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Type :final_year_project
Subject :QA Mathematics
Main Author :Nor Lailatul Akasyah Zoldi
Title :Sistem pengesanan emosi pada masa nyata (SPEMN)
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
Pengesanan emosi dapat digunakan dalam pelbagai jenis aplikasi, dan ia juga wajar digunakan dalam sistem E-pembelajaran yang dapat memberikan faedah yang terbaik. Kajian ini  dijalankan bertujuan untuk mengenal pasti emosi pelajar semasa proses pembelajaran dalam talian  (E-learning). Kajian ini menumpukan kepada tiga objektif iaitu mengenalpasti elemen-elemen yang  diperlukan dalam sistem pengesanan  emosi pada masa nyata dan membangunkan sistem pengesanan emosi  yang dapat mengesan ekspresi wajah pelajar pada masa nyata. Selain itu, sistem ini akan menilai  keberkesanan sistem web yang dihasilkan menggunakan matrik penilaian accuracy, presicion dan  recall. Pembangunan sistem ini menggunakan model Rapid Application Development (RAD). Seramai 10  orang pelajar dari Fakulti Seni, Komputeran & Industri Kreatif, Universiti Pendidikan Sultan Idris  (UPSI) telah dipilih secara rawak dan diuji bagi mendapatkan ketepatan sistem tersebut. Hasil  menunjukkan sistem ini mampu mengesan emosi dengan nilai accuracy sebanyak 0.55072, precision  sebanyak 0.88583, dan recall sebanyak  0.547.  Oleh  itu,  sistem  ini  diharap  dapat  membantu   meningkatkan  proses pembelajaran atas talian dengan lebih baik.  

References

Ahmad Banafa (2016, June). What is Effective Computing. Retrieved from OpenMind:

https://www.bbvaopenmind.com/en/technology/digital-world/what-is-affective-co

mputing/

 

Al-Awni, A. (2016). Mood extraction using facial features to improve learning curves of

students in elearning systems. International Journal of Advanced Computer

Science and Applications, 7(11), 444-453.

 

Altuwairqi, K., Jarraya, S. K., Allinjawi, A., & Hammami, M. (2018). A new emotion–

based

affective model to detect student’s engagement. Journal of King Saud

University-Computer and Information Sciences.

 

Ayvaz, U., Gürüler, H., & Devrim, M.O. (2017). Use of Facial Emotion Recognition in e

Learning Systems.

 

Bahreini, K., Nadolski, R., & Westera, W. (2016). Towards real-time speech emotion

recognition for affective e-learning. Education and information

technologies, 21(5), 1367-1386.

 

Basu, S., Chakraborty, J., Bag, A., & Aftabuddin, M. (2017, March). A review on

emotion

recognition using speech. In 2017 International Conference on Inventive

Communication and Computational Technologies (ICICCT) (pp. 109-114). IEEE.

 

Chunyan Ma (2016). Design of an emotional interaction mode in e-learning. World

Transactions on Engineering and Technology Education Vol.14, No.1, 2016

Correa, E., Jonker, A., Ozo, M., & Stolk, R., (2016). Emotion Recognition using Deep

Convolutional Neural Networks.

 

Deep Learning Application (2019). Advancing Human Potential. Retrieved from NVISO:

https://nviso.ai/en

 

Deepface Portal (2019). Understanding Human Faces. Retrieved from DeepFace;https://deepface.ir/

 

Dillon, J., Bosch, N., Chetlur, M., Wanigasekara, N., Ambrose, G. A., Sengupta, B., &

D'Mello, S. K. (2016). Student Emotion, Co-Occurrence, and Dropout in a

MOOC Context. International Educational Data Mining Society.

 

Duncun, D., Shine, G., & English, C. (2016). Facial emotion recognition in real time.

Standford University.

Ewais, A., & Samra, D. A. (2017, October). Adaptive MOOCs: A framework for

adaptive

course based on intended learning outcomes. In 2017 2nd International

Conference on Knowledge Engineering and Applications (ICKEA) (pp. 204-209).

IEEE.

 

Fernández-Caballero, A., Martínez-Rodrigo, A., Pastor, J. M., Castillo, J. C., Lozano

Monasor, E., López, M. T.,& Fernández-Sotos, A. (2016). Smart environment

architecture for emotion detection and regulation. Journal of biomedical

informatics, 64, 55-73.

