ANILA: Adaptive Neuro-Inspired Learning Algorithm for Efficient Machine Learning, AI Optimization, and Healthcare Enhancement

Abstract

The Adaptive Neuro-Inspired Learning Algorithm (ANILA) offers a breakthrough in the realm of machine learning by drawing inspiration from the biological processes of the human brain. Developed to address limitations in conventional models such as CNNs and RNNs, ANILA enhances real-time responsiveness, energy efficiency, and system adaptability. By emulating neurobiological behaviors particularly sparse coding and synaptic plasticity ANILA allows systems to process data dynamically, adjust to novel inputs without retraining, and scale effectively across environments like IoT and healthcare diagnostics. Performance evaluations highlight significant reductions in latency, increases in energy efficiency (up to 92%), and exceptional adaptability to changing data streams. Despite current constraints linked to neuromorphic hardware and the interpretability of its learning processes, ANILA sets a robust foundation for the development of responsive, scalable, and sustainable AI systems across diverse sectors, particularly in real-time healthcare monitoring and diagnostics.