ArticlesOpen Access

How Artificial Intelligence Is Transformation Cloud Computing: Unlocking Possibilities for Businesses

DOI: 10.18535/mcsj/v2020.07· Pages: 60-71· (2020)· Published: December 20, 2020
PDF
Views: 9 PDF downloads: 1

Abstract

The convergence of Artificial Intelligence (AI) and Cloud Computing is reshaping the technological landscape, providing businesses with unprecedented opportunities to innovate, optimize operations, and scale their services. AI’s ability to process large datasets, generate predictive analytics, and automate complex tasks, combined with the scalability and accessibility of cloud platforms, offers transformative potential for organizations across industries. This paper explores how AI is redefining the capabilities of cloud computing by enhancing data processing, improving security protocols, and delivering cost-effective solutions. The integration of AI in cloud environments has enabled real-time analytics, intelligent automation, and personalized customer experiences, empowering businesses to make data-driven decisions faster and more efficiently.This study provides an in-depth analysis of the interplay between AI and cloud computing, presenting a comprehensive review of existing literature, methodologies, and real-world applications. By examining industry-specific case studies, we highlight the tangible benefits and strategic advantages for businesses adopting AI-powered cloud solutions. Furthermore, the paper discusses the challenges, including ethical concerns, data privacy issues, and the resource-intensive nature of implementing AI systems. The findings underline the pivotal role of this integration in driving digital transformation and fostering a competitive edge in an increasingly data-centric world.

