Scenario based Deep Learning Prediction for Legal Judgment using LSTM and CNN

The problem facing society is the dark side of law ignorance. This paper is planned to build the solution to solve the problem of people’s ignorance in the law department. This paper provides deep analyses of problem scenarios or crime scenes and provides technical suggestions for people irrespective of branch. It guides the people to take the necessary steps to make them aware of the case section and the evidence to be needed. This helps to glow the light of justice for illiterate people. It gives the work of architecture of law with chains of the future. This function gives the superpower of critical analysis and support assistance of legal terms. The lawyer’s more critical work and legal terms are not aware by the people, this gives ultimate protection to survive and grip the handle of justice. This shows the real power of justice and the work of architecture of law. This is the best system people wish for the assistance of law and the judiciary. This shows the percentage of success in terms of law and evidence required. This gives real-time assistance based on the understanding of the scenario of the case and the type of case for which it is required. The NLP is used for the preprocessing steps to get the feature of the documents and also helps in linear vectorizing. This works on the methodology of vectorization using BERT with the dataset of cases. The vectorized input is trained into the LSTM and CNN for model generation. The generated model is accumulated for the best results for each scenario and propagates to find the network and form the connections.