Optimization The Utility Of E-Learning Platform Through Integrating Smart Emotional Recognition Feature
-
Published:2022-12-01
Issue:2
Volume:1
Page:6
-
ISSN:2617-5517
-
Container-title:Jornual of AL-Farabi for Engineering Sciences
-
language:
-
Short-container-title:JFES
Author:
Layth Mohammed Abbas Al- Mashhadani ,Isam Abdulmunem Alobaidi ,Alaa Mohammed Abbas Al- Mashhadani
Abstract
Educational applications of image processing have emerged due to data collection tools development. Education is vital field in human life where highly accurate performance is required. Integrating image processing and deep learning with the education will help to optimize the performance of entire system. It is possible now to make out the student’s emotional status through study the features from facial images taken for a group of students. That reduces the time and cost of the education by providing a facility similar to the regular classrooms environments. Which may help plenty of people who are unable to access regular educational facilities due to intolerable cost. In this paper, automatic emotional detection is being performed using neural network. Two models are used namely artificial neural network and CNN neural network. The models are tested using emotional images data. Results are reported 96.7 % and 99.2 % accuracies from bother artificial neural network and CNN respectively.
Publisher
Al-Farabi University College
Reference12 articles.
1. Fitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., & Dicker, D. J. (2017). A systematic analysis for the global burden of disease study. JAMA Oncol, 3(4), 524-548. 2. Fletcher, C. D., Unni, K., & Mertens, F. (2002). World Health Organization classification of tumours. Pathology and genetics of tumours of soft tissue and bone. IARC press. 3. Motlagh, M. H., Jannesari, M., Aboulkheyr, H., Khosravi, P., Elemento, O., Totonchi, M., & Hajirasouliha, I. (2018). Breast cancer histopathological image classification: A deep learning approach. BioRxiv, 242818. 4. Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009). Histopathological image analysis: A review. IEEE reviews in biomedical engineering, 2, 147-171. 5. Rahman, A., Lee, J., & Choi, K. (2016, March). Efficient FPGA acceleration of convolutional neural networks using logical-3D compute array. In 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 1393-1398). IEEE.
|
|