Authors:
Sudheer Panyaram

Addresses:
Department of Information Technology ERP Applications, Fisker Automotive, Bloomington, Illinois, United States of America. sudheer5940@gmail.com

Abstract:

Artificial intelligence and big data are paramount for generating real-time insights enabling decision-making in complex systems. Integrating massive data streams and AI algorithms presents huge opportunities for extracting actionable insights at unprecedented speed and precision. This paper discusses how integrating artificial intelligence and big data facilitates handling complex, dynamic tasks in the health, finance, and supply chain management industries. The study describes how advanced machine learning models, neural networks, and decision algorithms enable these systems to process big data in real-time, even as that helps improve predictive and adaptive decision-making. The paper discusses our performance evaluation of AI-driven decision systems, focusing on architecture that supports efficient data processing. In addition, we present a framework that elucidates how AI models interpret Big Data within multi-layered, real-time environments. The study will also include results in terms of impedance and multi-line graphs to demonstrate system performances. We will also provide some of the tables in the key metrics. This study highlights the benefits and drawbacks of AI data integration and its potential implementation.

Keywords: Artificial Intelligence; Big Data; Real-Time Insights; Complex Systems; Decision-Making; Self-Adjusting Mechanism; AI Algorithms; Recurrent Neural Network.

Received on: 09/01/2024, Revised on: 15/03/2024, Accepted on: 25/04/2024, Published on: 07/06/2024

DOI: 10.69888/FTSIN.2024.000211

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 2, Pages: 85–95

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