SafeguardNet: Enhancing Corporate Safety via Tailored Deep Transfer Learning for Threat Recognition-Reference-Cited by-同舟云学术

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SafeguardNet: Enhancing Corporate Safety via Tailored Deep Transfer Learning for Threat Recognition

Published:2024 Issue: Volume:12 Page:113502-113517
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jahan Nusrat¹,Chowdhury Mohammad Sayem¹^ORCID,Sultan Tofayet¹,Mridha M. F.¹^ORCID,Mukta Md Saddam Hossain²^ORCID

Affiliation:

1. Computer Science and Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh

2. LUT School of Engineering Sciences, LUT University, Lappeenranta, Finland

Funder

FinELib

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10637407.pdf?arnumber=10637407

Reference45 articles.

1. Fire Safety Risk Assessment of Workplace Facilities: A Case Study

2. Cyber-physical systems security: Limitations, issues and future trends

3. A Framework for Data-Driven Physical Security and Insider Threat Detection

4. Automatic Forest Fire Detection Based on a Machine Learning and Image Analysis Pipeline

5. Automated Detection of Firearms and Knives in a CCTV Image

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