Justice : A Predicting Criminal Acts According To IPC Section

Authors

  • Gaurav Varshney Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
  • Modi Manankumar R Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
  • Rajesh Maheshwari Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
  • Tirth Chhabhaiya Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author
  • Bikram Kumar Department of Computer Science and Engineering, PIET, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT2490215

Keywords:

Machine Learning, Natural Language Processing, Crime Summary Analysis, Criminal Law, Indian Penal Code, Text Classification, Crime Detection, Automated Crime Analysis, Supervised Learning

Abstract

The AI-driven IPC Section Prediction for Crime Classification project is a groundbreaking initiative with far- reaching implications for the legal and law enforcement sectors in India. Traditional crime classification and the assignment of the appropriate IPC section are often time-consuming and prone to human error. Our web application addresses these challenges by offering an efficient, accurate, and user-friendly solution. One of the key strengths of our application lies in its adaptability. It can process a wide range of crime descriptions, including those involving complex legal language or colloquial terms, ensuring its utility in diverse scenarios. Additionally, our system is designed to continuously learn and evolve. It adapts to changes in legal terminology, updates in the IPC, and emerging crime trends, thereby maintaining its relevance and precision over time. The social impact of this project cannot be overstated. By streamlining crime classification, it empowers law enforcement agencies to allocate resources more efficiently and prioritize cases based on severity and relevance. It also aids legal professionals by expediting case preparation and documentation. Moreover, it facilitates greater public engagement with the legal system, enabling citizens to better understand and navigate the complexities of the IPC. In conclusion, our AI-driven IPC Section Prediction web application is a pioneering tool that has the potential to revolutionize crime classification and legal processes. Its adaptability, continuous improvement, and positive societal impact make it an asset for law enforcement, legal practitioners, and the general public alike.

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References

V. DN, K. Vidyashree, A. P. J. TS, K. D. Gupta, and R. Sahana, “Paper on different approaches for crime prediction system.”

J. Azeez and D. J. Aravindhar, “Hybrid approach to crime prediction using deep learning,” pp. 1701–1710, 2015. DOI: https://doi.org/10.1109/ICACCI.2015.7275858

N. Shah, N. Bhagat, and M. Shah, “Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention,” Visual Computing for Industry, Biomedicine, and Art, vol. 4, pp. 1–14, 2021. DOI: https://doi.org/10.1186/s42492-021-00075-z

K. Jha, A. Doshi, P. Patel, and M. Shah, “A comprehensive review on automation in agriculture using artificial intelligence,” Artificial Intelligence in Agriculture, vol. 2, pp. 1–12, 2019. DOI: https://doi.org/10.1016/j.aiia.2019.05.004

K. Jenga, C. Catal, and G. Kar, “Machine learning in crime prediction,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 3, pp. 2887–2913, 2023. DOI: https://doi.org/10.1007/s12652-023-04530-y

A. J. F. ABBAS, “A survey of research into artificial neural networks for crime prediction,” epce ay, vol. 33, 2019.

N. A. K. Rosili, N. H. Zakaria, R. Hassan, S. Kasim, F. Z. C. Rose, and T. Sutikno, “A systematic literature review of machine learning methods in predicting court decisions,” IAES International Journal of Artificial Intelligence, vol. 10, no. 4, p. 1091, 2021. DOI: https://doi.org/10.11591/ijai.v10.i4.pp1091-1102

D. M. Katz, M. J. Bommarito II, and J. Blackman, “Predicting the behavior of the supreme court of the United States: A general approach,” arXiv preprint arXiv:1407.6333, 2014. DOI: https://doi.org/10.2139/ssrn.2463244

M. Matveeva, “Monitoring of law-making: the problem of theoretical justification,” Proc. Russ. Acad. advocacy, vol. 2, pp. 44–47, 2014.

N. Aletras, D. Tsarapatsanis, D. Preo¸tiuc-Pietro, and V. Lampos, “Predicting judicial decisions of the european court of human rights: A natural language processing perspective,” PeerJ computer science, vol. 2, p. e93, 2016. DOI: https://doi.org/10.7717/peerj-cs.93

E. Fersini, E. Messina, D. Toscani, F. Archetti, M. Cislaghi et al., “Semantics and machine learning for building the next generation of judicial court management systems.” in KMIS, 2010, pp. 51–60.

