Abstract
This chapter presents an extension of the existing research on autonomously analysing and grading work-related stress. The use of Genetic Algorithm (GA) are being assessed to minimize the stressors both physical and psychological that gives rise to work-related stress or workplace stress, for their ability to produce new solutions by combining existing solutions based on a fitness value. This effort is aimed at enabling employers to manipulate the stressors and prevent stress within the workplace with respect to changes in workload. Workplace stress can be considered as an event that affects people from all professions in some form or the other. This is of widespread concern and requires intervention as a preventive measure that aims at reducing work stress. Intelligent Multi-Agent Decision Analyser (IMADA) is a multi-agent-based hybridized autonomous model, that can analyse work-related stress data. The model was designed to process data automatically optimizing productivity and predictability. The interactive model provides the user with an automated mechanism that can provide a first step towards the identification and prevention of work stress, and making perceptible changes to the working environment.
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References
Ghosh, A., Nafalski, A., Tweedale, J., Dollard, M.: Hybridized technique to analyse work-stress related data via the stresscafè. In: ICCESSE 2012: International Conference on Computer, Electrical, and Systems Sciences, and Engineering, October 24-25, vol. 70, pp. 1618–1622 (2012)
Ghosh, A., Nafalski, A., Tweedale, J., Dollard, M.: Using hybridized techniques to develop an online workplace risk assessment tool. Journal of Informatics Control Measurement in Economy and Environment Protection 4b, 42–45 (2012)
Ghosh, A., Tweedale, J.W., Nafalski, A., Dollard, M.: Multi-agent based system for analysing stress using the stresscafè. Advances in Knowledge-Based and Intelligent Information and Engineering Systems 243, 1656–1665 (2012)
Dollard, M.F., Taylor, A.: Cohort profile: The australian workplace barometer. Technical report, University of South Australia Internal Report (2010)
McCarthy, J.: Program with common sense. In: Mechanization of Thought Processes. Proceedings of the Symposium of the National Physics Laboratory, London, U.K, pp. 77–84 (1959)
McCarthy, J.: From here to human-level AI. Artificial Intelligence 171(18), 1174–1182 (2007)
Nilsson, N.J.: The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press, Cambridge (2010)
Bruner, J.S., Goodnow, J.J., Austin, G.A.: A study of thinking. Wiley, New York (1956)
Russell, S.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, New Jersey (2010)
Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)
Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems Journal 1(1), 7–38 (1998)
Jain, L.C., Jain, R.K. (eds.): Hybrid Intelligent Engineering Systems. World Scientific, Singapore (2009)
Jain, L.C., Chen, Z., Ichalkranje, N.: Intelligent Agents and their Applications. Springer, Germany (2002)
Resconi, G., Jain, L.C.: Intelligent Agents: Theory and Applications. STUDFUZZ, vol. 155. Springer, Germany (2005)
Khosla, R., Ichalkaranje, N., Jain, L.C.: Design of Intelligent Multi-Agent Systems. Springer, Germany (2005)
Rumelhart, D., Hilton, G., Williams, R.: Learning representations by backpropagating errors, Parallel Distributed Processing. MIT Press, Cambridge (1986)
Rojas, R.: Neural networks: a systematic introduction. Springer (1996)
Patterson, D.W.: Artificial neural networks: theory and applications. Prentice Hall PTR (1998)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Jang, J.R., Sun, C., Mizutani, E.: Neuro-fuzzy and soft computing – a computational approach to learning and machine intelligence [book review]. IEEE Transactions on Automatic Control 42(10), 1482–1484 (1997)
LaMontagne, A.D.: Workplace Stress in Victoria: Developing a Systems Approach: Report to the Victorian Health Promotion Foundation. Victorian Health Promotion Foundation (May 2006)
Chen, P., Spector, P.: Relationship of work stressors with aggression, withdrawal, theft, and substance abuse: An exploratory study. Journal of Occupational and Organizational Psychology 65, 177–184 (1992)
Hall, G.B., Dollard, M.F., Coward, J.: Psychosocial safety climate: Development of the psc-12. International Journal of Stress Management 17(4), 353–383 (2007)
NOHSC: Compendium of workers’ compensation statistics, australia (2003)
Rechenberg, I.: Evolution strategy. Computational Intelligence: Imitating Life, vol. 1. IEEE Press, Piscataway (1994)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press (1975)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press (1992)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, vol. 412. Addison-Wesley, Reading (1989)
Forrest, S.: Genetic algorithms: Principles of natural selection applied to computation. Science, American Association for the Advancement of Science 261(5123), 872–878 (1993)
Holland, J.H.: Genetic agorithms. Scientific American 267(1), 66–72 (1992)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Karasek, R.A.: An analysis of 19 international case studies of stress prevention through work reorganization using the demand/control model. Bulletin of Science and Technology 24, 446–456 (2004)
Kahn, R.L., Byosiere, P.: Stress in organizations. In: Dunnette, M., Hough, D., Leaetta, M. (eds.) Handbook of Industrial and Organizational Psychology, vol. 3(2), pp. 571–650. Consulting Psychologists, Palo Alto (1992)
Cooper, C.L., Cartwright, S.: Healthy mind; healthy organization – a proactive approach to occupational stress. Human Relations 47(4), 455–471 (1994)
Karasek, R.A., Theorell, T.: Healthy work: stress, productivity, and the reconstruction of working life. Basic Books (1992)
Cooper, C.L.: Job distress: Recent research and the emerging role of the clinical occupational psychologist. Bulletin of the British Psychological Society, British Psychological Society (1986)
Farrington, A.: Stress and nursing. British Journal of Nursing 4(10), 574 (1995)
Quine, L.: Effects of stress in an nhs trust: a study. Nursing Standard (Royal College of Nursing)Â 13(3), 36 (1998)
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Ghosh, A., Tweedale, J.W., Nafalski, A. (2015). Optimizing Stressors Using Genetic Algorithm to Minimize Work-Related Stress. In: Tweedale, J., Jain, L., Watada, J., Howlett, R. (eds) Knowledge-Based Information Systems in Practice. Smart Innovation, Systems and Technologies, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-13545-8_17
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DOI: https://doi.org/10.1007/978-3-319-13545-8_17
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