TARU PUBLICATIONS
 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Volume 29·Issue 5·May 2026·7 Articles

Volume 29, Issue 5

Articles

Open Access Research Article·pp. 443–453·Vol. 29, Issue 5May 2026

Statistical analysis in AI and IoT integration for real-time quality control in Industry 4.0

V. Nirmala, R. Reka

Published Online: 27 Apr 2026DOI: 10.47974/JSMS-1522

In this paper: As industries evolve toward digital transformation, the integration of traditional quality management tools with emerging technologies becomes imperative. This paper presents a novel framework that sy...

AbstractReferencesFull Text PDF (389 KB)Views: 74Downloads: 22Citations: 0
Open Access Research Article·pp. 455–461·Vol. 29, Issue 5May 2026

Predicting a random determinant of order n filled with independently and identically distributed (i.i.d) exponential variates

Shashi Kant Agrawal, Pinky Pandey, Soubhik Chakraborty

Published Online: 16 Feb 2026DOI: 10.47974/JSMS-1531

In this paper: The paper derives the fiducial limits for a determinant D of order 4, filled with independently and identically distributed (i.i.d) Exponential variates, using Chebyshev’s inequality and then, compari...

AbstractReferencesFull Text PDF (367 KB)Views: 97Downloads: 26Citations: 0
Open Access Research Article·pp. 463–486·Vol. 29, Issue 5May 2026

Assessment of renewable energy management systems using AI and deep learning techniques : A comprehensive review

Mohini Reddy, Vikas Khare

Published Online: 27 Apr 2026DOI: 10.47974/JSMS-1549

In this paper: The global transition toward sustainable energy systems is accelerating due to increasing concerns about climate change, carbon emissions and energy security. Renewable energy sources such as solar an...

AbstractReferencesFull Text PDF (650 KB)Views: 106Downloads: 31Citations: 0
Open Access Research Article·pp. 487–515·Vol. 29, Issue 5May 2026

A Bivariate mixture of student’s t and normal distribution : Properties and estimation

G. S. David Sam Jayakumar, W. Samuel, Bejoy John Thomas

Published Online: 13 Mar 2026DOI: 10.47974/JSMS-1554

In this paper: This paper investigates a Bivariate mixture of the student’s t and Normal distributions, referred to as the t-Normal distribution, where the marginals are univariate Student’s t and Normal distributio...

AbstractReferencesFull Text PDF (1.1 MB)Views: 92Downloads: 37Citations: 0
Open Access Research Article·pp. 517–527·Vol. 29, Issue 5May 2026

AI-driven traffic signal system with YOLO for dynamic congestion control

Ravi Saharan, Omkar Ji Saini, Ravi Raj Choudhary

Published Online: 27 Apr 2026DOI: 10.47974/JSMS-1557

In this paper: This project develops an AI-powered Smart Traffic Light System utilizing YOLOv8 (You Only Look Once) for real-time vehicle detection to improve and better traffic management. The system dynamically ad...

AbstractReferencesFull Text PDF (710 KB)Views: 113Downloads: 55Citations: 0
Open Access Research Article·pp. 529–552·Vol. 29, Issue 5May 2026

Hand gesture recognition using hybrid deep learning models for deaf communication support

Gautam Govind, Dhruv Gupta, Kavita Jhajharia

Published Online: 27 Apr 2026DOI: 10.47974/JSMS-1584

In this paper: Hand gesture recognition has become an indispensable part of human-computer interaction. It supports intuitive, contactless, and accessible means of communication. This work proposes a robust deep lea...

AbstractReferencesFull Text PDF (1.4 MB)Views: 139Downloads: 35Citations: 0
Open Access Research Article·pp. 553–572·Vol. 29, Issue 5May 2026

Comparison of machine learning classification algorithms under different data distributions

Ceyda Murat, Esra Gökpınar

Published Online: 20 Mar 2026DOI: 10.47974/JSMS-1607

In this paper: In this study, we compare the performance of various machine learning algorithms through simulations conducted on datasets with different distributional characteristics. Widely used models such as Log...

AbstractReferencesFull Text PDF (266 KB)Views: 73Downloads: 22Citations: 0