Skip to main content
Springer Nature Link
Log in

A Study on Argument-Based Analysis of Legal Model

  • Conference paper
  • First Online:
Innovations in Bio-Inspired Computing and Applications (IBICA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1372))

  • 641 Accesses

Abstract

Unbounded delay in the delivery of justice has resulted in large number of pending cases thereby affecting the well-being of beneficiary in a broader sense. Due to this delay, complainant, accused or witness may either become unfit for trail or hostile, etc. which may jeopardize the promptness of judicial system. However, its efficiency can be enhanced using Machine Learning algorithm thereby reducing the workload of legal professional so that they can engage more time to resolve those pending cases. This paper focuses on argument-based prediction system over legal data set. Precisely, we have applied Supervised Machine Learning technique over the cases related to ‘Domestic Violence Against Women’ and have proposed a model to predict guilt of an accused. We have approached in sequential manner, precisely: (i) Hard-copies of argument based legal documents are collected from various trial courts of West Bengal. (ii) Data set are generated manually based on specific parameters which act as deciding factors for prediction of accusation. (iii) Supervised Machine Learning algorithm are used to train those legal data set to predict our desired output. We have validated the performance and accuracy of our proposed legal prediction system using standard classifiers like Decision Tree, Naive-Bayes Classifier and K-Nearest Neighbor (KNN).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., Lee, I.: Artificial intelligence in the 21st century. IEEE Access 6, 34403–34421 (2018). https://doi.org/10.1109/ACCESS.2018.2819688

    Article  Google Scholar 

  2. Singh, G., Kumar, B., Gaur, L., Tyagi, A.: Comparison between multinomial and bernoulli Naïve Bayes for text classification. In: 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pp. 593–596. IEEE (2019). https://doi.org/10.1109/ICACTM.2019.8776800

  3. Van Engers, T., Boer, A., Breuker, J., Valente, A., Winkels, R.: Ontologies in the legal domain. In: Digital Government, pp. 233–261. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-71611-4_13

  4. Rosencranz, A., Jackson, M.: The Delhi pollution case: the supreme court of india and the limits of judicial power. Colum. J. Envtl. L. 28(2003), 223 (2003)

    Google Scholar 

  5. Mahmud, S.R., Maowa, J., Wibowo, F.W.: Women empowerment: one stop solution for women. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 485–489. IEEE (2017). 10. 1109/ICITISEE.2017.8285555.

    Google Scholar 

  6. Zeleznikow, J., Stranieri, A.: Knowledge discovery in the legal domain. In: Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence, pp. 574–577. IEEE (1997). https://doi.org/10.1109/TAI.1997.632305

  7. Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: A review of classification techniques. Emerg. Arti. Intell. Appl. Comput. Eng. 160(1), 3–24 (2007)

    Google Scholar 

  8. Islam, M.J., Wu, Q.J., Ahmadi, M., Sid-Ahmed, M.A.: Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers. In: 2007 International Conference on Convergence Information Technology (ICCIT 2007), pp. 1541–1546. IEEE, November 2007

    Google Scholar 

  9. Guvenir, H.A., Acar, B., Demiroz, G., Cekin, A.: A supervised machine learning algorithm for arrhythmia analysis. In: Computers in Cardiology 1997, pp. 433–436. IEEE, September 1997

    Google Scholar 

  10. Yaram, S.: Machine learning algorithms for document clustering and fraud detection. In: 2016 International Conference on Data Science and Engineering (ICDSE), pp. 1–6. IEEE, August 2016

    Google Scholar 

  11. Alfathdyanto, K., Febrianti, M.S., Prihatmanto, A.S., Machbub, C.: Creating database for traditional dance categorization using CSV file format. In: 2018 IEEE 8th International Conference on System Engineering and Technology (ICSET), pp. 167–171. IEEE (2018). https://doi.org/10.1109/ICSEngT.2018.8606387

  12. Fang, Z., Wang, J., Geng, J., Kan, X.: Feature selection for malware detection based on reinforcement learning. IEEE Access 7(2019), 176177–176187 (2019). https://doi.org/10.1109/ACCESS.2019.2957429

    Article  Google Scholar 

  13. Pan, X., Hu, X., Zhang, Y.H., Feng, K., Wang, S.P., Chen, L., Huang, T., Cai, Y.D.: Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection. Genes 9(4), 208 (2018)

    Article  Google Scholar 

  14. Babu, B.V., Kar, S.K.: Domestic violence against women in eastern India: a population-based study on prevalence and related issues. BMC Public Health 9(1), 129 (2009)

