Context-Aware Augmented Reality Using Human–Computer Interaction Models

References

• Al-Turjman, F. (2016, October). Impact of user's habits on smartphones’ sensors: An overview. In 2016 HONET-ICT (pp. 70–74). IEEE.
   Google Scholar
• Balaanand, M., Karthikeyan, N., Karthik, S., Varatharajan, R., Manogaran, G., & Sivaparthipan, C. B. (2019). An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter. The Journal of Supercomputing, 75(9), 6085–6105. https://doi.org/10.1007/s11227-019-02948-w
   Web of Science ®Google Scholar
• BalaAnand, M., Sankari, S., Sowmipriya, R., & Sivaranjani, S. (2015). Identifying fake user’s in social networks using non verbalbehavior. International Journal of Technology and Engineering System (IJTES), 7(2), 157–161.
   Google Scholar
• Carneiro, J., Laranjeira, J., Marreiros, G., Freitas, C. F., & Santos, R. (2012). A context-aware model to support ubiquitous group decision making. Journal of Internet Services and Information Security, 2(1/2), 105–118.
   Google Scholar
• Chen, S., Zhao, X., Luo, B., & Sun, Z. (2020). Visual browse and exploration in motion capture data with phylogenetic tree of context-aware poses. Sensors, 20(18), 5224. https://doi.org/10.3390/s20185224
   PubMed Web of Science ®Google Scholar
• Elhoseny, M., Shehab, A., & Osman, L. (2018, February). An empirical analysis of user behavior for P2P IPTV workloads. In International Conference on advanced machine learning technologies and applications (pp. 252–263). Springer.
   Google Scholar
• Freeman, J. (2020). Content enhancement with augmented reality and machine learning. Journal of Southern Hemisphere Earth Systems Science, 70(1), 143–150. https://doi.org/10.1071/ES19046
   Web of Science ®Google Scholar
• Gao, J., Wang, H., & Shen, H. (2020). August). machine learning based workload prediction in cloud computing. In 2020 29th international conference on computer communications and networks (ICCCN) (pp. 1–9). IEEE.
   Google Scholar
• Gattullo, M., Evangelista, A., Manghisi, V. M., Uva, A. E., Fiorentino, M., Boccaccio, A., … Gabbard, J. L. (2020a). Towards next generation technical documentation in augmented reality using a context-aware information manager. Applied Sciences, 10(3), 780. https://doi.org/10.3390/app10030780
   Google Scholar
• Geng, J., Song, X., Pan, Y., Tang, J., Liu, Y., Zhao, D., & Ma, Y. (2020). A systematic design method of adaptive augmented reality work instruction for complex industrial operations. Computers in Industry, 119, 103229. https://doi.org/10.1016/j.compind.2020.103229
   Web of Science ®Google Scholar
• Gheisari, M., Najafabadi, H. E., Alzubi, J. A., Gao, J., Wang, G., Abbasi, A. A., & Castiglione, A. (2021). Obpp: An ontology-based framework for privacy-preserving in IoT-based smart city. Future Generation Computer Systems, 123, 1–13. https://doi.org/10.1016/j.future.2021.01.028
   Web of Science ®Google Scholar
• Hameed, K. W., Noras, J. M., Radwan, A., Al-Turjman, F., Rodriguez, J., & Abd-Alhameed, R. A. (2018). June). Optimal array size for multiuser MIMO. In 2018 14th International Wireless communications & mobile computing Conference (IWCMC) (pp. 1296–1300). IEEE.
   Google Scholar
• Huet, A., Pinquié, R., Véron, P., Mallet, A., & Segonds, F. (2021). Canada: A knowledge graph for a context-aware cognitive design assistant. Computers in Industry, 125, 103377. https://doi.org/10.1016/j.compind.2020.103377
   Web of Science ®Google Scholar
• Jiang, S., Saravanan, V., Yan, P., & Sridhar, R. (2016, September). A fully parallel content addressable memory design using multi-bank structure. In 2016 29th IEEE International system-on-chip Conference (SOCC) (pp. 338–343). IEEE.
