Current Issue - May 2023, Volume 14, Number 2

 May 2023, Volume 14, Number 2

Texts Classification with the usage of Neural Network based on the Word2vec’s Words Representation 

D. V. Iatsenko, Southern Federal University, Russia

ABSTRACT

Assigning the submitted text to one of the predetermined categories is required when dealing with application-oriented texts. There are many different approaches to solving this problem, including using neural network algorithms. This article explores using neural networks to sort news articles based on their category. Two word vectorization algorithms are being used — The Bag of Words (BOW) and the word2vec distributive semantic model. For this work the BOW model was applied to the FNN, whereas the word2vec model was applied to CNN. We have measured the accuracy of the classification when applying these methods for ad texts datasets. The experimental results have shown that both of the models show us quite the comparable accuracy. However, the word2vec encoding used for CNN showed more relevant results, regarding to the texts semantics. Moreover, the trained CNN, based on the word2vec architecture, has produced a compact feature map on its last convolutional layer, which can then be used in the future text representation. I.e. Using CNN as a text encoder and for learning transfer.

KEYWORDS

Deep Learning, Text classification, Word2Vec, BOW, CNN

Original Source URL: https://aircconline.com/ijsc/V14N2/14223ijsc01.pdf

https://airccse.org/journal/ijsc/current2023.html

Current Issue: May 2023, Volume 14, Number 2

Texts Classification with the usage of Neural Network based on the Word2vec’s Words Representation

D. V. Iatsenko, Southern Federal University, Russia

https://aircconline.com/ijsc/V14N2/14223ijsc01.pdf

https://airccse.org/journal/ijsc/current2023.html

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Paper Submission URL - https://airccse.com/submissioncs/home.html

Introduction of a Novel Anomalous Sound Detection Methodology

 International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

https://airccse.org/journal/ijsc/ijsc.html

Current Issue

August 2022, Volume 13, Number 3

Introduction of a Novel Anomalous Sound Detection Methodology 

Xiao Tan and S M Yiu, University of Hong Kong, Hong Kong

https://aircconline.com/ijsc/V13N3/13322ijsc01.pdf

https://airccse.org/journal/ijsc/current2022.html

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 Current Issue

August 2022, Volume 13, Number 3

Introduction of a Novel Anomalous Sound Detection Methodology 

Xiao Tan and S M Yiu, University of Hong Kong, Hong Kong

ABSTRACT

This paper is to introduce a novel semi-supervised methodology, the enhanced incremental principal component analysis (“IPCA”) based deep convolutional neural network autoencoder (“DCNN-AE) for Anomalous Sound Detection (“ASD”) with high accuracy and computing efficiency. This hybrid methodology is to adopt Enhanced IPCA to reduce the dimensionality and then to use DCNN-AE to extract the features of the sample sound and detect the anomality. In this project, 228 sets of normal sounds and 100 sets of anomaly sounds of same machine are used for the experiments. And the sound files of machines (stepper motors) for the experiments are collected from a plant site. 50 random test cases are executed to evaluate the performance of the algorithm with AUC, PAUC, F measure and Accuracy Score. IPCA Based DCNN-AE shows high accuracy with the average AUC of 0.815793282, comparing with that of Kmeans++ of 0.499545351, of Incremental PCA based DBSCAN clustering of 0.636348073, of Incremental based PCA based One-class SVM of 0.506749433 and of DCGAN of 0.716528104. From the perspective of computing efficiency, because of the dimensions-reduction by the IPCA layer, the average execution time of the new methodology is 15 minutes in the CPU computing module of 2.3 GHz quad-core processors, comparing with that of DCGAN with 90 minutes in GPU computing module of 4 to 8 kernels.

KEYWORDS

Internet of things (“IoT”), Anomalous Sound Detection (“ASD”), Audio Frames, Deep learning, Deep Convolutional Neural Network, Autoencoder.

Original Source URL: https://aircconline.com/ijsc/V13N3/13322ijsc01.pdf

https://airccse.org/journal/ijsc/current2022.html

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