Paper Title
Comparison of Supervised and Unsupervised Learning Approaches for Intelligent Decision-Making

Abstract
Decision making is the keystone function of numerous contemporary sys-tems, and this demands algorithms that can make sense out of complicated data. This paper is a comparative study of the two great categories: super-vised learning and unsupervised learning though this time lays weight upon their accuracies, especially their performance, applicability, and limitations. The parameters for the analyses are decision accuracy, robustness, introduc-tion of new knowledge, and complexity of algorithms. The results reveal that supervised learning is most appropriate for the well-annotated datasets demanding accuracy over precision, while the unsupervised learning meth-od suits more to find a working model with unannotated datasets. These re-sults direct implementations of the techniques in different intelligent deci-sion-making scenarios and also encourage hybrid development that will improve the performance. Keywords - Supervised learning, Unsupervised learning, Intelligent decision-making, Accuracy, Robustness, Adaptability, Algorithmic complexity.