   Google Scholar
• Kim, J. C., Laine, T. H., & Åhlund, C. (2021). Multimodal Interaction Systems based on internet of things and augmented reality: A systematic literature review. Applied Sciences, 11(4), 1738. https://doi.org/10.3390/app11041738
   Google Scholar
• Lampropoulos, G., Keramopoulos, E., & Diamantaras, K. (2020). Enhancing the functionality of augmented reality using deep learning, semantic web and knowledge graphs: A review. Visual Informatics, 4(1), 32–42. https://doi.org/10.1016/j.visinf.2020.01.001
   Google Scholar
• Manogaran, G., Mumtaz, S., Mavromoustakis, C., Pallis, E., & Mastorakis, G. (2021). Artificial intelligence and blockchainassisted offloading approach for data availability maximization in edge nodes. IEEE Transactions on Vehicular Technology, 70(3).
   Web of Science ®Google Scholar
• Manogaran, G., Srivastava, G., Muthu, B. A., Baskar, S., Shakeel, P. M., Hsu, C. H., … Kumar, P. M. (2020). A response-aware traffic offloading scheme using regression machine learning for user-centric large-scale internet of things. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2020.3022322.
   Web of Science ®Google Scholar
• Mohanty, S. N., Rejina Parvin, J., Vinoth Kumar, K., Ramya, K. C., Sheeba Rani, S., & Lakshmanaprabu, S. K. (2019). Optimal rough fuzzy clustering for user profile ontology based web page recommendation analysis. Journal of Intelligent & Fuzzy Systems, 37(1), 205–216. https://doi.org/10.3233/JIFS-179078
   Web of Science ®Google Scholar
• Nguyen, C. H., Pham, T. L., Nguyen, T. N., Ho, C. H., & Nguyen, T. A. (2021). The linguistic summarization and the interpretability, scalability of fuzzy representations of multilevel semantic structures of word-domains. Microprocessors and Microsystems, 81, 103641. https://doi.org/10.1016/j.micpro.2020.103641
   Web of Science ®Google Scholar
• Nguyen, N. T., & Liu, B. H. (2018). The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are NP-hard. IEEE Systems Journal, 13(2), 1312–1315. https://doi.org/10.1109/JSYST.2018.2828879
   Web of Science ®Google Scholar
• Pascoal, R., Almeida, A. D., & Sofia, R. C. (2020). Mobile Pervasive Augmented Reality Systems—MPARS: The Role of User Preferences in the Perceived Quality of Experience in Outdoor Applications. ACM Transactions on Internet Technology, 20(1), 1–17. https://doi.org/10.1145/3375458
   Web of Science ®Google Scholar
• Quandt, M., Beinke, T., & Freitag, M. (2020). User-centered evaluation of an augmented reality-based assistance system for maintenance. Procedia CIRP, 93, 921–926. https://doi.org/10.1016/j.procir.2020.03.053
   Google Scholar
• Sutrala, A. K., Obaidat, M. S., Saha, S., Das, A. K., Alazab, M., & Park, Y. (2021). Authenticated Key agreement scheme With user anonymity and untraceability for 5G-enabled softwarized industrial cyber-physical systems. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3056704
   Web of Science ®Google Scholar
• Vallejo-Correa, P., Monsalve-Pulido, J., & Tabares-Betancur, M. (2021). A systematic mapping review of context-aware analysis and its approach to mobile learning and ubiquitous learning processes. Computer Science Review, 39, 100335. https://doi.org/10.1016/j.cosrev.2020.100335
   Web of Science ®Google Scholar
• Xu, J., & Feng, D. (2017). Identification of ICS Security risks toward the analysis of packet interaction characteristics using state sequence matching based on SF-FSM. Security and Communication Networks, 2017. https://doi.org/10.1155/2017/2430835
   Google Scholar