vision, Augmented Reality Virtual...
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Advances in Artificial Intelligence for Computer Vision, Augmented Reality Virtual Reality and Metaverse

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 18 March 2026 | Viewed by 2162

Special Issue Editors

Dr. A F M Saifuddin Saif

E-Mail Website
Guest Editor
Department of Computing, Information and Mathematical Sciences, and Technology (CIMST), Chicago State University, Chicago, IL, USA
Interests: computer vision; machine learning; image processing; augmented reality; virtual reality
Prof. Dr. Antonio Celesti

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 98166 Messina, Italy
Interests: distributed systems; cloud computing; edge computing; Internet of Things (IoT); machine learning; assistive technology; eHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Yu-Sheng Lin

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
Interests: medical device; mechatronics; computed-aided engineering; additive manufacturing; AIoT in healthcare
Dr. Edelberto Franco Silva

E-Mail Website
Guest Editor
Department of Computer Science, Federal University of Juiz de Fora (UFJF), Juiz de Fora 36036-330, MG, Brazil
Interests: computer networks and cybersecurity; computer science; machine learning; network communication; networking; wireless computing; network security

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI)’s role in computer vision (CV), augmented reality (AR), virtual reality (VR) and the metaverse is transformative and can be employed in various sectors, including security, surveillance, education, architecture, and healthcare. An effective integration of AI in CV, AR, VR and the metaverse will seamlessly integrate hardware and software for immersive user experience. While the application of AI in CV, AR, VR and the metaverse holds immense promise, there are still challenges to overcome, such as big data storage and management, cloud/edge continuum, high-performance computing (HPC), security and privacy, human–computer interaction in cyberphysical systems regarding user satisfaction, etc. 

Even with the latest advancements in technology, there is still a long way to go toward the extensive adoption of AI in this context. However, it is also true that digital transformation is accelerating. In fact, AI applications mainly based on machine learning and deep learning are expanding, and immersive technology is progressing rapidly. This Special Issue focuses on how AI is being used to enhance real-time modeling, automate content generation, make object detection and tracking easier, and make it possible to customize virtual worlds in the context of CV, AR, VR and the metaverse. This Special Issue aims to comprehensively review the promises and challenges associated with this emerging cutting-edge research area. Moreover, this Special Issue will explore the fascinating synergy of AI in CV, AR, VR and the metaverse, highlighting the pivotal role of AI plays in shaping the future of technological change. 

The scope of this Special Issue includes, but is not limited to, AI for computer vision, augmented reality, virtual reality and the metaverse focusing on the following areas: video and serious games, 3D simulators, telemedicine, medical image processing, satellite image processing, path planning, localization, detection, recognition, tracking, 3D reconstruction, augmented human intelligence, natural language processing (NLP), sentiment analysis, emotion recognition, security and privacy, surveillance, etc. 

We look forward to receiving your contributions. 

Dr. A F M Saifuddin Saif
Prof. Dr. Antonio Celesti
Dr. Yu-Sheng Lin
Dr. Edelberto Franco Silva
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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
  • machine learning
  • deep learning
  • computer vision
  • augmented reality
  • virtual reality
  • metaverse

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Published Papers (2 papers)

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17 pages, 12830 KB  
Article
Your Eyes Under Pressure: Real-Time Estimation of Cognitive Load with Smooth Pursuit Tracking
by Pierluigi Dell’Acqua, Marco Garofalo, Francesco La Rosa and Massimo Villari
Big Data Cogn. Comput. 2025, 9(11), 288; https://doi.org/10.3390/bdcc9110288 - 13 Nov 2025
Viewed by 237
Abstract
Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and [...] Read more.
Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Computer Vision, Augmented Reality Virtual Reality and Metaverse)
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20 pages, 1798 KB  
Article
An Approach to Enable Human–3D Object Interaction Through Voice Commands in an Immersive Virtual Environment
by Alessio Catalfamo, Antonio Celesti, Maria Fazio, A. F. M. Saifuddin Saif, Yu-Sheng Lin, Edelberto Franco Silva and Massimo Villari
Big Data Cogn. Comput. 2025, 9(7), 188; https://doi.org/10.3390/bdcc9070188 - 17 Jul 2025
Viewed by 1174
Abstract
Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications [...] Read more.
Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications through users’ voice commands presents significant challenges due to the hardware and software limitations of headset devices. This paper aims to bridge this gap by proposing a methodology to address these issues. In particular, starting from a Mel-Frequency Cepstral Coefficient (MFCC) extraction algorithm able to capture the unique characteristics of the user’s voice, we pass it as input to a Convolutional Neural Network (CNN) model. After that, in order to integrate the CNN model with a VR application running on a standalone headset, such as Oculus Quest, we converted it into an Open Neural Network Exchange (ONNX) format, i.e., a Machine Learning (ML) interoperability open standard format. The proposed system demonstrates good performance and represents a foundation for the development of user-centric, effective computing systems, enhancing accessibility to VR environments through voice-based commands. Experiments demonstrate that a native CNN model developed through TensorFlow presents comparable performances with respect to the corresponding CNN model converted into the ONNX format, paving the way towards the development of VR applications running in headsets controlled through the user’s voice. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Computer Vision, Augmented Reality Virtual Reality and Metaverse)
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