A Novel User Experience Cloud Computing Model for Examining Brand Image Through Virtual Reality

Main Article Content

Yan Nie
Fengguo Liu

Abstract

This research paper presents a novel Cloud Computing User Experience (CCUE) approach to reconstructing the brand image of traditional Shanghai cosmetic brands by leveraging virtual reality (VR) technology and user experience (UX) research. Traditional Shanghai cosmetic brands possess rich cultural heritage and unique product offerings, but often face challenges in maintaining relevance in the modern market. The proposed CCUE uses the VR technology to create immersive and interactive experiences that allow consumers to explore and engage with the brand in a virtual environment. The developed CCUE model integrates the Artificial Intelligence (AI) integrated Imperialist Competitive Algorithm (ICA) for the user-machine interaction. With the CCUE a combination of VR simulations, product showcases, and interactive storytelling, users can experience the essence and history of traditional Shanghai cosmetic brands, fostering a deep connection and emotional attachment. Additionally, UX research techniques are employed to gather user feedback and insights, enabling the refinement and optimization of the VR experience. The findings of this CCUE contribute to the field of brand reconstruction and provide practical insights for traditional brands seeking to revitalize their image in a rapidly evolving market.

Article Details

How to Cite
Nie, Y. ., & Liu, F. . (2023). A Novel User Experience Cloud Computing Model for Examining Brand Image Through Virtual Reality. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 305–318. https://doi.org/10.17762/ijritcc.v11i6.7721
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Articles

References

Khadim, Q., Hannola, L., Donoghue, I., Mikkola, A., Kaikko, E. P., & Hukkataival, T. (2021). 12 Integrating the user experience throughout the product lifecycle with real-time simulation-based digital twins. Real-time Simulation for Sustainable Production: Enhancing User Experience and Creating Business Value.

Poojara, S. R., Dehury, C. K., Jakovits, P., & Srirama, S. N. (2022). Serverless data pipeline approaches for IoT data in fog and cloud computing. Future Generation Computer Systems, 130, 91-105.

Alazab, M., Manogaran, G., & Montenegro-Marin, C. E. (2022). Trust management for internet of things using cloud computing and security in smart cities. Cluster Computing, 1-13.

Waghmode , S. T. ., & Patil , B. M. . (2023). Adaptive Load Balancing in Cloud Computing Environment. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 209–217. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2495

Al Najjar, M. T., Al Shobaki, M. J., & El Talla, S. A. (2022). Supporting Senior Management and the Readiness of the Organizational Structure in Palestinian Charitable Institutions to Adopt and Implement Cloud Computing. International Journal of Academic Information Systems Research (IJAISR), 6(3), 1-17.

Rongstad, K., & Zhang, R. (2021). Enterprise network security from cloud computing perspective. Issues in Information Systems, 22(3).

Pande, S. D. ., & Ahammad, D. S. H. . (2021). Improved Clustering-Based Energy Optimization with Routing Protocol in Wireless Sensor Networks. Research Journal of Computer Systems and Engineering, 2(1), 33:39. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/17

Cai, C., & Chen, C. (2021). Optimization of human resource file information decision support system based on cloud computing. Complexity, 2021, 1-12.

Ortiz, G., Zouai, M., Kazar, O., Garcia-de-Prado, A., & Boubeta-Puig, J. (2022). Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing. Computer Standards & Interfaces, 79, 103550.

Ortiz, G., Zouai, M., Kazar, O., Garcia-de-Prado, A., & Boubeta-Puig, J. (2022). Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing. Computer Standards & Interfaces, 79, 103550.

Le-Anh, T., Ngo-Van, Q., Vo-Huy, P., Huynh-Van, D., & Le-Trung, Q. (2021, May). A container-based edge computing system for smart healthcare applications. In Industrial Networks and Intelligent Systems: 7th EAI International Conference, INISCOM 2021, Hanoi, Vietnam, April 22-23, 2021, Proceedings (pp. 324-336). Cham: Springer International Publishing.

Taylor, D., Roberts, R., Rodriguez, A., González, M., & Pérez, L. Efficient Course Scheduling in Engineering Education using Machine Learning. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/121

Zhang, W. (2022). English Online Teaching Resource Processing Based on Intelligent Cloud Computing Technology. Mobile Information Systems, 2022.

Saeik, F., Avgeris, M., Spatharakis, D., Santi, N., Dechouniotis, D., Violos, J., ... & Papavassiliou, S. (2021). Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions. Computer Networks, 195, 108177.

Shams, A., Sharif, H., & Helfert, M. (2021). A novel model for cloud computing analytics and measurement. Journal of Advances in Information Technology, 12(2), 93-106.

Wu, Z., Cai, Z., Tang, X., Xu, Y., & Deng, T. (2022). A forward and backward private oblivious RAM for storage outsourcing on edge-cloud computing. Journal of Parallel and Distributed Computing, 166, 1-14.

Zhang, S., Pandey, A., Luo, X., Powell, M., Banerji, R., Fan, L., ... & Luzcando, E. (2022). Practical Adoption of Cloud Computing in Power Systems—Drivers, Challenges, Guidance, and Real-World Use Cases. IEEE Transactions on Smart Grid, 13(3), 2390-2411.

Mr. Bhushan Bandre, Ms. Rashmi Khalatkar. (2015). Impact of Data Mining Technique in Education Institutions. International Journal of New Practices in Management and Engineering, 4(02), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/35

Karar, M. E., Alsunaydi, F., Albusaymi, S., & Alotaibi, S. (2021). A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alexandria Engineering Journal, 60(5), 4423-4432.

Alkalbani, A. M., & Hussain, W. (2021). Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services. International Journal of Communication Systems, 34(8), e4780.

Anuradha, M., Jayasankar, T., Prakash, N. B., Sikkandar, M. Y., Hemalakshmi, G. R., Bharatiraja, C., & Britto, A. S. F. (2021). IoT enabled cancer prediction system to enhance the authentication and security using cloud computing. Microprocessors and Microsystems, 80, 103301.

Sharma, S., Gupta, S., Gupta, D., Juneja, S., Singal, G., Dhiman, G., & Kautish, S. (2022). Recognition of gurmukhi handwritten city names using deep learning and cloud computing. Scientific Programming, 2022, 1-16.

Dang, H. V., Tatipamula, M., & Nguyen, H. X. (2021). Cloud-based digital twinning for structural health monitoring using deep learning. IEEE Transactions on Industrial Informatics, 18(6), 3820-3830.

Mishra, S., & Tyagi, A. K. (2022). The role of machine learning techniques in internet of things-based cloud applications. Artificial intelligence-based internet of things systems, 105-135.

Zhang, Q., Bai, C., Chen, Z., Li, P., Yu, H., Wang, S., & Gao, H. (2021). Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing. Concurrency and Computation: Practice and Experience, 33(7), 1-1.

Zhang, Q., Bai, C., Chen, Z., Li, P., Yu, H., Wang, S., & Gao, H. (2021). Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing. Concurrency and Computation: Practice and Experience, 33(7), 1-1.

Qu, G., Wu, H., Li, R., & Jiao, P. (2021). DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing. IEEE Transactions on Network and Service Management, 18(3), 3448-3459.

Rjoub, G., Bentahar, J., Abdel Wahab, O., & Saleh Bataineh, A. (2021). Deep and reinforcement learning for automated task scheduling in large?scale cloud computing systems. Concurrency and Computation: Practice and Experience, 33(23), e5919.