AI-DRIVEN DATA ANALYTICS FOR REAL-TIME DECISION-MAKING
Description
Artificial intelligence-powered data analytics functions as an indispensable transformational force that helps organizations obtain immediately useful information from large databases while responding rapidly to shifting market conditions across different business sectors. This research analyzes how artificial intelligence, when connected to data analytics, drives transformational development through analyses of real-time applications along with advantages and obstacles that exist in addition to future analytical patterns. Through their union, data analytics and artificial intelligence systems enable businesses to derive actionable decisions from large database analysis, which leads to changed strategic decisions in multiple enterprise domains. Organizations now recognize that processing large amounts of data in real-time has become a strategic necessity to achieve operational excellence while maximizing customer satisfaction. Artificial intelligence and data analytics harmonization created a fundamental change in the real-time decision framework that allows organizations to use data power for agile strategic moves in dynamic business environments. AI algorithms working together with data analytics methods allow organizations to obtain important insights so they can predict future business trends while automating operational choices to enhance overall productivity along with business opportunities. AI, together with data analytics, produces maximum effects during mission-critical decision-making situations involving finance risk control medical diagnosis supply chain continuity, and cybersecurity protection events. AI algorithms empower the automated analysis of complex datasets by using machine learning together with deep learning as well as natural language processing to detect hidden patterns, anomalies, and correlations that traditional methods would struggle to reveal.
Files
IJPREMS50400066723.pdf
Files
(157.0 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:65adde5bf6487d08302f610e5a4e793e
|
148.8 kB | Preview Download |
|
md5:5d800f011851a2bcdedb65acc88b420e
|
8.1 kB | Download |
Additional details
References
- Kasula, V. K. (2023). AI-driven banking: A review on transforming the financial sector. World Journal of Advanced Research and Reviews, 2023, 20(02), 1461-1465
- S. R. Addula, K. Meduri, G. S. Nadella, and H. Gonaygunta, "AI and Blockchain in Finance: Opportunities and Challenges for the Banking Sector," IJARCCE, vol. 13, no. 2, Feb. 2024, doi: 10.17148/ijarcce.2024.13231.
- Konda, B. (2024). Predictive Analysis for Employee Turnover Prevention Using Data-Driven Approach. International Journal of Science and Engineering Applications, 13(08), pp. 112-116.
- Daruvuri, R. (2025). Adaptive resource allocation in cloud computing using advanced AI techniques. 5th IEEE Int. Conf. Expert Clouds and Applications (ICOECA), Bangalore, India, 2025, pp. 90–96.
- Meduri, K., Nadella, G. S., Yadulla, A. R., Kasula, V. K., Maturi, M. H., Brown, Satish, S., & Gonaygunta, H. (2024). Leveraging Federated Learning for Privacy-Preserving Analysis of Multi-Institutional Electronic Health Records in Rare Disease Research. Journal of Economy and Technology, vol 3, 177-189.
- Kumar, D., & Singh, S. (2024). Analyzing the impact of machine learning algorithms on risk management and fraud detection in financial institutions. International Journal of Research Publication and Reviews, 5(5), 1797- 1804.
- Addula, S. R., & Sajja, G. S. (2024). Automated machine learning to streamline data-driven industrial application development. 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES), 1-4. https://doi.org/10.1109/ic3tes62412.2024.10877481
- Yadulla, A. R., Yenugula, M., Kasula, V. K., Konda, B., Addula, S. R., & Rakki, S. B. (2023). A time-aware LSTM model for detecting criminal activities in blockchain transactions. International Journal of Communication and Information Technology 2023; 4(2): 33-39
- K. Patibandla, R. Daruvuri, and P. Mannem (2024). Streamlining workload management in AI-driven cloud architectures: A comparative algorithmic approach. International Research Journal of Engineering and Technology, vol. 11, no. 11, pp. 113-121.
- Kumar, D., Pawar, P. P., Gonaygunta, H., Nadella, G. S., Meduri, K., & Singh, S. (2024). Machine learning's role in personalized medicine & treatment optimization. World Journal of Advanced Research and Reviews, 21(2), 1675-1686.
