Article Per Year (5 Year)
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This Author published in this journals
All Journal Journal of Management and Informatics Journal of Technology Informatics and Engineering
Noorul Hassan S.
Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India
Computer Science & IT Decision Sciences, Operations Research & Management

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Blockchain-Based Supply Chain Transparency for Agricultural Produce Noorul Hassan S.; Sivaranjani S.; Sudha S.; Jaya Shree K.; Rajeswari R.
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.329

Abstract

Food safety and economic growth depend on agricultural supply chains, yet these systems frequently struggle with fraud, inefficiency, and a lack of transparency.  Because traditional centralized systems struggle with traceability and stakeholder trust, it is challenging to ensure the quality and authenticity of produce.  Although modern technologies like cloud platforms and the Internet of Things (IoT) offer partial solutions, they remain susceptible to data manipulation and interoperability issues.  Blockchain offers a potential alternative for improving efficiency, accountability, and trust due to its decentralized and tamper-resistant nature. Creating a blockchain-based framework to improve transparency in agricultural product supply chains is the main goal of this project. This framework enables real-time tracking of products from farm to consumer, ensuring data integrity at every stage. It enhances coordination among farmers, distributors, retailers, and regulators through secure and transparent information sharing. Ultimately, the proposed system aims to reduce fraud, minimize losses, and strengthen consumer confidence in agricultural products.
Self-Supervised Representation Learning for Criminology: Detecting Anomalies, Classifying Reports, and Mapping Networks Noorul Hassan S.; Sivalakshmi S.; Janani M.; Fouziya A.; Thirisha S.
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.473

Abstract

Crime analysis using various types of data, such as video surveillance, crime reports, and criminal networks, has been widely investigated in digital criminology. Most of the available data are unlabelled. In this work, we introduce a self-supervised learning framework for multimodal criminology, which enables the fully automatic learning of effective features for unlabelled video, text, and graph datasets and the completion of crime analysis tasks, including anomaly detection, crime report classification, and high-risk node prediction via contrastive learning, masked prediction, and graph self-supervised learning. The experimental results show that our SSL model learns high-quality features and achieves better performance than its supervised counterpart and baseline models. Unlike traditional deep learning-based models that require large amounts of labeled data, our proposed SSL model is label-efficient, scalable, and robust to artificial or anonymous data. Our work aims to develop an AI-based multimodal self-supervised learning approach for efficient, accurate, reliable, and safe crime analysis
Co-Authors Fouziya A. Janani M. Jaya Shree K. Rajeswari R. Sivalakshmi S. Sivaranjani S. Sudha S. Thirisha S.