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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61176
Volume:
10 |
4 |
April 2026
Pages:
1-5
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.
Department of IT
SVIT
Department of IT
SVIT
Department of IT
SVIT
Department of IT
SVIT
Department of IT
SVIT
In recent times, maintaining safety and academic integrity within educational institutions has become a critical challenge. While CCTV cameras are extensively deployed across campuses and examination halls, they rely heavily on manual monitoring, which is inefficient, prone to human error, and causes delayed responses to critical events. This paper proposes a novel, multi-scenario automated surveillance framework based on advanced deep learning and computer vision techniques. The proposed system is designed to operate in two distinct modes: a Campus Safety mode and an Academic Integrity mode. For violence detection, the architecture integrates YOLOv7 for rapid human detection with a MobileNet-BiLSTM classifier to accurately recognize violent actions. For examination monitoring, the system utilizes Deep Keyframe Detection combined with a Multilayer Perceptron (MLP) enhanced YOLOv8 algorithm (SE-YOLOv8) and a ResNet-based 3D CNN to identify subtle cheating behaviors like passing notes or unauthorized looking. By unifying these dual pipelines into a single deployable framework, this study outlines a scalable solution that minimizes manual invigilation, enhances real-time threat detection, and ensures a secure, fair educational environment.
KEYWORDS
AssessArc demonstrates that essential VAPT functionality can be integrated into an Android application with acceptable performance and strong safety controls. The implemented modules (hash identifier, hash cracker, directory enumeration, ZIP cracker, and brute-force module) provide a practical foundation for portable, authorized security assessments.
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Publication Date
4/27/2026
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.
Funding Information
No funding information available for this research.
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