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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM51671
Volume:
9 |
7 |
Jul 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
Abstarct: Elephant Detection System Using AI is a comprehensive, multi-stakeholder platform designed to detect and manage wild elephant intrusions using camera-based surveillance. Utilizing advanced detection algorithms and real-time image recognition, the system identifies elephants in human-inhabited or protected areas, triggering automatic alerts and notifications across various sectors such as Forestry, Krishibhavan, Hospitals, Insurance agencies, and Farmers. This platform integrates user roles including Administrator, Forestry, Hospital, Krishibhavan, Insurance, Farmer, and General Public to efficiently manage alerts, complaints, claims, and communications arising from elephant-related incidents. The system not only facilitates early warning and prevention mechanisms through cameras and alarms but also ensures a streamlined post-incident workflow involving reporting, insurance claim processing, and support services. To ensure high detection accuracy in dynamic environments, the system uses feature extraction techniques based on shape, color, and texture, and is trained with datasets curated for real-world elephant images captured under various lighting and background conditions. This increases the model’s reliability and reduces false positives, even in cluttered scenes or nighttime footage. Additionally, the system includes modules for report generation, feedback management, and public awareness, making it not just a technological solution but also a platform for community engagement. The alerts are delivered via SMS, web notifications, or alarm systems, allowing residents and forest officers to respond proactively. This improves safety, reduces crop and property damage, and supports conservation efforts by enabling non-invasive monitoring.
KEYWORDS
Publication Date
7/31/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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