Cognitive Computing In Motion: Investigating Real-Time Learning Models For Enhanced Decision-Making In Mobile Environments

Abstract

This paper investigates real-time learning models for enhancing decision-making in mobile environments, focusing on cognitive computing applications. The research methodology involves generating a hypothetical dataset to simulate input data, splitting it into training and testing sets, and training a Random Forest classifier. The decision boundary of the model is visualized to understand its decision-making process. Additionally, cognitive computing scores are simulated over time to analyze their temporal evolution, and performance metrics for tabulative, programmable, and cognitive systems are visualized. Results and discussions provide insights into the models' performance and behavior, highlighting their potential for improving decision-making in dynamic mobile contexts. The decision boundary graph illustrates the model's ability to differentiate between classes in a two-dimensional feature space, while cognitive computing score graphs depict trends in cognitive computing advancements over time. The visualizations of performance metrics offer valuable insights into the development and implications of various cognitive computing aspects. Overall, this study contributes to advancing real-time learning models for enhanced decision-making in mobile environments, with implications for diverse applications in cognitive computing.