Glass break detection system using deep auto encoders with fuzzy rules induction algorithm

Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibr...

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Main Authors:	Nyein Naing, Wai Yan, Htike, Zaw Zaw
Format:	Article
Language:	English English
Published:	Science Gate 2019
Subjects:	Q350 Information theory
Online Access:	http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/1/69673_Glass%20break%20detection%20system%20using%20deep.pdf http://irep.iium.edu.my/69673/2/69673_Glass%20break%20detection%20system%20using%20deep_WOS.pdf

id	iium-69673
recordtype	eprints
spelling	iium-696732019-03-12T08:21:35Z http://irep.iium.edu.my/69673/ Glass break detection system using deep auto encoders with fuzzy rules induction algorithm Nyein Naing, Wai Yan Htike, Zaw Zaw Q350 Information theory Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibration-based and acoustic-based glass break detectors are designed to detect predetermined temporal and frequency feature thresholds of glass breakage sound signals. This leads to the inability to differentiate glass break from environmental sounds (such as the sound of striking objects, heavy sounds and shouted sounds) that are similar in their amplitude threshold and frequency pattern. Machine learning based acoustic audio classification has been popular in security surveillance applications. Researchers are interested in this research area, and different approaches have been proposed for anomaly event detection (such as gunshots, glass breakage sounds, etc.). This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. The algorithm reduces false alarms and improves detection accuracy. Experimental results indicate that proposed fuzzy deep auto-encoder network system attained 95.5% correct detection for the proposed audio dataset. Science Gate 2019-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/69673/1/69673_Glass%20break%20detection%20system%20using%20deep.pdf application/pdf en http://irep.iium.edu.my/69673/2/69673_Glass%20break%20detection%20system%20using%20deep_WOS.pdf Nyein Naing, Wai Yan and Htike, Zaw Zaw (2019) Glass break detection system using deep auto encoders with fuzzy rules induction algorithm. International Journal of Advanced and Applied Sciences, 6 (2). pp. 33-38. ISSN 2313-626X E-ISSN 2313-3724 http://www.science-gate.com/IJAAS/2019/V6I2/1021833ijaas201902006.html 10.21833/ijaas.2019.02.006
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q350 Information theory
spellingShingle	Q350 Information theory Nyein Naing, Wai Yan Htike, Zaw Zaw Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
description	Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibration-based and acoustic-based glass break detectors are designed to detect predetermined temporal and frequency feature thresholds of glass breakage sound signals. This leads to the inability to differentiate glass break from environmental sounds (such as the sound of striking objects, heavy sounds and shouted sounds) that are similar in their amplitude threshold and frequency pattern. Machine learning based acoustic audio classification has been popular in security surveillance applications. Researchers are interested in this research area, and different approaches have been proposed for anomaly event detection (such as gunshots, glass breakage sounds, etc.). This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. The algorithm reduces false alarms and improves detection accuracy. Experimental results indicate that proposed fuzzy deep auto-encoder network system attained 95.5% correct detection for the proposed audio dataset.
format	Article
author	Nyein Naing, Wai Yan Htike, Zaw Zaw
author_facet	Nyein Naing, Wai Yan Htike, Zaw Zaw
author_sort	Nyein Naing, Wai Yan
title	Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
title_short	Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
title_full	Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
title_fullStr	Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
title_full_unstemmed	Glass break detection system using deep auto encoders with fuzzy rules induction algorithm
title_sort	glass break detection system using deep auto encoders with fuzzy rules induction algorithm
publisher	Science Gate
publishDate	2019
url	http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/ http://irep.iium.edu.my/69673/1/69673_Glass%20break%20detection%20system%20using%20deep.pdf http://irep.iium.edu.my/69673/2/69673_Glass%20break%20detection%20system%20using%20deep_WOS.pdf
first_indexed	2023-09-18T21:38:54Z
last_indexed	2023-09-18T21:38:54Z
_version_	1777413006887485440

Glass break detection system using deep auto encoders with fuzzy rules induction algorithm

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