Ant Colony Optimization for Network Customization in Cognitive Human-Computer Communication

In the era of cognitive computing, seamless and efficient human-computer communication is paramount for enhancing user experience and system performance. This paper introduces a novel approach, the Ant Colony Optimized-Long Short-Term Memory (ACO-LSTM) framework, designed for network customization to optimize communication channels in cognitive computing environments. Inspired by the collective intelligence of ant colonies, the ACO-LSTM framework combines the adaptive learning capabilities of LSTM neural networks with the optimization prowess of ant colony algorithms. The ACO-LSTM framework operates by leveraging ant-like agents to explore and exploit the solution space for optimal network configurations. These agents dynamically adapt the communication pathways based on environmental cues and user interactions, ensuring adaptability to changing conditions. The LSTM component of the framework captures temporal dependencies and patterns in the communication data, facilitating intelligent decision-making for network customization. To validate the efficacy of the proposed approach, experiments were conducted in diverse cognitive computing scenarios. The proposed ACO-LSTM model shows better accuracy with 97.9% which is 13.1% higher when compared with other methods. The integration of ant colony optimization and LSTM provides a synergistic solution for optimizing network configurations in cognitive human-computer communication, offering a promising avenue for advancing the field of intelligent systems.