Relevant Text Extraction Using Enhanced Artificial Neural Network Fuzzy Inference System

Identifying the security level of a private record is an indispensable task for associations to ensure the secret data represented. Different rules and systems are being applied by human specialists. Expanding the number of private data in associations are making it hard to order all the reports cautiously with human exertion. In the present advanced period, it turns into a test for users of the web to discover explicit data on the web. Many online records are recovered, and it is difficult to process all the recovered data. The programmed text summary is a procedure that recognizes the significant focuses from all the related archives to deliver a compact outline. Enormous datasets surround covered patterns which pass on significant information about the dataset. Information mining research manages the extraction of helpful and important data from such huge datasets. The procedure of information extraction can be seen as exploration and examination of huge amounts of information, via programmed or self-loader mean, to find significant examples and rules. These days, text acknowledgement has become a significant developing zone in handling images and extraction of data. In this proposed work, an efficient text extraction method enhanced artificial neural network-based fuzzy inference system (EANN-FIS) is introduced for relevant text extraction including hidden layers. The proposed method is compared with the traditional methods and the results show that the proposed method is exhibiting better performance in terms of accuracy and speed.