A Hybrid Framework Combining Rule-Based and Deep Learning Approaches for Data-Driven Verdict Recommendations
Muhammad Hameed Siddiqi1,*, Menwa Alshammeri1, Jawad Khan2,*, Muhammad Faheem Khan3, Asfandyar Khan4, Madallah Alruwaili1, Yousef Alhwaiti1, Saad Alanazi1, Irshad Ahmad5
1 College of Computer and Information Sciences, Jouf University, Sakaka, 2014, Aljouf, Saudi Arabia
2 School of Computing, Gachon University, Seongnam, 13120, Republic of Korea
3 Institute of Computer Science & IT, University of Science and Technology, Bannu, 28100, KPK, Pakistan
4 Institute of Computer Sciences and Information Technology, University of Agriculture, Peshawar, 25130, KPK, Pakistan
5 Department of Computer Science, Islamia College Peshawar, 25000, KPK, Pakistan
* Corresponding Author: Muhammad Hameed Siddiqi. Email: 
; Jawad Khan. Email: 
(This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.062340
Received 16 December 2024; Accepted 28 March 2025; Published online 18 April 2025
Abstract
As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model, analyzing the feature vectors of query cases against a legal knowledge base. Verdicts are then recommended through a rule-based retrieval system, enhanced by predefined legal statutes and regulations. By merging rule-based methodologies with deep learning, this framework addresses the interpretability challenges often associated with contemporary AI models, thereby enhancing both transparency and generalizability across diverse legal contexts. The system was rigorously tested using a legal corpus of 43,000 case laws across six categories: Criminal, Revenue, Service, Corporate, Constitutional, and Civil law, ensuring its adaptability across a wide range of judicial scenarios. Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6% with an F-Score of 95%. The semantic similarity module, tested using Manhattan, Euclidean, and Cosine distance metrics, achieved 88% accuracy and a 93% F-Score for short queries (Manhattan), 89% accuracy and a 93.7% F-Score for medium-length queries (Euclidean), and 87% accuracy with a 92.5% F-Score for longer queries (Cosine). The verdict recommendation module outperformed existing methods, achieving 90% accuracy and a 93.75% F-Score. This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes, offering a robust, interpretable, and adaptable solution for the evolving demands of modern legal systems.
Keywords
Verdict recommendation; legal knowledge base; judicial text; case laws; semantic similarity; legal domain features; rule-based; deep learning
Open Access
Login
Register
Submit a Paper
Propose a Special lssue
Download PDF
Downloads
Citation Tools
