Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri

Every year, millions of crimes are reported all across the world. According to the statistical analysis of the crime rate for Malaysia, it shows that in Malaysia, the crime index ratio per 100,000 population was 273.8 cases in the year 2018. However, for WP Kuala Lumpur, for every 100,000 population...

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Main Author:	Mohamad Zamri, Nurul Farhana
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Crimes and criminal classes
Online Access:	https://ir.uitm.edu.my/id/eprint/82550/1/82550.pdf
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id	my-uitm-ir.82550
record_format	uketd_dc
spelling	my-uitm-ir.825502023-12-26T06:46:39Z Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri 2022 Mohamad Zamri, Nurul Farhana Crimes and criminal classes Every year, millions of crimes are reported all across the world. According to the statistical analysis of the crime rate for Malaysia, it shows that in Malaysia, the crime index ratio per 100,000 population was 273.8 cases in the year 2018. However, for WP Kuala Lumpur, for every 100,000 population it is 642.6 cases. Thus, it shows that crime usually happens within cities and towns. Besides the negative impacts on citizens' everyday lives, there is a significant impact on economic growth that shows the relationship between crime and economic growth in Malaysia. Hence, this study focused on snatch theft, including evaluation and validation in real-time detection, which has not been fully explored. This study aims to differentiate snatch theft scenarios from normal scenarios in predicting and detecting snatch theft crimes classification utilising snatch theft databases obtained from 120 videos on YouTube and Google. 2022 Thesis https://ir.uitm.edu.my/id/eprint/82550/ https://ir.uitm.edu.my/id/eprint/82550/1/82550.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Md. Tahir, Nooritawati
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Md. Tahir, Nooritawati
topic	Crimes and criminal classes
spellingShingle	Crimes and criminal classes Mohamad Zamri, Nurul Farhana Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri
description	Every year, millions of crimes are reported all across the world. According to the statistical analysis of the crime rate for Malaysia, it shows that in Malaysia, the crime index ratio per 100,000 population was 273.8 cases in the year 2018. However, for WP Kuala Lumpur, for every 100,000 population it is 642.6 cases. Thus, it shows that crime usually happens within cities and towns. Besides the negative impacts on citizens' everyday lives, there is a significant impact on economic growth that shows the relationship between crime and economic growth in Malaysia. Hence, this study focused on snatch theft, including evaluation and validation in real-time detection, which has not been fully explored. This study aims to differentiate snatch theft scenarios from normal scenarios in predicting and detecting snatch theft crimes classification utilising snatch theft databases obtained from 120 videos on YouTube and Google.
format	Thesis
qualification_level	Master's degree
author	Mohamad Zamri, Nurul Farhana
author_facet	Mohamad Zamri, Nurul Farhana
author_sort	Mohamad Zamri, Nurul Farhana
title	Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri
title_short	Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri
title_full	Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri
title_fullStr	Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri
title_full_unstemmed	Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri
title_sort	snatch theft crime for criminal patterns detection and classification using deep learning model / nurul farhana mohamad zamri
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/82550/1/82550.pdf
_version_	1794191930803879936

Snatch theft crime for criminal patterns detection and classification using deep learning model / Nurul Farhana Mohamad Zamri

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