Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems-Reference-Cited by-同舟云学术

Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

Published:2023 Issue: Volume:11 Page:95104-95113
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Aldehim Ghadah¹,Asiri Mashael M²,Aljebreen Mohammed³,Mohamed Abdullah⁴,Assiri Mohammed⁵^ORCID,Ibrahim Sara Saadeldeen⁶

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

2. Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi Arabia

3. Department of Computer Science, Community College, King Saud University, Riyadh, Saudi Arabia

4. Research Centre, Future University in Egypt, New Cairo, Egypt

5. Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, Saudi Arabia

6. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

Funder

Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project

Princess Nourah bint Abdulrahman University Researchers

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

King Saud University, Riyadh, Saudi Arabia

Prince Sattam bin Abdulaziz University

Future University in Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10235967.pdf?arnumber=10235967

Reference36 articles.

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2. Violence detection in video using computer vision techniques;nievas;Proc Int Conf Comput Anal Images Patterns,2011

3. A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras

4. RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting

5. Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance

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