International Journal of Artificial Intelligence and Neural Networks
Author(s) : ANJALI SINGH , DEEPIKA GUPTA, MOHIT ARORA, PRIYANKA GAUR
The transmission of data across communication paths is an expensive process in respect time and bandwidth. Data compression is usually obtained by substituting a shorter symbol for an original symbol in the source data, which should contain the same information but with a smaller representation in length. The purpose of this paper is to show that neural networks may be promising tools for data compression without loss of information. We combine neural nets, standard statistical compression methods like Huffman coding and arithmetic coding. This paper uses Artificial Neural Network (ANN) based techniques provide new ways for the compression of data at the transmitter and decompression at the receiver with more secure manner. In this paper, security of the data can be obtained along the communication path as it is not in its original form on the communication line.