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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68419

Judicial Case Management System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68419

Volume:

10 |

10 |

Oct 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68419

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KBG

Karan B Gaddemalle

Department of Computer Science

Kalpataru Institute of Technology

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PSG

Preetham S Gowda

Department of Computer Science

Kalpataru Institute of Technology

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KV

Kanasu V

Department of Computer Science

Kalpataru Institute of Technology

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BPB

Bhuvana P Bharadwaj

Department of Computer Science

Kalpataru Institute of Technology

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Mentors

SS

Shilpa S

Department of Computer Science

Kalpataru Institute of Technology

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Abstract

Court backlogs delay justice, and the delay weighs most on people held in custody before trial. This paper presents JCMS, a Judicial Case Management System that helps a court registry decide who should be heard first, estimates when a case will end, and tells citizens about their hearings. JCMS combines a transparent fixed-weight priority score (P-Score) that always places undertrial prisoners first, two estimators of case duration with SHAP explanations and calibrated 80% ranges, and a weekly hearing scheduler whose output stays a draft until a registrar approves it, with a written reason for every override. A fairness-quota variant protects a share of slots for the longest-waiting cases. Citizens receive e-mail alerts confirmed by a six-digit one-time code and recorded in a tamper-evident log. All evaluation uses synthetic data and is reported as such. In a 52-week simulation, P-Score scheduling cuts the mean wait of undertrial cases from 168 to 4 days but leaves 48 of 750 old cases unheard; a 10% quota removes that starvation. The duration models beat a median baseline by only 2.7 to 4.0%, because most variation is noise. We discuss limits and the path to real data.

KEYWORDS

judicial case management case prioritization court scheduling explainable prediction fairness undertrial prisoners.

Cite This Article

Karan B Gaddemalle, Preetham S Gowda, Kanasu V, Bhuvana P Bharadwaj, Shilpa S, "Judicial Case Management System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-9, Oct 2026. https://doi.org/10.55041/IJSREM68419

Full Article/Conclusion

JCMS shows that prioritisation, duration estimation, hearing scheduling and citizen notification can be combined in one system that keeps a human in charge and states its uncertainty. On synthetic data, a fixed-weight P-Score reduces the wait of undertrial cases from 168 to 4 days, and the plain greedy scheduler starves 48 of 750 old cases; a protected 10% quota removes that starvation without changing the formula. The duration models are only slightly better than a baseline, which the noise ceiling explains. Future work is to validate the method on real data with court partners, to test an age term based on time since the last hearing, to let courts choose the protected share per week, and to complete the language review and screen-reader testing.

References

[1]               National Judicial Data Grid, eCourts Services, Department of Justice, Government of India. [Online]. Available: https://njdg.ecourts.gov.in/ (accessed Oct. 27, 2026). [2]               "Jammu Kashmir High Court pendency rises to 44,551 cases; subordinate courts cross 3.65 lakh, Centre tells Lok Sabha," Kashmir Life, Jul. 2026. [Online]. Available: https://kashmirlife.net/ (accessed Oct. 27, 2026). [3]               National Crime Records Bureau, Prison Statistics India 2022. New Delhi, India: Ministry of Home Affairs, Government of India, 2023. [4]               Law Commission of India, "Arrears and backlog: Creating additional judicial (wo)manpower," Rep. No. 245, Jul. 2014. [5]               J. Angwin, J. Larson, S. Mattu, and L. Kirchner, "Machine bias," ProPublica, May 23, 2016. [6]               N. Aletras, D. Tsarapatsanis, D. Preoţiuc-Pietro, and V. Lampos, "Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective," PeerJ Comput. Sci., vol. 2, p. e93, 2016. [7]               D. M. Katz, M. J. Bommarito II, 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. [8]               S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Proc. Adv. Neural Inf. Process. Syst. (NIPS), vol. 30, 2017, pp. 4765–4774. [9]               S. M. Lundberg et al., "From local explanations to global understanding with explainable AI for trees," Nat. Mach. Intell., vol. 2, no. 1, pp. 56–67, Jan. 2020. [10]             G. Ke et al., "LightGBM: A highly efficient gradient boosting decision tree," in Proc. Adv. Neural Inf. Process. Syst. (NIPS), vol. 30, 2017, pp. 3146–3154.

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Publication Date

10/28/2026

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Double-Blind Peer Review

Review Rounds: 2

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