Artificial Neural Networks for Data Processing: A Case Study of Image Classification

Abstract

An Artificial Neural Network (ANN) is a data processing paradigm inspired by the way organic nervous systems, such as the brain, process data. The innovative structure of the information processing system is a crucial component of this paradigm. It is made up of a huge number of highly linked processing components (neurons) that work together to solve issues. Neural networks handle data in the same manner that the human brain does. The network is made up of several densely linked processing units (neurons) that operate in parallel to solve a given problem. They are unable to be programmed to execute a specific activity. ANN, like humans, learns by example. Through a learning process, an ANN is trained for a specific application, such as pattern recognition or data categorization. In biological systems, learning includes changes to the synaptic connections that occur between neurons. This is also true for ANNs. Artificial Neural Networks are used for classification, regression, and grouping. Stages of image processing are classified as preprocessing, feature extraction, and classification. It can be utilized later in the process. ANN should be provided with features and output should be classified. This paper provides an overview of Artificial Neural Networks (ANN), their operation, and training. It also explains the application and its benefits. Artificial Neural Network has been used to classify the MNIST dataset.