Judgement classification using hybrid ANN-shuffled frog leaping model on cyber crime judgement database

Abstract

The world has taken dramatic transformation after advent of Information Technology, it is hard to find the people without cyber connected and every activity of us is guided and regulated by the connected networks. As the world is depending upon the information technology there is same extent of research is getting on cyber monitoring activities taking place around the world. Now, it is very vital to classify and prediction of cybercrimes on the connected era. The objective of the paper is to classify the cyber crime judgments precedents for providing knowledgeable and relevant information to the cyber crime legal stakeholders. The stakeholders extract information from the precedents is a crucial research problem because so much of judgments available in a digital form with remarkable evaluation of internet and bid data analytics. It is necessary to classify the precedents and to provide a bird- eye view of the relevant legal topics. In this study cybercrime related 2500 judgments are considered for evaluation of the Feed Forward Neural - Shuffled Frog Leaping (FNN-SFL) model. To achieve this objective a Feed Forward Neural based model with tuning of Term weights by adaption of a Bio Inspired tuning model Shuffled Frog Leaping model. The experiments are conducted and implemented the newly proposed FNNSFL algorithm. The results and discussions are presented. The conclusions and future scope are presented at the end of the paper.