INTEGRATING NEURAL NETWORKS AND AI IN DATA SCIENCE: ADVANCING PREDICTIVE MODELING AND DECISION-MAKING IN COMPLEX COMPUTER SYSTEMS

Abstract

In the modern world of technological advancements in every field of life, Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the world due to their innovative features in decision-making and predictive analytics across the healthcare and manufacturing industries. The integration of AI and ML enables the storage and processing of vast amounts of data, the recognition of complex patterns, and the development of new efficiencies in adapting to any environment.  This research has focused on providing a detailed and comprehensive review of the fundamental and theoretical foundations of ML and AI, as well as their applications in practice. It emphasizes and highlights the high potential of these technologies in enhancing decision-making capacity and accuracy, as well as predictive ability. A thorough review of previous related research has been conducted in the context of the study under observation. This review identified some key developments, advancements, and challenges, and also suggests future directions for achieving greater success in AI and ML.  The findings of this research indicate that ML and AI-based applications and systems can improve predictive accuracy and enhance decision-making efficiency. It is also explored that this has far-reaching implications in all fields of life, including healthcare, finance, and manufacturing.  This study accentuates the capacity and potential of Machine Learning (ML) and Artificial Intelligence (AI) to transform and revolution the data-driven process and predictive modeling. This research points the way and paves the way for future innovations.

Keywords

Decisions-Making, Predictive Analytics, Neural Networking, Data Science, Artificial Intelligence, Machine Learning.