The Influence of Data Structures on AI Decision-Making Processes

Abstract: This research examines the impact of different data structures on the efficacy, precision, and efficiency of AI systems' decision-making. These data structures include arrays, linked lists, trees, graphs, and hash tables. Artificial intelligence's (AI) decision-making process is highly dependent on the data structures utilized to arrange, retrieve, and modify information. Knowing how data architectures affect AI decision-making is crucial as AI develops and applications get more complicated.

Artificial Intelligence, data structures, decision-making, algorithm

1. Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. [Google Scholar] [Crossref]

2. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. [Google Scholar] [Crossref]

3. Artificial Intelligence, Tata McGraw Hill, Elaine Rich and Kevin Knight Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms. MIT Press. [Google Scholar] [Crossref]

• Topology Optimization for Low-Power-Wide-Area Networks (LPWANs) within Internet of Things (IOT)
• Detecting Misinformation Using Multimodal AI Models on Social Media Platforms
• Enhanced Face Detection Using Haar Cascade with Histogram Equalization, Sharpening, and Denoising for Real-Time Applications
• The Evolution of Data Analytics and Its Future Implication
• Utilizing AI Approaches for Generating Code Automatically