Hybridized Technique to Analyze Workstress Related Data via the StressCafé
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Hybridized Technique to Analyze Workstress Related Data via the StressCafé

Authors: Anusua Ghosh, Andrew Nafalski, Jeffery Tweedale, Maureen Dollard

Abstract:

This paper presents anapproach of hybridizing two or more artificial intelligence (AI) techniques which arebeing used to fuzzify the workstress level ranking and categorize the rating accordingly. The use of two or more techniques (hybrid approach) has been considered in this case, as combining different techniques may lead to neutralizing each other-s weaknesses generating a superior hybrid solution. Recent researches have shown that there is a need for a more valid and reliable tools, for assessing work stress. Thus artificial intelligence techniques have been applied in this instance to provide a solution to a psychological application. An overview about the novel and autonomous interactive model for analysing work-stress that has been developedusing multi-agent systems is also presented in this paper. The establishment of the intelligent multi-agent decision analyser (IMADA) using hybridized technique of neural networks and fuzzy logic within the multi-agent based framework is also described.

Keywords: Fuzzy logic, intelligent agent, multi-agent systems, neural network, workplace stress.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334736

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3915

References:


[1] R. A. Karasek, "An analysis of 19 international case studies of stress prevention through work reorganization using the demand/control model", Bulletin of Science and Technology, 2004, 24, pp. 446-456.
[2] A. Ghosh, A. Nafalski, M. Dollard, J. Tweedale, W.F. Mahmudy, "An autonomous system for psychological risk assessment using the Australian workplace barometer", The 3rd Asia pacific Expert Workshop on Psychological Factor at Work, 2-3 August 2012, Tokyo, Japan.
[3] A. Ghosh, J. Tweedale, A. Nafalski, M. Dollard, "Multi-agent based system for analysing stress using the Stresscafe". Advances in Knowledge-Based and Intelligent Information and Engineering Systems, 2012, Amsterdam, vol 243, pp.1656-1665.
[4] M. F. Dollard, A. Taylor, et.al. "Cohort profile: The Australian workplace barometer (AWB)", 2010.
[5] StressCafé®: Workplace stress. Retrieved July 14, 2010, from http://www.stresscafe.edu.au.
[6] S. J. Russell, P. Norvig, Artificial Intelligence: A modern approach, 3rded, 2010, Upper Saddle River, NJ, Prentice Hall.
[7] M. Wooldridge, N. Jennings, Intelligent agents: Theory and practice, Knowledge Engineering Review, Prentice Hall, Inc., 3rd edition, 199510(2), 115-152.
[8] D. Rumelhart, G. Hilton, R Williams, "Learning representations by backpropogation errors", Nature, 1986, vol. 323, pp. 533-536.
[9] W. Pedrycz, F. Gomide, Toward human centric computing, Fuzzy System Engineering, 2007, IEEE Press.
[10] Z. Zang, C. Zang, Agent-based hybrid intelligent systems, LANI 2938, ┬® Springer-Verlag Berlin Heidelberg 2004,pp. 3-11.
[11] L. A, Zadeh, "Fuzzy sets", Inform and Control, 1965, vol, 8, pp. 338-353.
[12] D.W.Patterson, "Artificial neural networks theory and applications", Prentice Hall International, 1996, pp. 247-264.
[13] P.Werbos, "Beyond regression: new tools for prediction and analysis in thr behavioral sciences," PhD. Dissertation, 1974, Harvard University.
[14] D.Parker, Learning logic, Invention Report, S81-64, File 1, Office of Technology Licensing, 1982, Stanford University.