D. M. Katz, M. J. Bommarito, and J. Blackman, “A general approach for predicting the behavior of the supreme court of the United States,” PloS one, vol. 12, no. 4, p. e0174698, 2017. DOI: https://doi.org/10.1371/journal.pone.0174698

T. W. Ruger, P. T. Kim, A. D. Martin, and K. M. Quinn, “The supreme court forecasting project: Legal and political science approaches to predicting supreme court decision-making,” Columbia law review, pp. 1150–1210, 2004. DOI: https://doi.org/10.2307/4099370

A. Zavrsˇnik, “Criminal justice, artificial intelligence systems, and human rights,” vol. 20, no. 4, pp. 567–583, 2020. DOI: https://doi.org/10.1007/s12027-020-00602-0

G. V. Travaini, F. Pacchioni, S. Bellumore, M. Bosia, and F. De Micco, “Machine learning and criminal justice: A systematic review of advanced methodology for recidivism risk prediction,” International journal of environmental research and public health, vol. 19, no. 17, p. 10594, 2022. DOI: https://doi.org/10.3390/ijerph191710594

K. P. Linthicum, K. M. Schafer, and J. D. Ribeiro, “Machine learning in suicide science: Applications and ethics,” Behavioral sciences & the law, vol. 37, no. 3, pp. 214–222, 2019. DOI: https://doi.org/10.1002/bsl.2392

G. Sukanya and J. Priyadarshini, “A meta-analysis of attention models on legal judgment prediction system,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 2, 2021. DOI: https://doi.org/10.14569/IJACSA.2021.0120266

H. Zhong, Z. Guo, C. Tu, C. Xiao, Z. Liu, and M. Sun, “Legal judgment prediction via topological learning,” pp. 3540–3549, 2018. DOI: https://doi.org/10.18653/v1/D18-1390

R. M. Aziz, A. Hussain, P. Sharma, and P. Kumar, “Machine learning- based soft computing regression analysis approach for crime data prediction,” Karbala International Journal of Modern Science, vol. 8, no. 1, pp. 1–19, 2022. DOI: https://doi.org/10.33640/2405-609X.3197

P. Das and A. K. Das, “Application of classification techniques for prediction and analysis of crime in india,” pp. 191–201, 2019. DOI: https://doi.org/10.1007/978-981-10-8055-5_18

A. Bogomolov, B. Lepri, J. Staiano, N. Oliver, F. Pianesi, and A. Pent- land, “Once upon a crime: towards crime prediction from demographics and mobile data,” pp. 427–434, 2014. DOI: https://doi.org/10.1145/2663204.2663254

U. Thongsatapornwatana, “A survey of data mining techniques for analyzing crime patterns,” pp. 123–128, 2016. DOI: https://doi.org/10.1109/ACDT.2016.7437655

H. Adel, M. Salheen, and R. A. Mahmoud, “Crime in relation to urban design. case study: The greater cairo region,” Ain Shams Engineering Journal, vol. 7, no. 3, pp. 925–938, 2016. DOI: https://doi.org/10.1016/j.asej.2015.08.009

J. L. LeBeau, “The methods and measures of centrography and the spatial dynamics of rape,” Journal of quantitative criminology, vol. 3, pp. 125–141, 1987. DOI: https://doi.org/10.1007/BF01064212

R. Iqbal, M. A. A. Murad, A. Mustapha, P. H. S. Panahy, and N. Khanah- madliravi, “An experimental study of classification algorithms for crime prediction,” Indian Journal of Science and Technology, vol. 6, no. 3, pp. 4219–4225, 2013. DOI: https://doi.org/10.17485/ijst/2013/v6i3.6

S. Shiju, M. Devan, and S. S. Gangadharan, “Crime analysis and prediction using data mining,” pp. 406–412, 2014.

S. Yadav, M. Timbadia, A. Yadav, R. Vishwakarma, and N. Yadav, “Crime pattern detection, analysis & prediction,” vol. 1, pp. 225–230, 2017. DOI: https://doi.org/10.1109/ICECA.2017.8203676

K. B. S. Al-Janabi, “A proposed framework for analyzing crime data set using decision tree and simple k-means mining algorithms,” Journal of Kufa for Mathematics and Computer, vol. 1, no. 3, pp. 8–24, 2011. DOI: https://doi.org/10.31642/JoKMC/2018/010302

A. Singh, N. Thakur, and A. Sharma, “A review of supervised machine learning algorithms,” pp. 1310–1315, 2016.

D. Varshitha, K. Vidyashree, P. Aishwarya, T. Janya, D. G. KR, and R. Sahana, “Paper on different approaches for crime prediction system,” International Journal of Engineering Research and Technology (IJERT), 2017.

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Published

12-03-2024

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Section

Research Articles

How to Cite

[1]
G. Varshney, M. Manankumar R, R. Maheshwari, T. C. Chhabhaiya, and B. Kumar, “Justice : A Predicting Criminal Acts According To IPC Section”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 2, pp. 129–139, Mar. 2024, doi: 10.32628/CSEIT2490215.

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