Hybrid Intelligent Systems (HIS) represent a novel paradigm in artificial intelligence, synthesizing diverse AI techniques to create more powerful and adaptable systems. This abstract delves into the core attributes and applications of Hybrid Intelligent Systems, emphasizing their multidisciplinary approach. By integrating rule-based systems, expert systems, machine learning, fuzzy logic, and other AI methodologies, Hybrid Intelligent Systems harnesses the complementary strengths of different techniques. This integration enables improved problem-solving capabilities, flexibility, and performance across various domains. Hybrid Intelligent System’s adaptability is a key feature, allowing for dynamic adjustments based on the nature of tasks and evolving environments. This adaptability is facilitated by components that incorporate adaptive learning, ensuring continuous improvement over time. The versatility of Hybrid Intelligent Systems is evident in its real-world applications, spanning industries such as finance, healthcare, and manufacturing. However, the ethical dimensions of Hybrid Intelligent Systems development and deployment cannot be overstated. Transparency, fairness, user privacy protection, and accountability are paramount principles to uphold, reinforcing responsible and ethical use of these advanced technologies. As Hybrid Intelligent Systems continues to evolve, its collaborative nature, balancing artificial and human intelligence, positions it as a pivotal force in addressing complex challenges and shaping the future of AI.
Hybrid Intelligence (HI), AI methodologies, real-world applications
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