- Daruvuri, R. (2025). Efficient CSI feedback for large-scale MIMO IoT systems using YOLOv8-based network. 1st IEEE Conf. Secure and Trustworthy CyberInfrastructure for IoT and Microelectronics (SaTC), Ohio, USA, 2025, pp. 1–5.
- Kasula, V. K., Konda, B., Yadulla, A. R., & Yenugula, M. (2022). Hybrid Short Comparable Encryption with Sliding Window Techniques for Enhanced Efficiency and Security. International Journal of Science and Research Archive, 5(01), 151-161.
- H. Gonaygunta, D. Kumar, S. Maddini, and S. F. Rahman, "How can we make IoT Applications better with Federated Learning- A Review," IJARCCE, vol. 12, no. 2. Feb. 20, 2023. doi: 10.17148/ijarcce.2023.12213.
- Yenugula, M., Yadulla, A. R., Konda, B., Addula, S. R., & Kasula, V. K. (2023). Enhancing Mobile Data Security with Zero-Trust Architecture and Federated Learning: A Comprehensive Approach to Prevent Data Leakage on Smart Terminals. Journal of Recent Trends in Computer Science and Engineering (JRTCSE), 11(1), 52-64.
- Konda, B., Kasula, V. K., Yenugula, M., Yadulla, A. R., & Addula, S. R. (2022). Homomorphic encryption and federated attribute-based multi-factor access control for secure cloud services in integrated space-ground information networks, International Journal of Communication and Information Technology, 3(2): 33-40.
- Yenugula, M. (2022). Google Cloud Monitoring: A Comprehensive Guide. Journal of Recent Trends in Computer Science and Engineering (JRTCSE), vol. 10, no. 2, pp. 40-50.
- Pawar, P. P., Kumar, D., Krupa, R., Pareek, P. K., Manoj, H. M., & Deepika, K. S. (2024, July). SINN Based Federated Learning Model for Intrusion Detection with Blockchain Technology in Digital Forensic. In 2024 International Conference on Data Science and Network Security (ICDSNS)(pp. 01-07). IEEE.
- Kasula, V. K. (2024). Awareness of Cryptocurrency Scams. University of the Cumberlands, Kentucky, United States, 2024.
- Kumar, D., Pawar, P., Gonaygunta, H., & Singh, S. (2023). Impact of federated learning on industrial iot-A Review. Int. J. Adv. Res. Comput. Commun. Eng, 13(1), 1-12.
- Kumar, D. (2022). Factors Relating to the Adoption of IoT for Smart Home. University of the Cumberlands.
- Pawar, P. (2022). Factors Influencing Blockchain Technology Adoption in Supply Chain (Doctoral dissertation, University of the Cumberlands).
- Pillai, S. E. V. S., & Pawar, P. (2024, April). Blockchain Technology for Enhancing Trust and Security in Mobile Networks. In 2024 2nd International Conference on Networking and Communications (ICNWC) (pp. 1-6). IEEE.
- Arafat, Y. (2025). A Comprehensive Study on Utilizing Machine Learning Techniques for Detecting Anomalies in Internet of Things (IoT) Environments. Journal Publication of International Research for Engineering & Management (JOIREM), 10(02).
- Tusher, S. H. Design of a Secure and Scalable Smart Home Architecture Using IoT and Blockchain Integration for Enhanced Privacy Preservation, Data Integrity, and Decentralized Control.
- F. Sufi, "Open-source cyber intelligence research through PESTEL framework: Present and future impact," Societal Impacts, vol. 3, p. 100047, Feb. 2024, doi: 10.1016/j.socimp.2024.100047.
- Chen, Y. P., Karkaria, V., Tsai, Y. K., Rolark, F., Quispe, D., Gao, R. X., ... & Chen, W. (2025). Real-time decision-making for digital twin in additive manufacturing with model predictive control using time-series deep neural networks. arXiv preprint arXiv:2501.07601.
- S. Vasanth, SP. Keerthana, and G. Saravanan, "Demystifying AI: A Robust and Comprehensive Approach to Explainable AI," p. 1, Nov. 2024, doi: 10.1109/icec59683.2024.10837078.