Open Access

ARTICLE

Fine Tuned Hybrid Deep Learning Model for Effective Judgment Prediction

G. Sukanya, J. Priyadarshini^*

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

* Corresponding Author: J. Priyadarshini. Email:

(This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)

Computer Modeling in Engineering & Sciences 2025, 142(3), 2925-2958. https://doi.org/10.32604/cmes.2025.060030

Received 22 October 2024; Accepted 20 January 2025; Issue published 03 March 2025

Abstract

Advancements in Natural Language Processing and Deep Learning techniques have significantly propelled the automation of Legal Judgment Prediction, achieving remarkable progress in legal research. Most of the existing research works on Legal Judgment Prediction (LJP) use traditional optimization algorithms in deep learning techniques falling into local optimization. This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching. Typically, the selection of search agents within a boundary is done randomly, which increases the time required to achieve global optimization. To address this, the proposed Chaotic Opposition Learning-based Pelican Optimization (COLPO) method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function, enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization. Also, the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout. The output scores are fused using improved score level fusion to boost prediction accuracy. The proposed COLPO method experiments with real-time Madras High Court criminal cases (Dataset 1) and the Supreme Court of India database (Dataset 2), and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm (SSA), COOT, Spider Monkey Optimization (SMO), Pelican Optimization Algorithm (POA), as well as baseline classifier models and transformer neural networks. The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4% and 94.24% accuracy, respectively.

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Keywords

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