Paper Details

An Artificial Neural Network is basically a computing system that imitates the functionality and structure of
a human brain. Just like the brain uses neurons to process data and make decisions, Artificial Neural Networks use
artificial neurons to analyze data, identify patterns and make predictions. These networks consist of layers of
interconnected neurons that work together to solve complex problems. Now these layers learn, adapt and solve tough
problems in sectors like image classification, speech processing, natural language processing and weather prediction,
making them a key part of new technology trends. An Artificial Neural Network is a computational model inspired by the
human brain. It consists of interconnected processing units called neurons that learn to recognize patterns, process
complex data, and make predictions. Artificial Neural Networks serve as the backbone for modern machine learning and
artificial intelligence. These networks have input, hidden and output layers. Image classification is the process of
assigning a predefined label to an image based on its visual content. The goal is to enable a model to automatically
recognize patterns, textures and shapes to categorize images into classes it has learned during training correctly. In
deep learning, image classification is a common task where a model learns to recognize objects, scenes, or patterns in
images. A trained model can classify images into categories like cat, dog or elephant based on features it has extracted
from the input images.

Chetan Hirjibhai Vanapariya, “IMAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 13, Issue 6, pp. 10-16, June 2026.