Depth Examination of Deep Learning Applications in Crowd Analysis

Authors

  • Eman Mohamed Elatrash Department of Computer Science, Faculty of Information Technology, Alasmarya Islamic University, Ashiekh District, Libya
  • Hanan Mohammed Esmail Department of Computer Science, Faculty of Education, Aljufra University, Waddan Jufra, Libya
  • Salma Ali Alajeli Infeesh Department of Computer Technology, Faculty of Information Technology, Zawia University, Libya
  • Zayed Alarabi Khalifa Department of Computer Technology, Faculty of Information Technology, Zawia University, Libya
  • Nashwa E. Zaqout Department of Data Analysis, Faculty of Economic, Zawia University, Libya

Keywords:

Deep Learning; Artificial Neural Networks; Convolutional Neural Networks; Recurrent Neural Networks; Crowd analysis.

Abstract

Artificial neural networks and machine learning have a long history of tackling diverse problems. However, as problems grew in complexity and datasets expanded, computational demands increased significantly. This paper provides a concise overview of the evolution from artificial neural networks to deep learning, highlighting models, applications, and essential technical details, including hardware, software, and libraries. It particularly focuses on conventional methods employed in crowd analysis and delves deeply into the applications of deep learning in this field, accompanied by an examination of relevant datasets. Additionally, recent studies in crowd analysis are meticulously analyzed and compared. In conclusion, crowd analysis stands as both an academic pursuit and a practical domain where deep learning has led to remarkable achievements.

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Published

2024-12-30

How to Cite

Elatrash, E. M. ., Esmail, H. M. ., Infeesh, S. A. A. ., Khalifa, Z. A. ., & Zaqout, N. E. . (2024). Depth Examination of Deep Learning Applications in Crowd Analysis. Journal of Information Systems Research and Practice, 2(5), 2–15. Retrieved from https://adab.um.edu.my/index.php/JISRP/article/view/57836

Issue

Vol. 2 No. 5 (2024): SUPPLEMENTARY ISSUE: Soft Computing and Data Mining (SCDM)

Section

Articles

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