Paper Title
NEURAL NETWORK-BASED ERUDITION AUTOMATA USING CNN: THE DEVELOPMENT OF AUTONOMOUS LEARNING SYSTEMS

Abstract
Huge advancements in AI and ML have had a significant influence on the evolution of autonomous learning systems. This study delves into the topic of building Neural Network-Based Erudition Automata using Convolutional Neural Networks (CNNs). Convolutional neural networks (CNNs) are a novel architecture developed to enhance autonomous learning and decision-making. Utilising convolutional neural networks (CNNs), renowned for its efficacy in processing structured grid-like data like photos, allows the automata to autonomously detect patterns, learn from massive datasets, and make informed decisions. The proposed automata leverage CNNs' hierarchical feature extraction capabilities to achieve better performance in tasks such as image classification, object recognition, and real-time decision-making. Because they are built on CNNs, the automata are better at learning, can adapt to new situations quickly, and can handle complex data inputs with ease. The experimental consequences display that the proposed machine is helpful; it outperforms conventional neural community fashions in studying pace, generalisability, and accuracy. This examine determined that convolutional neural networks (CNNs) will be the spine of destiny autonomous learning structures. More sophisticated AI applications with little to no human intervention can be viable due to this. Keywords - Autonomous Learning Systems, Convolutional Neural Networks (CNNs), Neural Network-Based Erudition Automata, Hierarchical Feature Extraction, Real-Time Decision-Making, Adaptive Learning