A hybrid CNN + BILSTM deep learning-based DSS for efficient prediction of judicial case decisions

作者：

Highlights：

• Utilization of an x2 test to rank and select optimal features has a significant role in the prediction of judicial case decisions.

• Utilization of a hybrid DL (CNN+BiLSTM)-based DSS to predict judicial case decisions.

• Evaluate the performance of classic ML classifiers in comparison to the proposed DSS to predict judicial case decisions.

摘要

•Utilization of an x2 test to rank and select optimal features has a significant role in the prediction of judicial case decisions.•Utilization of a hybrid DL (CNN+BiLSTM)-based DSS to predict judicial case decisions.•Evaluate the performance of classic ML classifiers in comparison to the proposed DSS to predict judicial case decisions.

论文关键词：Judicial case prediction,Legal data,Hybrid deep learning,Neural networks,Feature selection,Decision support system

论文评审过程：Received 4 May 2021, Revised 12 July 2022, Accepted 27 July 2022, Available online 29 July 2022, Version of Record 4 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118318