Scopus Indexed Publications

Paper Details


Title
A Proficient Deep Learning Approach to Classify the Usual Military Signs by CNN with Own Dataset
Author
Md. Ekram Hossain, Ashraful Hossen Thusar, Md. Musa, Md. Sanzidul Islam, Nahid Kawsar Nisat, Zaman Hossain,
Email
ekram35-1936@diu.edu.bd
Abstract

Everyday, around the world crimes, like kidnapping or forced to do something to enemy’s command, are happening. General people are being the main victim in most cases. Hostage people are usually rescued by military or special force sometimes. The best way to build communication between hostage and military is by using the basic sign language of that military or special force. In this research, we analyzed 2400 images for 24 different basic signs what they use in their real mission. For this analysis, we classified their basic signs by convolutional neural network (CNN) algorithm. This research will help general people to take decision on hostage circumstances so that they can easily communicate with the military who have gone there to rescue them. We used multiple convolutional layers and get 92.50% accuracy. Using our model in any real system, a novice member of the military or special force can learn and validate his sign.

Keywords
Convolutional Neural Network (CNN) Deep learning Military sign Image classification
Journal or Conference Name
Soft Computing Techniques and Applications. Advances in Intelligent Systems and Computing
Publication Year
2021
Indexing
scopus