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Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set

Author

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  • Daniyal Alghazzawi

    (Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia)

  • Omaimah Bamasag

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia)

  • Aiiad Albeshri

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia)

  • Iqra Sana

    (Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan 29220, Pakistan)

  • Hayat Ullah

    (Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan 29220, Pakistan)

  • Muhammad Zubair Asghar

    (Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan 29220, Pakistan)

Abstract

As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts is no easy task. Currently, previous research on forecasting court outcomes using small experimental datasets yielded a number of unanticipated predictions utilizing machine learning (ML) models and conventional methodologies for categorical feature encoding. The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN, in order to effectively forecast court rulings using historic judicial datasets. By prioritizing and choosing features that scored the highest in the provided legal data set, only the most pertinent features were picked. After that, the LSTM+CNN model was utilized to forecast lawsuit verdicts. In contrast to previous related experiments, this composite model’s testing results were promising, showing 92.05 percent accuracy, 93 percent precision, 94 percent recall, and a 93 percent F1-score.

Suggested Citation

  • Daniyal Alghazzawi & Omaimah Bamasag & Aiiad Albeshri & Iqra Sana & Hayat Ullah & Muhammad Zubair Asghar, 2022. "Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set," Mathematics, MDPI, vol. 10(5), pages 1-30, February.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:683-:d:755989
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    References listed on IDEAS

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    1. Daniel Martin Katz & Michael J Bommarito II & Josh Blackman, 2017. "A general approach for predicting the behavior of the Supreme Court of the United States," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
    2. Kongfan Zhu & Rundong Guo & Weifeng Hu & Zeqiang Li & Yujun Li, 2020. "Legal Judgment Prediction Based on Multiclass Information Fusion," Complexity, Hindawi, vol. 2020, pages 1-12, October.
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    Cited by:

    1. Jingjing Wu & Le Cheng & Yi Yang, 2022. "A corpus-based interpretation of the discourse–cognitive–society triangle on Chinese court judgments," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    2. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.

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