References

  1. Mahmud, U., Alam, K., Mostakim, M. A., & Khan, M. S. I. (2018). AI-driven micro solar power grid systems for remote communities: Enhancing renewable energy efficiency and reducing carbon emissions. Distributed Learning and Broad Applications in Scientific Research, 4.Google Scholar ↗
  2. Alam, K., Mostakim, M. A., & Khan, M. S. I. (2017). Design and Optimization of MicroSolar Grid for Off-Grid Rural Communities. Distributed Learning and Broad Applications in Scientific Research, 3.Google Scholar ↗
  3. Integrating solar cells into building materials (Building-Integrated Photovoltaics-BIPV) to turn buildings into self-sustaining energy sources. Journal of Artificial Intelligence Research and Applications, 2(2).Google Scholar ↗
  4. Manoharan, A., & Nagar, G. MAXIMIZING LEARNING TRAJECTORIES: AN INVESTIGATION INTO AI-DRIVEN NATURAL LANGUAGE PROCESSING INTEGRATION IN ONLINE EDUCATIONAL PLATFORMS.Google Scholar ↗
  5. Joshi, D., Sayed, F., Jain, H., Beri, J., Bandi, Y., & Karamchandani, S. A Cloud Native Machine Learning based Approach for Detection and Impact of Cyclone and Hurricanes on Coastal Areas of Pacific and Atlantic Ocean.Google Scholar ↗
  6. Agarwal, A. V., & Kumar, S. (2017, November). Unsupervised data responsive based monitoring of fields. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 184-188). IEEE.Google Scholar ↗
  7. Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.Google Scholar ↗
  8. Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).Google Scholar ↗
  9. Agarwal, A. V., Verma, N., & Kumar, S. (2018). Intelligent Decision Making Real-Time Automated System for Toll Payments. In Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (pp. 223-232). Springer Singapore.Google Scholar ↗
  10. Agarwal, A. V., & Kumar, S. (2017, October). Intelligent multi-level mechanism of secure data handling of vehicular information for post-accident protocols. In 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (pp. 902-906). IEEE.Google Scholar ↗
  11. Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R. (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia: case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.Google Scholar ↗
  12. Shakibaie-M, B. (2013). Comparison of the effectiveness of two different bone substitute materials for socket preservation after tooth extraction: a controlled clinical study. International Journal of Periodontics & Restorative Dentistry, 33(2).Google Scholar ↗
  13. Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the treatment of acute hyponatremia associated with acute kidney injury. Case reports in nephrology, 2013(1), 801575.Google Scholar ↗
  14. Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-657.Google Scholar ↗
  15. Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339.Google Scholar ↗
  16. Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and survival of patients with eccrine carcinoma: a cohort study. Journal of the American Academy of Dermatology, 75(1), 215-217.Google Scholar ↗
  17. Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental Sciences, 2(43), 8251-8255.Google Scholar ↗
  18. Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking Malignancy: A Case Report. tuberculosis, 14, 15.Google Scholar ↗
  19. Maddireddy, B. R., & Maddireddy, B. R. (2020). Proactive Cyber Defense: Utilizing AI for Early Threat Detection and Risk Assessment. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 64-83.Google Scholar ↗
  20. Maddireddy, B. R., & Maddireddy, B. R. (2020). AI and Big Data: Synergizing to Create Robust Cybersecurity Ecosystems for Future Networks. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 40-63.Google Scholar ↗
  21. Damaraju, A. (2020). Social Media as a Cyber Threat Vector: Trends and Preventive Measures. Revista Espanola de Documentacion Cientifica, 14(1), 95-112.Google Scholar ↗
  22. Chirra, B. R. (2020). Enhancing Cybersecurity Resilience: Federated Learning-Driven Threat Intelligence for Adaptive Defense. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 260-280.Google Scholar ↗
  23. Chirra, B. R. (2020). Securing Operational Technology: AI-Driven Strategies for Overcoming Cybersecurity Challenges. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 281-302.Google Scholar ↗
  24. Chirra, B. R. (2020). Advanced Encryption Techniques for Enhancing Security in Smart Grid Communication Systems. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 208-229.Google Scholar ↗
  25. Chirra, B. R. (2020). AI-Driven Fraud Detection: Safeguarding Financial Data in Real-Time. Revista de Inteligencia Artificial en Medicina, 11(1), 328-347.Google Scholar ↗
  26. Gadde, H. (2019). Integrating AI with Graph Databases for Complex Relationship Analysis. InternationalGoogle Scholar ↗
  27. Gadde, H. (2019). AI-Driven Schema Evolution and Management in Heterogeneous Databases. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 332-356.Google Scholar ↗
  28. Gadde, H. (2019). Exploring AI-Based Methods for Efficient Database Index Compression. Revista de Inteligencia Artificial en Medicina, 10(1), 397-432.Google Scholar ↗
  29. Goriparthi, R. G. (2020). AI-Driven Automation of Software Testing and Debugging in Agile Development. Revista de Inteligencia Artificial en Medicina, 11(1), 402-421.Google Scholar ↗
  30. Goriparthi, R. G. (2020). Neural Network-Based Predictive Models for Climate Change Impact Assessment. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 421-421.Google Scholar ↗
  31. Reddy, V. M., & Nalla, L. N. (2020). The Impact of Big Data on Supply Chain Optimization in Ecommerce. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 1-20.Google Scholar ↗
  32. Nalla, L. N., & Reddy, V. M. (2020). Comparative Analysis of Modern Database Technologies in Ecommerce Applications. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 21-39.Google Scholar ↗
  33. Nalla, L. N., & Reddy, V. M. Machine Learning and Predictive Analytics in E-commerce: A Data-driven Approach.Google Scholar ↗
  34. Reddy, V. M., & Nalla, L. N. Implementing Graph Databases to Improve Recommendation Systems in E-commerce.Google Scholar ↗
  35. Krishnan, S., Shah, K., Dhillon, G., & Presberg, K. (2016). 1995: FATAL PURPURA FULMINANS AND FULMINANT PSEUDOMONAL SEPSIS. Critical Care Medicine, 44(12), 574.Google Scholar ↗
  36. Krishnan, S. K., Khaira, H., & Ganipisetti, V. M. (2014, April). Cannabinoid hyperemesis syndrome-truly an oxymoron!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 29, pp. S328-S328). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.Google Scholar ↗
  37. Krishnan, S., & Selvarajan, D. (2014). D104 CASE REPORTS: INTERSTITIAL LUNG DISEASE AND PLEURAL DISEASE: Stones Everywhere!. American Journal of Respiratory and Critical Care Medicine, 189, 1Google Scholar ↗
Author details
Vinay Chowdary Manduva
The convergence of Artificial Intelligence (AI) and Cloud Computing is reshaping the technological landscape, providing businesses with unprecedented opportunities to innovate, optimize operations, and scale their services. AI’s ability to process large datasets, generate predictive analytics, and automate complex tasks, combined with the scalability and accessibility of cloud platforms, offers transformative potential for organizations across industries. This paper explores how AI is redefining the capabilities of cloud computing by enhancing data processing, improving security protocols, and delivering cost-effective solutions. The integration of AI in cloud environments has enabled real-time analytics, intelligent automation, and personalized customer experiences, empowering businesses to make data-driven decisions faster and more efficiently.This study provides an in-depth analysis of the interplay between AI and cloud computing, presenting a comprehensive review of existing literature, methodologies, and real-world applications. By examining industry-specific case studies, we highlight the tangible benefits and strategic advantages for businesses adopting AI-powered cloud solutions. Furthermore, the paper discusses the challenges, including ethical concerns, data privacy issues, and the resource-intensive nature of implementing AI systems. The findings underline the pivotal role of this integration in driving digital transformation and fostering a competitive edge in an increasingly data-centric world.
✉ Corresponding Author
👤 View Profile →🔗 Is this you? Claim this publication