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.

知识发现和管理 (KDM) 包含一个全面的流程和方法，涉及创建、发现、捕获、组织、细化、呈现和提供数据、信息和知识，并牢记特定的目标。知识管理和人工智能（AI）的核心是围绕知识本身。人工智能作为一种机制，使机器能够获取、获取、处理和利用信息，从而执行任务并发现可以与人们共享的知识，以增强战略决策。虽然传统方法在 KDM 流程中发挥着作用，但结合智能方法可以进一步提高时间和准确性方面的效率。智能技术，特别是软计算方法，具有通过利用逻辑、推理和其他计算能力在任何环境中学习的能力。这些技术可大致分为学习算法（监督式、无监督式和强化式）、逻辑和基于规则的算法（模糊逻辑、贝叶斯网络和 CBR-RBR）、自然启发算法（遗传算法、粒子群优化和蚁群优化），以及结合这些算法的混合方法。这些智能技术的主要目标是解决农村和智能数字社会面临的日常挑战。在这项研究中，作者广泛研究了与不同问题相关的各种智能计算方法（ICM），提供了准确合理的基于知识的解决方案。探索了单一 ICM 和组合 ICM 的应用来解决特定领域的问题，并分析和讨论了它们的有效性。结果表明，与单一 ICM 相比，组合 ICM 表现出更高的效率。此外，作者还根据 ICM 的应用领域、参数、方法/算法、效率和可接受的结果对 ICM 进行了分析和比较。此外，作者还确定了可以使用智能技术有效解决的几个问题场景。