An Optimization Method Using Clustering Technique for the Human Emotions Detection Artificial Neuro-Fuzzy Logic System
DOI:
https://doi.org/10.24297/ijct.v15i9.695Keywords:
Clustering, Human emotions, Neuro-Fuzzy, Data MiningAbstract
This paper utilizes clustering tool in MATLAB to find an optimal set of input parameters for the detection of human emotions using a neuro-fuzzy logic system. Previous studies have relied on a total of 14 physiological factors to detect one or more of 22 different human emotions. In this paper, we use clustering techniques to rank the factors in terms of their significance and impact on the system, and thus find a smaller subset of the factors for the detection of emotions. The clustering method shows that the stroke volume factor (SV) has the lowest impact in the model and as such can be eliminated from the set of factors. The electroencephalography (EEG), heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) are shown to have the highest impact on the model, and must be include in the input set of the model. We compare the clustering method with exhaustive methods for finding the optimal set of factors.Downloads
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References
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[2] Albashiti, E. A. I., Malkawi, M., & Khasawneh, M. A NOVEL OPTIMIZATION ALGORITHM TO DETECT HUMAN EMOTIONS USING ARTIFICIAL INTELLIGENCE TECHNIQUES.
[3] Bakhtiyari, K., & Husain, H. (2014). Fuzzy model on human emotions recognition. arXiv preprint arXiv:1407.1474.
[4] Burkhardt, F., Van Ballegooy, M., Engelbrecht, K. P., Polzehl, T., & Stegmann, J. (2009, September). Emotion detection in dialog systems: applications, strategies and challenges. In Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on (pp. 1-6). IEEE.
[5] Charikar, M., Chekuri, C., Feder, T., & Motwani, R. (1997, May). Incremental clustering and dynamic information retrieval. In Proceedings of the twenty-ninth annual ACM symposium on Theory of computing (pp. 626-635). ACM.
[6] Demuth, Howard, and Mark Beale. "Neural network toolbox for use with MATLAB." (1993).
[7] Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
[8] Kohonen, T. (2001). Self-organizing maps, vol. 30 of Springer Series in Information Sciences.
[9] Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological psychology, 84(3), 394-421.
[10] Malkawi, M., & Murad, O. (2013). Artificial neuro fuzzy logic system for detecting human emotions. Human-Centric Computing and Information Sciences, 3(1), 1-13.
[11] Mc Cutchen, R. M., & Khuller, S. (2008). Streaming algorithms for k-center clustering with outliers and with anonymity. In Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques (pp. 165-178). Springer Berlin Heidelberg.
[12] Negnevitsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson Education.
[13] Nagpal, R., Nagpal, P., & Kaur, S. (2010). Hybrid technique for human face emotion detection. IJACSA) International Journal of Advanced Computer Science and Applications, 1, 91-101.
[14] Owaied, H. H., & Abu-A'ra, M. M. (2007, June). Functional Model of Human System as Knowledge Based System. In IKE (pp. 158-164).
[15] Sarazin, T., Azzag, H., & Lebbah, M. (2014, May). SOM Clustering using Spark-MapReduce. In Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International (pp. 1727-1734). IEEE.
[16] Timmons, N. F., & Scanlon, W. G. (2004, October). Analysis of the performance of IEEE 802.15. 4 for medical sensor body area networking. In Sensor and ad hoc communications and networks, 2004. IEEE SECON 2004. 2004 First Annual IEEE Communications Society Conference on (pp. 16-24). IEEE.
[17] Varsta, M., Heikkonen, J., Lampinen, J., & Millán, J. D. R. (2001). Temporal Kohonen map and the recurrent self-organizing map: Analytical and experimental comparison. Neural processing letters, 13(3), 237-251.
[18] Zadeh, L. A. (1965). Fuzzy sets. Information and
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Published
2016-05-24
How to Cite
Murad, O., & Malkawi, M. (2016). An Optimization Method Using Clustering Technique for the Human Emotions Detection Artificial Neuro-Fuzzy Logic System. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(9), 7090–7096. https://doi.org/10.24297/ijct.v15i9.695
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