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Journal of Information Science and Engineering, Vol. 25 No. 6, pp. 1737-1751 (November 2009)

Blood Cell Identification Using Emotional Neural Networks

ADNAN KHASHMAN
?Intelligent Systems Research Group (ISRG)
Faculty of Engineering
Near East University
Lefkosa, Mersin 10, Turkey

The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time.

Keywords: neural networks, back propagation, emotion modeling, anxiety and confidence, object identification, blood cells

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Received January 23, 2008; revised September 23 & December 9, 2008; accepted December 24, 2008.
Communicated by Pau-Choo Chung.