Convolutional Neural Network-Powered AI Workflow Automation for Sign Language Interpretation in Vehicular Edge-Cloud Systems with Microservices

Article Sidebar

PDF
Published: 2023-07-05
DOI: https://doi.org/10.15662/IJRPETM.2023.0604001
Keywords:
Convolutional Neural Networks (CNNs), sign language interpretation, AI workflow automation, vehicular edge-cloud systems, microservices, containerization, real-time communication, intelligent transportation, accessibility in mobility, human-computer interaction

Main Article Content

Vinita Harish Kulkarni, Vikas Rajeshwar Singh
Department of Information Technology, Sir Visvesvaraya Institute of Technology, Nashik, India

Abstract

The integration of inclusive communication technologies into vehicular systems is critical for enhancing accessibility and safety in next-generation transportation. This paper presents a convolutional neural network (CNN)-powered AI workflow automation framework for real-time sign language interpretation within vehicular edge-cloud ecosystems. The proposed architecture leverages CNNs to recognize and interpret hand gestures and sign language patterns, enabling seamless communication between drivers, passengers, and vehicular systems. Microservices-based design and containerization ensure modularity, scalability, and efficient deployment across heterogeneous edge and cloud environments. AI-driven workflow automation dynamically optimizes data flows, minimizing latency and ensuring reliable interpretation even under high vehicular mobility and variable network conditions. Experimental results demonstrate that the framework achieves high recognition accuracy, low latency, and robust performance, contributing to accessible, intelligent, and adaptive vehicular communication infrastructures. This approach highlights the potential of combining CNNs, microservices, and edge-cloud architectures to support inclusivity in smart mobility ecosystems.

Article Details

Issue

Vol. 6 No. 4 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Convolutional Neural Network-Powered AI Workflow Automation for Sign Language Interpretation in Vehicular Edge-Cloud Systems with Microservices. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 8982-8985. https://doi.org/10.15662/IJRPETM.2023.0604001

References

1. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3(5), 637-646.

2. Sangannagari, S. R. (2023). Smart Roofing Decisions: An AI-Based Recommender System Integrated into RoofNav. International Journal of Humanities and Information Technology, 5(02), 8-16.

3. Sugumar, Rajendran (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification (14th edition). Int. J. Business Intelligence and Data Mining 14 (3):322-358.

4. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2020). Explain ability and interpretability in machine learning models. Journal of Computer Science Applications and Information Technology, 5(1), 1-7.

5. Zhang, K., Mao, Y., Leng, S., Maharjan, S., & Zhang, Y. (2017). Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things. IEEE Communications Magazine, 56(4), 39-45.

6. Alam, T., Islam, M. R., & Babar, M. A. (2019). A Survey on Orchestration Techniques in Edge Computing. Journal of Network and Computer Applications, 135, 84-103.

7. Chen, X., Jiao, L., Li, W., & Fu, X. (2019). Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing. IEEE/ACM Transactions on Networking, 24(5), 2795-2808.

8. Li, Y., Wang, F., & Xie, S. (2020). Machine Learning-Based Resource Allocation in Edge-Cloud Computing: A Survey. IEEE Access, 8, 29501-29520.

9. Poovaiah, S. A. D. (2022). Benchmarking provable resilience in convolutional neural networks: A study with Beta-CROWN and ERAN.

10. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.

11. T. Yuan, S. Sah, T. Ananthanarayana, C. Zhang, A. Bhat, S. Gandhi, and R. Ptucha. 2019. Large scale sign language interpretation. In Proceedings of the 14th IEEE International Conference on Automatic Face Gesture Recognition (FG’19). 1–5.

12. Cherukuri, Bangar Raju. "Microservices and containerization: Accelerating web development cycles." (2020).

13. Luo, C., Zhou, J., Liu, Y., & Peng, J. (2021). AI-Driven Edge-Cloud Collaboration for Smart Traffic Signal Control. IEEE Transactions on Intelligent Transportation Systems.

14. Chen, L., Li, F., & Zhao, L. (2020). Real-Time Sensor Fusion for Autonomous Driving: A Deep Learning Approach. IEEE Transactions on Vehicular Technology, 69(5), 5487-5499.

15. Kairouz, P., McMahan, H. B., et al. (2019). Advances and Open Problems in Federated Learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210.

16. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data. Indian Journal of Science and Technology 9 (48):1-5.

17. Deng, R., Yu, F. R., Zhang, Y., & Ning, Z. (2021). Privacy-Preserving Data Analytics for Smart Cities: A Review. IEEE Communications Surveys & Tutorials, 23(2), 1322-1353.