Enhancing knowledge discovery and management through intelligent computing methods: a decisive investigation

Abstract

Knowledge Discovery and Management (KDM) encompasses a comprehensive process and approach involving the creation, discovery, capture, organization, refinement, presentation, and provision of data, information, and knowledge with a specific goal in mind. At the core, Knowledge Management and Artificial Intelligence (AI) revolve around knowledge itself. AI serves as the mechanism enabling machines to obtain, acquire, process, and utilize information, thereby executing tasks and uncovering knowledge that can be shared with people to enhance strategic decision-making. While conventional methods play a role in the KDM process, incorporating intelligent approaches can further enhance efficiency in terms of time and accuracy. Intelligent techniques, particularly soft computing approaches, possess the ability to learn in any environment by leveraging logic, reasoning, and other computational capabilities. These techniques can be broadly categorized into Learning algorithms (Supervised, Unsupervised, and Reinforcement), Logic and Rule-Based algorithms (Fuzzy Logic, Bayesian Network, and CBR-RBR), Nature-inspired algorithms (Genetic algorithm, Particle Swarm Optimization, and Ant Colony Optimization), and hybrid approaches that combine these algorithms. The primary objective of these intelligent techniques is to address the day-to-day challenges faced by rural and smart digital societies. In this study, the authors extensively investigated various intelligent computing methods (ICMs) specifically relevant to distinct problems, providing accurate and reasonable knowledge-based solutions. The application of both single ICMs and combined ICMs was explored to solve domain-specific problems, and their effectiveness was analyzed and discussed. The results indicated that combined ICMs exhibited superior efficiency compared to single ICMs. Furthermore, the authors conducted an analysis and comparison of ICMs based on their application domain, parameters, methods/algorithms, efficiency, and acceptable outcomes. Additionally, the authors identified several problem scenarios that can be effectively resolved using intelligent techniques.