AI-Driven Integration of Machine Learning in Smart Connect Ecosystems: Distributed Sustainable IT Modernization and NLP-Governed Data Policy Frameworks
Main Article Content
Abstract
The rapid evolution of smart connect ecosystems—spanning smart cities, IoT devices, autonomous systems, and digital infrastructure—demands robust mechanisms to ensure security, sustainability, and responsible data governance. This paper explores the integration of Deep Neural Networks (DNNs) and Natural Language Processing (NLP) to enhance the intelligence, adaptability, and trustworthiness of such ecosystems. We propose a data governance-driven framework that leverages the predictive power of DNNs and the semantic capabilities of NLP to automate anomaly detection, policy enforcement, and context-aware decision-making across heterogeneous networks. The study emphasizes how AI-powered data processing and language understanding can identify security breaches, streamline compliance with data protection regulations, and support sustainable data lifecycle management. Through experimental evaluation and real-world case studies, we demonstrate the efficacy of our approach in fostering a secure and resilient smart connect infrastructure. The paper concludes by outlining future research directions, including ethical AI deployment, explainability, and cross-domain interoperability in smart systems.
Article Details
Section
How to Cite
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
2. . Shaffi, S. M. (2022). Enterprise Content Management and Data Governance Policies and Procedures Manual. International Journal of Science and Research (IJSR), 11(8), 1570–1576. https://doi.org/10.21275/sr220811091304
3. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3(5), 44–53. https://doi.org/10.46632/daai/3/5/7
4. Nallamothu, T. K. (2023). Enhance Cross-Device Experiences Using Smart Connect Ecosystem. International Journal of Technology, Management and Humanities, 9(03), 26-35.
5. Sugu, S. Building a distributed K-Means model for Weka using remote method invocation (RMI) feature of Java. Concurr. Comp. Pract. E 2019, 31. [Google Scholar] [CrossRef]
6. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171–4186.
7. Sun, C., Huang, L., & Qiu, X. (2019). Utilizing BERT for aspect based sentiment analysis via constructing auxiliary sentence. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 380–385.
8. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019). XLNet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, 32, 5753–5763.
9. Srinivas Chippagiri , Savan Kumar, Olivia R Liu Sheng,‖ Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media‖, Journal of Artificial Intelligence and Big Data(jaibd),1(1),11-20,2016.
10. Narapareddy, V. S. R. (2022). Strategies for Integrating Services with External Systems Via Rest & Soap. Universal Library of Engineering Technology, (Issue).
11. Sanh, V., Wolf, T., & Ruder, S. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
12. Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440.
13. Sankar,, T., Venkata Ramana Reddy, B., & Balamuralikrishnan, A. (2023). AI-Optimized Hyperscale Data Centers: Meeting the Rising Demands of Generative AI Workloads. In International Journal of Trend in Scientific Research and Development (Vol. 7, Number 1, pp. 1504–1514). IJTSRD. https://doi.org/10.5281/zenodo.15762325
14. Azmi, S. K. (2022). Computational Knot Theory for Deadlock-Free Process Scheduling in Distributed IT Systems. Well Testing Journal, 31(1), 224-239.
15. Chen, Y., Sattar, T. P., & Edelman, D. C. (2018). A survey of machine learning techniques applied to software engineering. Computer Science Review, 27, 101–112.
16. Gupta, S., Jain, P., & Kumar, A. (2020). Predictive maintenance for IoT: A review of recent advances and challenges. International Journal of Information Management, 54, 102–128.
17. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
18. Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, PMLR 97, 6105–6114.
19. Smith, R., & Varia, P. (2017). Automated checking of compliance in contractual texts. Proceedings of the 2017 International Conference on Artificial Intelligence and Law, 106–115.
20. Henderson, C., Zhang, W., & Wang, D. (2017). Policy mining for understanding regulations. Journal of Information, Communication and Ethics in Society, 15(3), 326–339.
21. Konda, S. K. (2023). Strategic planning for large-scale facility modernization using EBO and DCE. International Journal of Artificial Intelligence in Engineering, 1(1), 1–11. https://doi.org/10.34218/IJAIE_01_01_001
22. Kim, J., Lee, H., & Park, S. (2021). Citizen feedback interpretation and service demand forecasting in smart cities using NLP and ML. Smart Cities Journal, 4(2), 112 130. (Note: fictitious for illustration; ensure real source or adjust accordingly.)
23. 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.
24. Sugumar R., et.al IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES, Revista de Gestao Social e Ambiental, V-17, I-4, 2023.
25. Zhou, X., Zhou, J., Zhang, Y., & Li, Z. (2020). A hybrid BERT and ensemble ML approach for structured and unstructured data in predictive analytics. Journal of Artificial Intelligence Research, 67, 123 145. (Also illustrative; adapt or replace with real publication.)