CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Activity based Mental Stress Detection and Analysis

by V. R. Kavitha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 152 - Number 10
Year of Publication: 2016
Authors: V. R. Kavitha
10.5120/ijca2016911838

V. R. Kavitha . Activity based Mental Stress Detection and Analysis. International Journal of Computer Applications. 152, 10 ( Oct 2016), 33-37. DOI=10.5120/ijca2016911838

@article{ 10.5120/ijca2016911838,
author = { V. R. Kavitha },
title = { Activity based Mental Stress Detection and Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 152 },
number = { 10 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume152/number10/26360-2016911838/ },
doi = { 10.5120/ijca2016911838 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:57:51.720578+05:30
%A V. R. Kavitha
%T Activity based Mental Stress Detection and Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 152
%N 10
%P 33-37
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Educational Data Mining is a new discipline based on the Data Mining baseline and deals with educational data. Monitoring the stress of a person continuously will help to find out the pattern of stress and intern help the physician to take the decisions easily. Current work proposes a process model to explore data from educational settings. The model is used to find out descriptive patterns and predictions that characterize learners’ behaviors and achievements, domain knowledge content, assessments, and educational functionalities and applications.

References
  1. Alaa El-Halees, 2009, Mining Students Data to Analyze E-Learning Behavior: A Case Study.
  2. Ayesha Shaeela, Mustafa Tasleem, Raza Sattar Ahsan and M. Inayat Khan, “Data Mining Model for Higher Education System”, Europen Journal of Scientific Research, Vol.43, No.1, pp.24-29, 2010
  3. Baker, R.S.J.D.,2010, Data Mining for Education. In International Encyclopedia of Education (3rd Edition), B. Mcgaw, Peterson, P., Baker Ed. Elsevier, Oxford, Uk.
  4. Barnes, T. 2005. The Q-Matrix Method: Mining Student Response Data for Knowledge. In Proceedings of the AAAI-2005 Workshop on Educational Data Mining.
  5. Bharadwaj B.K. and Pal S., 2011, “Data Mining: A Prediction for Performance Improvement Using Classification”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 9, No. 4, 136-140.
  6. Carlisle R., Karen M. Fitzpatrick, Jason M. Oreskovich, George T. Fredrick, 2008, “Cardiac Stress Testing for Diagnosis of Coronary Artery Disease in Adults with Acute Chest Pain”, Cardiac Stress Testing, Nov., 2008, pp: 21-29.
  7. Cocea, M., Hershkovitz, A. and Baker, R.S.J.D. 2009. The Impact of Off-Task and Gaming Behaviors on Learning: Immediate or Aggregate? In Proceedings of The 14th International Conference on Artificial Intelligence in Education, 507-514.
  8. Dekker, G., Pechenizkiy, M. and Vleeshouwers, J., 2009, Predicting Students Drop Out: A Case Study, In Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09), 41-50.
  9. Desmarais, M.C. and Pu, X. 2005. A Bayesian Student Model Without Hidden Nodes and Its Comparison with Item Response Theory. International Journal Of Artificial Intelligence In Education 15, 291-323.
  10. Hijazi, Syed Tahir and Naqvi, S.M.M. Raza. (January 2006). ‘Factors Affecting Students’ Performance: ACase of Private Colleges’. Bangladesh e-Journal of Sociology: Volume 3, Number 1.
  11. Jeong, H. and Biswas, G. 2008. Mining Student Behavior Models in Learning-By-Teaching Environments. In Proceedings of the 1st International Conference on Educational Data Mining, 127-136.
  12. Pandey, and Pal, 2011, Data Mining: A Prediction of Performer or Underperformer Using Classification, (IJCSIT) International Journal of Computer Science and Information Technology, Vol. 2(2), 686-690.
  13. Pavlik, P., Cen, H. and Koedinger, K.R. 2009. Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models. In Proceedings of the 2nd International Conference on Educational Data Mining, 121-130.
Index Terms

Computer Science
Information Sciences

Keywords

Mental Stress Student Activity Stress Classifier

Powered by PhDFocusTM