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All Journal Bulletin of Electrical Engineering and Informatics Realism: Law Review
Shah, Rahul
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Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice

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REGULATIONS AND ETHICS OF REPORTING ON PERSONS WITH DISABILITIES IN THE MEDIA: A COMPARATIVE STUDY OF INDONESIA AND INDIA Setiana, Winda Ayu; Maifitri, Hafifah Maifitri; ’Aisyi, Tia Rahadatul; Tiara, Ayudya Soca; Shah, Rahul
Realism: Law Review Vol. 3 No. 1 (2025): Realism: Law Review
Publisher : Sabtida

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71250/rlr.v3i1.59

Abstract

Freedom of the press is a fundamental right in a democratic system guaranteed by Law Number 40 of 1999 concerning the Press. However, in some cases, freedom of the press can conflict with the rights of vulnerable groups, such as people with disabilities. This study focuses on a normative analysis related to the dilemma between freedom of the press and protection of the rights of people with disabilities, by referring to the laws and regulations in force in Indonesia. The results of the study indicate that although freedom of the press is guaranteed, there are regulations that govern the ethics of reporting so as not to violate the rights of people with disabilities, as regulated in Law Number 8 of 2016 and the Guidelines for Disability-Friendly Reporting from the Press Council. This study emphasizes the importance of a balance between freedom of the press and protection of the rights of people with disabilities in journalistic practice
Multi-feature fusion framework for enhanced image deduplication accuracy using adaptive deep learning Shah, Rahul; Kumar Shrivastava, Ashok
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9119

Abstract

Image deduplication is a critical task in domains such as digital asset management, content-based image retrieval (CBIR), and data storage optimization. This paper presents a novel method for improving deduplication accuracy by integrating multiple feature types. A comprehensive framework is proposed that combines visual, semantic, and structural image elements. The system employs deep learning architectures, including convolutional neural networks (CNNs) and transformers, to extract high-level features, which are fused through an adaptive weighting mechanism that dynamically adjusts based on image content. Experimental results across diverse datasets demonstrate that the proposed multi-feature fusion approach significantly outperforms traditional single-feature methods, achieving an average improvement of 15% in deduplication accuracy. By overcoming limitations in handling complex visual similarities, this study introduces a more robust and efficient solution for image deduplication.
Co-Authors Kumar Shrivastava, Ashok Maifitri, Hafifah Maifitri Setiana, Winda Ayu Tiara, Ayudya Soca ’Aisyi, Tia Rahadatul