Template-Type: ReDIF-Article 1.0
Author-Name:Gul e Rana Iftikhar, Masood Ahmad Arbab, Muhammad Iftikhar Khan, Atif Sardar Khan
Author-Email:maagul1986@gmail.com, arbabmasood@uetpeshawar.edu.pk, miftikhar@uetpeshawar.edu.pk, atifsardarkhan@uetpeshawar.edu.pk
Author-Workplace-Name:Department  of  Computer  Systems  Engineering,  University  of  Engineering  Technology, Peshawar, Pakistan, Department  of  Electrical  Engineering,  University  of  Engineering  Technology,  Peshawar, Pakistan, US–Pakistan  Center  for  Advanced  Studies  in  Energy,  University  of  Engineering  & Technology, Peshawar,25000,Pakistan
Title:Analysis of Social Media Imagery for Crisis Management Applications
Abstract:Social media  data  holds  immense  potential  for  real-time  disaster  response.  This  study explores  leveraging  deep  learning  to  automatically  detect  disaster-related  information across various social media platforms. By analyzing the performance of different models in  identifying  relevant  content,  we  aim  to  reduce  information  gathering  delays  and  support timely rescue efforts. Faster information gathering translates to quick deployment of rescue teams, potentially saving lives and minimizing property damage. We evaluate these models on a benchmark dataset and explore the potential of combining them for even greater accuracy. Among  the  models,  VGG16  achieved  an  accuracy  of  81%  in  identifying  disaster-related content.  Additionally,  exploring  different  fusion  techniques  for  combining  these  models further improved accuracy to 83% with Hybrid Fusion. This research offers valuable insights for future exploration of deep learning techniques in disaster management.
Keywords:CNN, Disasters, Fusion, Social media networks, SVM
Journal:International Journal of Innovations in Science and Technology
Pages:1320-1334
Volume:7
Issue:2
Year:2025
Month:July
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1413/1964
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1413
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:1320-1334