Abstract: In this paper we are generating the image of different alphabets I,L,O,A and C and predicting these alphabets using machine learning tool. After predicting the or estimating the number we have choose first alphabet I and add the Gaussian noise in that number with two different amounts i.e. 5%, and 50 %.We have seen the effect of noise on the number image. The same experiment was repeated with the Poisson noise environment. Finally we compare between the images affected by Gaussian and Poisson noise.
Keywords: Artificial neural networks (ANN), Image processing, machine learning, neural networks, Hopfield network
- INTRODUCTION
Artificial intelligence is a great tool in present scenario in all kind of optimization problems. There are several methods involve in AI. Machine learning and deep learning are the key techniques of machine learning. In deep learning the artificial neural networks are widely used for prediction and classification methods. Here we are also using the Hopfield method for image prediction under Gaussian noise and Poisson noise.
- Hopfield neural networks (HNN) :
Hopfield neural network is a type of artificial neural network which is discovered in1982 by Dr. John J. Hopfield. It consists of a single layer which contains one or more fully connected recurrent neurons. The application of Hopfield neural network is in character reorganization, pattern classification problems or in auto association optimization problems..
In the Hopfield neural network the input vector and output vectors are in the discrete form. The input can be classified into binary (0,1) or bipolar (+1, -1) form.. The network has symmetrical weights with no self-connections i.e., wij = wji and wii = 0. Network architecture is shown in Fig 1