Biometrics and Bioinformatics

Shiv Kumar ¹, Vijay K. Chaudhari ², R. K. Singh ³, Dinesh Varshney ⁴, C. L. Saxena ⁵

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
3 MATS University, Raipur (C.G.), IN
4 Multimedia Research Department, Multimedia Regional Center, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), IN
5 Technocrats Institute of Technology, Bhopal (M.P.), IN

Abstract

In this project report, (i.e. “Result Analysis to Compute Entropy of Voice Signal (CEVS) SNR using Matlab”) an approach is used to compute the entropy of given voice signal and signal to noise ratio (SNR) with the help of computed entropy. The main goals of this project are:
• To compute the tone of inputted voice signal
• To estimate entropy of tone
• To calculate SNR of entropy
To do this, psychoacoustic model and wavelet toolbox is used. Psychoacoustic model calculates masking threshold. Maximum distortion energy is computed from computed tone of inputted voice signal which defines the CEVS and SNR.

Keywords

Matlab 6.5, Wavelet Toolbox, Psychoacoustic Model, Algorithm.

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Shiv Kumar ¹, Vijay K. Chaudhari ², Md. Ilyas Khan ³, Neetesh Gupta ⁴, Bupendra Verma ⁵

Affiliations
1 Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology at Technocrat Institute of Technology-Bhopal (M.P.), IN
3 Technocrats Institute of Technology, Bhopal, IN
4 Department of CSE/IT at Technocrat Institute of Technology-Bhopal (M.P.), IN
5 Gandhi Technical University, Bhopal(M.P.), IN

Abstract

In this paper “Genes Analysis of Data by Using Hierarchical Quality Threshold Clustering” is an approach which proposed dynamically Growing Hierarchical Self Organizing Map (DGHSOM) with Nano array to identify co-expressed genes. The DGHSOM overcomes the problem of specifying the number of clusters and total number of iteration before the processing now, we are using QT (quality threshold) clustering is a method of partitioning data, which is invented for gene clustering. It requires more computing power than k- means, but does not require specifying the number of clusters. DNA Nano array technology is a challenging area in bioinformatics research, as we have to monitor millions of genes simultaneously. The expression profile of the gene can be useful in cancer disease analysis and its diagnosis. Gene expression data is very voluminous and very difficult to analyze. Several clustering algorithm have been proposed to identify co expressed genes. The Self-organizing-maps (SOM) is a powerful tool for recognizing and classifying features in complex, micro array data. But the interpretation of co- expression of genes are heavily depends on domain knowledge and SOM lacks since the number of clusters must be determined before training.

Keywords

Gene Expression Profile, Image Processing, Dynamically Growing Self Organizing Map, Nano Array, Qt Clustering.

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