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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Mihir Narayan Mohanty
Siksha O Anusandhan University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Variable Sign-Sign Wilcoxon Algorithm: A Novel Approach for System Identification Mihir Narayan Mohanty; Sidhartha Dash
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 4: August 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (146.171 KB)

Abstract

Behavioral study of a system is an important task. It is mostly used in real world environments and became an emergent research area. Various approaches have been proposed since last two decades. In this paper, we have proposed a Variable Step-Size Sign-Sign Wilcoxon Approach, that is robust against outliers in the desired data and also convergence speed is faster than Wilcoxon norm based approach. In initial stage, Sign-Sign Wilcoxon norm based approach has been verified. Next to it, the proposed approach is verified and compared for the application in Linear and Non-linear system identification problems in presence of outliers.DOI:http://dx.doi.org/10.11591/ijece.v2i4.837
Classification of Emotional Speech of Children Using Probabilistic Neural Network Hemanta Kumar Palo; Mihir Narayan Mohanty
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.801 KB) | DOI: 10.11591/ijece.v5i2.pp311-317

Abstract

Child emotions are highly flexible and overlapping. The recognition is a difficult task when single emotion conveys multiple informations. We analyze the relevance and importance of these features and use that information to design classifier architecture. Designing of a system for recognition of children emotions with reasonable accuracy is still a challenge specifically with reduced feature set. In this paper, Probabilistic neural network (PNN) has been designed for such task of classification. PNN has faster training ability with continuous class probability density functions. It provides better classification even with reduced feature set. LP_VQC and pH vectors are used as the features for the classifier. It has been attempted to design the PNN classifier with these features. Various emotions like angry, bore, sad and happy have been considered for this piece of work. All these emotions have been collected from children in three different languages as English, Hindi, and Odia. Result shows remarkable classification accuracy for these classes of emotions. It has been verified in standard databse EMO-DB to validate the result.
Co-Authors Hemanta Kumar Palo Laxmi Prasad Mishra Monojit Mitra Sarthak Panda Sidhartha Dash Sudarshan Chakraborty