Fine Tuned Hybrid Deep Learning Model for Effective Judgment Prediction  

作　　者：G.Sukanya J.Priyadarshini 

机构地区：[1]School of Computer Science and Engineering,Vellore Institute of Technology,Chennai,600127,India

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期2925-2958,共34页工程与科学中的计算机建模(英文)

摘　　要：Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled 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.

关 键 词：Bi-GRU deep maxout semantic similarity legal judgment prediction opposition based learning pelican optimization 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]