Nov 10, 2015 · Our model yields 70.4 % accuracy on predicting the overall decisions of Supreme Court, using only data available prior to the case and utilizing far less ...
We explicitly use only data available prior to the decision and predict the decisions with 70.4 percent accuracy across 7,700 cases with nearly 70,000 justice ...
Nov 9, 2015 · We explicitly use only data available prior to the decision and predict the decisions with 70.4 percent accuracy across 7,700 cases with nearly ...
The paper 'Using Modern Neural Networks to Predict the Decisions of Supreme Court of the United States with State-of-the-Art Accuracy' uses a more advanced ...
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A time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over ...
Using modern neural networks to predict the decisions of supreme court of the united states with state-of-the-art accuracy. RD Sharma, S Mittal, S Tripathi ...
May 24, 2023 · To do so, we engineered a deep neural network (DNN) called SCOTUS_AI to predict Supreme Court outcomes through a sentiment analysis of ...
The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN.
70.4% accuracy in predicting the judgments of US Supreme Court ... Using Modern. Neural Networks to Predict the Decisions of Supreme Court of the United States.
Abstract. In this paper, we discuss previous research in automatic prediction of court deci- sions. We define the difference between outcome identification, ...