Trapped human victims Machine Learning detection Model Using Recursive Feature Elimination with SVM Classification Algorithm-Reference-Cited by-同舟云学术

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Trapped human victims Machine Learning detection Model Using Recursive Feature Elimination with SVM Classification Algorithm

Published:2024-04-02 Issue: Volume:1 Page:1-7
ISSN:
Container-title:2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)
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Author:

Enoch Jiya Adana¹,Oluwafemi Ilesanmi B.²,Ibikunle Francis A.¹,Paul Olulope K.²,Anwar Nik Syahrim B N³,Folashade Olamide Ariba⁴

Affiliation:

1. Landmark Universuty Omu-Aran,Department of Electrical and Information Engineering,Omu-Aran,Nigeria

2. Ekiti State Universuty Ado Ekiti,Department of Electrical and Elctronic Engineering,Ado Ekiti,Nigeria

3. Univeristy Technical Malaysia,Department of Mechatronics Engineering,Malaka

4. Landmark University,Department. of Electrical and Information Engineering,Omu-Aran,Nigeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10629651/10629654/10630234.pdf?arnumber=10630234

Reference41 articles.

1. The Challenge of Reducing the Incidence of Building Collapse in Ghana: Analyzing the Perspectives of Building Inspectors in Kumasi

2. Survey of Sensors and Techniques in Microwave Imaging for Through-Wall Imaging for Detecting Trapped Humans under Rubble;Jiya;2023 Social Media and Crowdsourcing

3. Building collapse in Nigeria (2009-2019), causes and remedies–a review;Odeyemi;USEP: Journal of Science and Engineering Production,2019

4. UAV-Based Real-Time Survivor Detection System in Post-Disaster Search and Rescue Operations

5. Drones in emergency response – evidence from cross-border, multi-disciplinary usability tests

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