Energy-Aware Cognitive Learning Models with Bio-Inspired Optimization for Next-Generation Intelligent Electronics

Abstract

The rapid evolution of intelligent electronic systems has created a demand for energy-efficient and adaptive computational frameworks capable of supporting real-time decision-making and autonomous operations. This paper presents an energy-aware cognitive learning framework integrated with bio-inspired optimization techniques for next-generation intelligent electronics. The proposed architecture combines deep cognitive learning mechanisms with a hybrid optimization strategy to enhance energy utilization, prediction accuracy, and computational efficiency. Feature extraction and learning are performed through cognitive neural models, while bio-inspired optimization algorithms are employed to optimize system parameters and reduce energy consumption. Extensive experiments demonstrate that the proposed model achieves an average classification accuracy of 98.7%, precision of 98.4%, recall of 98.2%, and F1-score of 98.3%, while reducing energy consumption by approximately 24.8% and improving processing efficiency by 19.6% compared with conventional intelligent electronic frameworks. The obtained results indicate that the proposed energy-aware cognitive learning model provides a reliable and scalable solution for future intelligent electronic applications requiring low-power operation and high computational performance.