AI and IoT Integration: Maximizing Business Value through Data-Driven Insights

Authors

  • dheeraj

Abstract

This research paper explores the intersection of Artificial Intelligence (AI) and the Internet of Things (IoT) to drive enhanced business value through data-driven insights. It investigates how the integration of AI algorithms and IoT devices can harness real-time data, process it with advanced analytics, and generate actionable insights for organizations. Through a review of current AI and IoT applications, this paper highlights the potential for improved decision-making, increased operational efficiency, predictive maintenance, and cost optimization. Additionally, it addresses challenges such as data privacy and security while offering recommendations for successful AI and IoT integration. Ultimately, this research paper serves as a resource for businesses seeking to leverage the combined power of AI and IoT to enhance their operations and drive strategic value.

References

Kunduru, A. R. (2023). Industry best practices on implementing oracle cloud ERP security. International Journal of Computer Trends and Technology, 71(6), 1-8. https://doi.org/10.14445/22312803/IJCTT-V71I6P101

Kunduru, A. R. (2023). Cloud Appian BPM (Business Process Management) Usage In health care Industry. IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, 12(6), 339-343. https://doi.org/10.17148/IJARCCE.2023.12658

WHIG, P. (2023). Blockchain Revolution: Innovations, Challenges, and Future Directions. International Journal of Machine Learning for Sustainable Development, 5(3), 16-25.

Whig, P., Kouser, S., Bhatia, A. B., Nadikattu, R. R., & Sharma, P. (2023). Explainable Machine Learning in Healthcare. In Explainable Machine Learning for Multimedia Based Healthcare Applications (pp. 77-98). Cham: Springer International Publishing.

Whig, P., Velu, A., Nadikattu, R. R., & Alkali, Y. J. (2023). Computational Science Role in Medical and Healthcare‐Related Approach. Handbook of Computational Sciences: A Multi and Interdisciplinary Approach, 245-272.

Kunduru, A. R. (2023). Effective usage of artificial intelligence in enterprise resource planning applications. International Journal of Computer Trends and Technology, 71(4), 73-80. https://doi.org/10.14445/22312803/IJCTT-V71I4P109

Kunduru, A. R. (2023). Recommendations to advance the cloud data analytics and chatbots by using machine learning technology. International Journal of Engineering and Scientific Research, 11(3), 8-20.

WHIG, P. (2023). A Comprehensive Review of Mask Detection Using Artificial Intelligence: Methods, Challenges, and Applications. International Journal of Sustainable Development in Computing Science, 5(2), 11-20.

Kunduru, A. R. (2023). Security concerns and solutions for enterprise cloud computing applications. Asian Journal of Research in Computer Science, 15(4), 24–33. https://doi.org/10.9734/ajrcos/2023/v15i4327

Sharma, A., Kumar, A., & Whig, P. (2015b). On the performance of CDTA based novel analog inverse low pass filter using 0.35 µm CMOS parameter. International Journal of Science, Technology & Management, 4(1), 594–601.

Tomar, U., Chakroborty, N., Sharma, H., & Whig, P. (2021). AI based Smart Agricuture System. Transactions on Latest Trends in Artificial Intelligence, 2(2).

Velu, A., & Whig, P. (2021a). Protect Personal Privacy And Wasting Time Using Nlp: A Comparative Approach Using Ai. Vivekananda Journal of Research, 10, 42–52.

Atluri, H., & Thummisetti, B. S. P. (2023). Optimizing Revenue Cycle Management in Healthcare: A Comprehensive Analysis of the Charge Navigator System. International Numeric Journal of Machine Learning and Robots, 7(7), 1-13.

Atluri, H., & Thummisetti, B. S. P. (2022). A Holistic Examination of Patient Outcomes, Healthcare Accessibility, and Technological Integration in Remote Healthcare Delivery. Transactions on Latest Trends in Health Sector, 14(14).

Published

2023-10-13

How to Cite

dheeraj. (2023). AI and IoT Integration: Maximizing Business Value through Data-Driven Insights. Transactions on Recent Developments in Artificial Intelligence and Machine Learning, 15(15). Retrieved from https://journals.threws.com/index.php/TRDAIML/article/view/203

Issue

Section

Articles