 

Garcia-Garcia, J. M., Penichet, V. M., & Lozano, M. D. (2017, September). Emotion

detection: a technology review. In Proceedings of the XVIII International

Conference

on Human Computer Interaction (p. 8). ACM.

 

Gil, R., Virgili-Gomá, J., García, R., & Mason, C. (2015). Emotions ontology for

collaborative modelling and learning of emotional responses. Computers in

Human Behavior, 51, 610-617.

 

Happy, S. L., Dasgupta, A., Patnaik, P., & Routray, A. (2013, December). Automated a

lertness and emotion detection for empathic feedback during e-Learning. In 2013

IEEE Fifth International Conference on Technology for Education (t4e 2013) (pp.

47

50). IEEE.

 

Ingale, A. B., & Chaudhari, D. S. (2012). Speech emotion recognition. International

Journal

of Soft Computing and Engineering (IJSCE), 2(1), 235-238.

 

Kanimozhi, A., & Cyril, R. (2015). An Enhanced Intelligent Learning Environment for E

Learner Using Cognitive Architecture-Act-R. International Journal of

Engineering and Technology (IJET), 7(1).

 

Khediri, N., Ammar, M. B., & Kherallah, M. Towards an online Emotional Recognition

System for Intelligent Tutoring Environment.

 

Krithika, L. B. (2016). Student emotion recognition system (SERS) for e-learning

improvement based on learner concentration metric. Procedia Computer

Science, 85, 767-776.

 

Kumar, N., Khaund, K., & Hazarika, S. M. (2016). Bispectral analysis of EEG for

emotion

recognition. Procedia Computer Science, 84, 31-35.

 

Lalitha, S., Geyasruti, D., Narayanan, R., & Shravani, M. (2015). Emotion detection

using

MFCC and cepstrum features. Procedia Computer Science, 70, 29-35.

 

Landowska, A., Brodny, G., & Wrobel, M. R. (2017). Limitations of Emotion

Recognition

from Facial Expressions in e-Learning Context. In CSEDU (2) (pp. 383-389).

 

Pathak, A., Pandey, M., & Rautaray, S., (2018). Application of Deep Learning for Object

Detection. Procedia Computer Science 132 (2018) 1706–1717

 

Petrovica, S., Anohina-Naumeca, A., & Ekenel, H. K. (2017). Emotion recognition in

affective tutoring systems: Collection of ground-truth data. Procedia Computer

Science, 104, 437-444.

 

Pitaloka, D. A., Wulandari, A., Basaruddin, T., & Liliana, D. Y. (2017). Enhancing CNN

With preprocessing stage in automatic emotion recognition. Procedia computer

science, 116, 523-529.

 

Soltani, M., Zarzour, H., & Babahenini, M. C. (2018, March). Facial emotion detection in

Massive open online courses. In World Conference on Information Systems and

Technologies(pp. 277-286). Springer, Cham.

 

Tarnowski, P., Kołodziej, M., Majkowski, A., & Rak, R. J. (2017). Emotion recognition

using facial expressions. Procedia Computer Science, 108, 1175-1184.

 

The emotive couch-learning emotions by capacitively sensed. Procedia computer

science, 130, 263 270.

 

Turabzadeh, S., Meng, H., Swash, R., Pleva, M., & Juhar, J. (2018). Facial Expression

Emotion

Detection for Real-Time Embedded Systems. Technologies, 6(1), 17.

 

Tutorialspoints (2019). UML-Activity Diagrams. Retrieved from Tutoriolspoint:

https://www.tutorialspoint.com/uml/uml_activity_diagram.htm

 

Tutorialspoints (2019). UML-Class Diagram. Retrieved from Tutorialspoint:

https://www.tutorialspoint.com/uml/uml_class_diagram.htm

 

Tutorialspoints (2019). UML-Statechart Diagrams. Retrieved from Tutorialspoint:

https://www.tutorialspoint.com/uml/uml_statechart_diagram.htm

 

Vaishnav, S., & Mitra, S. (2016). Speech Emotion Recognition: A Review. International

Research Journal of Engineering and Technology (IRJET), 3(04).

 

Xu, T., Zhou, Y., Wang, Z., & Peng, Y. (2018). Learning Emotions EEG-based

Recognition

and Brain Activity: A Survey Study on BCI for Intelligent Tutoring

 

System. Procedia

computer science, 130, 376-38


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