    Article  Google Scholar 

  15. Yang, H.-D., Lee, S.-W.: Combination of manual and non-manual features for sign language recognition based on conditional random field and active appearance model. In: 2011 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 1726–1731. IEEE (2011). https://doi.org/10.1109/ICMLC.2011.6016973

  16. Tan, Y., Zhang, G.-J.: The application of machine learning algorithm in underwriting process. In: 2005 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3523–3527. IEEE (2005). https://doi.org/10.1109/ICMLC.2005.1527552

  17. Carneiro, G., Chan, A., Moreno, P., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007). https://doi.org/10.1109/TPAMI.2007.61

    Article  Google Scholar 

  18. Yu, Y., Zhong-Liang, F., Xiang-Hui, Z., Wen-Fang, C.: Combining classifier based on decision tree. In: 2009 WASE International Conference on Information Engineering, vol. 2, pp. 37–40. IEEE (2009). https://doi.org/10.1109/ICIE.2009.12

  19. Bittencourt, H.R., de Oliveira Moraes, D.A., Haertel, V.: A binary decision tree classifier implementing logistic regression as a feature selection and classification method and its comparison with maximum likelihood. In: 2007 IEEE International Geoscience and Remote Sensing Symposium, pp. 1755–1758. IEEE (2007). https://doi.org/10.1109/IGARSS.2007.4423159

  20. Babavalian, M.R., Moghadam, A.-M.E.: Simple and accurate decision tree based on fuzzy stop criteria approach. In 2013 13th Iranian Conference on Fuzzy Systems (IFSC), pp. 1–4. IEEE (2013). https://doi.org/10.1109/IFSC.2013.6675583

  21. Jinshu, L., Yijiang, W., Ganjun, W., Xiaoasheng, P., Taiwei, L., Yuhang, J.: Gradient boosting decision tree and random forest based partial discharge pattern recognition of HV cable. In: 2018 China International Conference on Electricity Distribution (CICED), pp. 327–331. IEEE (2018). https://doi.org/10.1109/CICED.2018.8592402

  22. McCallum, A., Nigam, K.: A comparison of event models for naive Bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization, vol. 752, no. 1, pp. 41–48, July 1998

    Google Scholar 

  23. Singh, G., Kumar, B., Gaur, L., Tyagi, A.: Comparison between multinomial and bernoulli Naïve Bayes for text classification. In: 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pp. 593–596. IEEE, April 2019

    Google Scholar 

  24. Jyothi, R., Hiwale, S., Bhat, P.V.: Classification of labour contractions using KNN classifier. In 2016 International Conference on Systems in Medicine and Biology (ICSMB), pp. 110–113. IEEE (2016). https://doi.org/10.1109/ICSMB.2016.7915100

  25. Mahmoud, H.A., El Hadad, H.M., Mousa, F.A., Hassanien, A.E.: Cattle classifications system using fuzzy k-nearest neighbor classifier. In: 2015 International Conference on Informatics, Electronics & Vision (ICIEV), pp. 1–5. IEEE 2015. https://doi.org/10.1109/ICIEV.2015.7334010

  26. Sil, R., Roy, A.: A novel approach on argument based legal prediction model using machine learning. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 487–490. IEEE, September 2020

    Google Scholar 

  27. Sil, R., Roy, A., Bhushan, B., Mazumdar, A.K.: Artificial intelligence and machine learning based legal application: the state-of-the-art and future research trends. In: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 57–62. IEEE, October 2019

    Google Scholar 

  28. Saha, D., Sil, R., Roy, A.: A study on implementation of text analytics over legal domain. In: Evolution in Computational Intelligence, pp. 561–571. Springer, Singapore (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Adamas University, Kolkata, 700126, India

    Riya Sil, Dipanjan Saha & Abhishek Roy

Authors
  1. Riya Sil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dipanjan Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhishek Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. National Institute of Information and Communications Technology (NICT), Koganei, Tokyo, Japan

    Hideyasu Sasaki

  3. Universidade Federal da Bahia, Salvador, Brazil

    Ricardo Rios

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. Institute of Technology and Science, Ghaziabad, Uttar Pradesh, India

    Umang Singh

  6. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sil, R., Saha, D., Roy, A. (2021). A Study on Argument-Based Analysis of Legal Model. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2020. Advances in Intelligent Systems and Computing, vol 1372. Springer, Cham. https://doi.org/10.1007/978-3-030-73603-3_42

Download citation

Publish with us

Policies and ethics