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Since the one-to-one word translator does not have the facility to translate pragmatic aspects of Javanese, the parallel text alignment model described uses a phrase pair combination. The algorithm aligns the parallel text automatically from the beginning to the end of each sentence. Even though the results of the phrase pair combination outperform the previous algorithm, it is still inefficient.Recording all possible combinations consume more space in the database and time consuming. The original algorithm is modified by applying the edit distance coefficient to improve the data-storage efficiency. As a result, the data-storage consumption is 90% reduced as well as its learning period (42s). Refereed/Peer-reviewed

Electromagnetic devices such as superconducting magnets, magnetically levitated vehicles and magnetic resonance imaging (MRI) create high magnetic fields within their immediate vicinity. This has generated some concerns over the effects that these fields may have on the outside environment and in particular on living organisms. 50/60Hz magnetic fields are of special concern because living organisms are exposed to them on our environment. The measurement of low frequency magnetic field on our environment is important to find out a possible relationship between magnetic field and health hazards. The low frequency magnetic field measurements in urban locations are presented. We investigated the influence of a strong 60Hz AC magnetic field of about 1.0T on organisms. The high AC magnetic field is generated by using a multilayer eddy-current type AC magnetic generator developed in our laboratory. In this paper, the generation of heat shock protein by magnetic stimulus is discussed in nematode, and in vitro experiments were performed with biochemical reactions, enzyme and DNA synthesis.

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., {"references":["R. A. Karasek, \"An analysis of 19 international case studies of stress\nprevention through work reorganization using the demand/control model\", Bulletin of Science and Technology, 2004, 24, pp. 446-456.","A. Ghosh, A. Nafalski, M. Dollard, J. Tweedale, W.F. Mahmudy, \"An\nautonomous system for psychological risk assessment using the\nAustralian workplace barometer\", The 3rd Asia pacific Expert Workshop\non Psychological Factor at Work, 2-3 August 2012, Tokyo, Japan.","A. Ghosh, J. Tweedale, A. Nafalski, M. Dollard, \"Multi-agent based system for analysing stress using the Stresscafe\". Advances in\nKnowledge-Based and Intelligent Information and Engineering Systems,\n2012, Amsterdam, vol 243, pp.1656-1665.","M. F. Dollard, A. Taylor, et.al. \"Cohort profile: The Australian workplace barometer (AWB)\", 2010.","StressCafé®: Workplace stress. Retrieved July 14, 2010, from\nhttp://www.stresscafe.edu.au.","S. J. Russell, P. Norvig, Artificial Intelligence: A modern approach,\n3rded, 2010, Upper Saddle River, NJ, Prentice Hall.","M. Wooldridge, N. Jennings, Intelligent agents: Theory and practice,\nKnowledge Engineering Review, Prentice Hall, Inc., 3rd edition,\n199510(2), 115-152.","D. Rumelhart, G. Hilton, R Williams, \"Learning representations by\nbackpropogation errors\", Nature, 1986, vol. 323, pp. 533-536.","W. Pedrycz, F. Gomide, Toward human centric computing, Fuzzy\nSystem Engineering, 2007, IEEE Press.\n[10] Z. Zang, C. Zang, Agent-based hybrid intelligent systems, LANI 2938, ┬®\nSpringer-Verlag Berlin Heidelberg 2004,pp. 3-11.\n[11] L. A, Zadeh, \"Fuzzy sets\", Inform and Control, 1965, vol, 8, pp. 338-353.\n[12] D.W.Patterson, \"Artificial neural networks theory and applications\",\nPrentice Hall International, 1996, pp. 247-264.\n[13] P.Werbos, \"Beyond regression: new tools for prediction and analysis in\nthr behavioral sciences,\" PhD. Dissertation, 1974, Harvard University.\n[14] D.Parker, Learning logic, Invention Report, S81-64, File 1, Office of Technology Licensing, 1982, Stanford University."]}