Algorithm of Mental Model of Art Design Based on Virtual Reality Technology
Main Article Content
Abstract
The mental model of art design in the realm of virtual reality (VR) technology is a dynamic fusion of creativity, immersion, and technological innovation. In this paradigm, artists leverage the immersive capabilities of VR to transcend traditional boundaries, creating a multisensory experience that goes beyond the confines of a two-dimensional canvas. The artist's mental canvas extends into a three-dimensional space where users can engage with art on a profound level, exploring and interacting with the artwork in ways previously unimaginable. The core objective is to redefine art design design through the lens of machine vision cognition into the ethical and social implications inherent in Art Design. With the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO) to amplify the capabilities of VR systems. At the forefront of this study is the reimagination of art design design, characterized by aesthetics, ergonomics, and functionality. The infusion of machine vision cognition into these designs not only enhances user experience but also prompts contemplation of ethical considerations surrounding privacy, accessibility, and informed consent. Ethical and social implications are scrutinized comprehensively, acknowledging the profound impact of VRs on individual rights, security, and privacy. The research probes into equitable access to VR technologies, ethical data utilization in art design, identity verification, and surveillance contexts. Central to this multidisciplinary inquiry is the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO). With swarm intelligence and probabilistic modelling, HMPSO enhances the efficiency, accuracy, and reliability of Mental Model VR systems. It addresses the critical challenge of reducing false positives and false negatives in VR authentication. The research methodology comprises performance evaluations, ethical analyses, and socio-cultural investigations, offering a comprehensive view of the interplay between design innovation, machine vision cognition, ethical awareness, and the application of HMPSO in the Art Design.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
J.T. Andrews et al., “Ethical considerations for collecting human-centric image datasets. arXiv preprint arXiv:2302.03629.
H.Y.Zhu, N.Q.Hieu, D.T.Hoang, D.N.Nguyen, C.Tin, “A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Surveym” arXiv preprint arXiv:2309.01848, 2023.
U.Asad, M.Khan, A.Khalid, W.A.Lughmani, “Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies,” Sensors, vol.23, no.8, pp.3938, 2023.
A.Paullada, I.D.Raji, E.M. Bender, E.Denton, A.Hanna, “Data and its (dis) contents: A survey of dataset development and use in machine learning research,” Patterns, vol.2, no.11, 2021.
W.Yang et al., “Survey on Explainable AI: From Approaches, Limitations and Applications Aspects,” Human-Centric Intelligent Systems, pp.1-28, 2023.
J.M.Górriz et al., “Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends,” Information Fusion, vol.100, pp.101945, 2023.
P. K.Mallick, V. E.Balas, A. K.Bhoi, A. F. Zobaa, Cognitive informatics and soft computing. Springer Singapore, 2021.
S.Nasir, R.A.Khan, S.Bai, “Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond,” arXiv preprint arXiv:2309.00064, 2023.
P.P.Liang et al., “Multibench: Multiscale benchmarks for multimodal representation learning,” arXiv preprint arXiv:2107.07502, 2021.
X.Gu et al., “Beyond supervised learning for pervasive healthcare,” IEEE Reviews in Biomedical Engineering, 2023.
E.Brophy, Deep learning-based signal processing approaches for improved tracking of human health and behaviour with wearable sensors (Doctoral dissertation, Dublin City University), 2022.
T.Hulsen, “Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare,” AI, vol.4, no.3,pp. 652-666, 2023.
C.Selvi et al., “A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems,” arXiv preprint arXiv:2307.06659, 2023.
G.Mirzaei, H.Adeli, “Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia,” Biomedical Signal Processing and Control, vol.72, pp.103293, 2022.
A.Kline et al., “Multimodal machine learning in precision health: A scoping review,” npj Digital Medicine, vol.5, no.1, pp.171, 2022.
F.Dell’Agnola et al., “Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones,” IEEE Journal of Biomedical and Health Informatics, vol.26, no.9, pp.4751-4762, 2022.
A.Rahman et al., “Multimodal EEG and keystroke dynamics based biometric system using machine learning algorithms,” IEEE Access, vol.9, pp.94625-94643, 2021.
M.Junaid, S.Ali, F.Eid, S.El-Sappagh, T.Abuhmed, “Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease,” Computer Methods and Programs in Biomedicine, vol.234, pp.107495, 2023.
H.Sharma, L.Drukker, A.T.Papageorghiou, J.A. Noble, “Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging,” Computers in biology and medicine, vol.135, pp.104589, 2021.
J.Z.Lim, J.Mountstephens, J.Teo, “Eye-tracking feature extraction for biometric machine learning,” Frontiers in neurorobotics, vol.15, pp.796895, 2022.
K.Bayoudh, R.Knani, F.Hamdaoui, A.Mtibaa, “A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets,” The Visual Computer, pp.1-32, 2021.
M.Gnacek et al., “Emteqpro—fully integrated biometric sensing array for non-invasive biomedical research in virtual reality,” Frontiers in virtual reality, vol.3, pp.781218, 2022.
S.Kinreich et al., “Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach,” Translational psychiatry, vol.11, no.1, pp.166, 2021.
H.Chen, “Alzheimer’s disease Neuroimaging Initiative, Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study,” European Radiology, vol.32, pp.448-459, 2022.
S.Sharma, P. K. Mandal, “A comprehensive report on machine learning-based early detection of alzheimer's disease using multi-modal neuroimaging data,” ACM Computing Surveys (CSUR), vol.55, no.2, pp.1-44, 2022.
G.A. Aguayo et al., “Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study,” BMC medical research methodology, vol.23, no.1, pp.1-13, 2023.
S.Qiu et al., “Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges,” Information Fusion, vol.80, pp.241-265, 2022.
T.B.Shams et al., “EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey,” ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol.20, no.2, pp.225-241, 2022.
N.Kaushal, S.Singh, J.Kumar, “Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System,” Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm, pp.157-165, 2021.