Design Analysis of Banking Transaction Descriptions via Deep Learning Mechanism

Authors

  • Pallavi D. Bankar  Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India
  • Prof. Anil V. Deorankar  Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India

Keywords:

Vibroarthrography, Wavelet decomposition, Feature extraction, Principal Component Analysis, support Vector Machine.

Abstract

A non-invasive technique using knee joint vibroarthographic (VAG) signals can be used for the early diagnosis of knee joint disorders. Among the algorithms devised for the detection of knee joint disorders using VAG signals, algorithms based on entropy measures can provide better performance. In this work, the VAG signal is preprocessed using wavelet decomposition into sub band signals. Features of the decomposed sub bands such as approximate entropy, sample entropy & wavelet energy are extracted as a quantified measure of complexity of the signal. A feature selection based on Principal Component Analysis (PCA) is performed in order to select the significant features. The extracted features are then used for classification of VAG signal into normal and abnormal VAG using support vector machine. It is observed that the classifier provides a better accuracy with feature selection using principal component analysis. And the results show that the classifier was able to classify the signal with an accuracy of 82.6%, error rate of 0.174, sensitivity of 1.0 and specificity of 0.888.

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International Journal of Scientific Research in Science and Technology

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Published

2021-10-30

Issue

Volume 8, Issue 5, September-October-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Pallavi D. Bankar, Prof. Anil V. Deorankar, " Design Analysis of Banking Transaction Descriptions via Deep Learning Mechanism, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 8, Issue 5, pp.272-277, September-October-2021.