Forecasting the decision making process of Supreme Court using hierarchical convolutional neural network. (14th June 2022) - Sivaranjani, N.; Jayabharathy, J.
Artificial intelligence is one of the most energising innovations applied in many fields like text processing, and image processing. Every case, which is present in the courtroom, is inspired to get justice. In this paper, we propose a decision forecasting model which aims to predict whether the filed case in the Supreme Court and also the cases with an unsatisfied decision from the lower court will win or not by considering the past similar cases. This is to be able to better predict if the current case will win if an appeal is chosen. In this paper, two algorithms have been proposed: 1) bi-SVM, it is used to classify the cases as civil or criminal; 2) C-XGB is used to predict the chances of winning. When an out-of-sample case, is given as input, the model yields 96% of accuracy which is higher than the accuracy of existing models.