Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi

Snake known as most dangerous reptile as it threaten our live and can deal fatal wound for human. Mostly people cannot differentiate between venomous and non-venomous snake because most of non-venomous snake are likely to look like venomous one. This kind of problem can be solved using Artificial In...

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Main Author:	Ahmad Tarmizi, Muhammad Danial
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Expert systems (Computer science) Fuzzy expert systems Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/35680/1/35680.pdf
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id	my-uitm-ir.35680
record_format	uketd_dc
spelling	my-uitm-ir.356802023-10-14T03:43:46Z Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi 2020 Ahmad Tarmizi, Muhammad Danial Expert systems (Computer science). Fuzzy expert systems Neural networks (Computer science) Artificial immune systems. Immunocomputers Snake known as most dangerous reptile as it threaten our live and can deal fatal wound for human. Mostly people cannot differentiate between venomous and non-venomous snake because most of non-venomous snake are likely to look like venomous one. This kind of problem can be solved using Artificial Intelligence approach. This paper aims to discuss about the project built for encountering that problems to detect and classify the snakes. Objectives of this project is to design the flow of the system, developed in Window application and test the functionality of the system and reliability for the predictive model. This project uses Convolutional Neural Network algorithms which is one the best algorithms for image processing. The algorithm is built using Tensorflow software. The development of the project is based on Waterfall methodology. Waterfall methodology consists of 6 phases starting from requirement analysis, system design, implementation, testing, deployment and maintenance. The predictive model success in detecting and classifying the snake. The model achieve 96.89 % of accuracy percentage in training set and 96% of accuracy from testing set. This project can be improved by employing in the less consumption hardware like Jetson Nano and using light sensitive camera to improve the image quality while detecting snake. 2020 Thesis https://ir.uitm.edu.my/id/eprint/35680/ https://ir.uitm.edu.my/id/eprint/35680/1/35680.pdf text en public degree Universiti Teknologi MARA, Cawangan Melaka Faculty of Computer and Mathematical Science Mahzan, Sulaiman
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mahzan, Sulaiman
topic	Expert systems (Computer science) Fuzzy expert systems Neural networks (Computer science) Expert systems (Computer science) Fuzzy expert systems
spellingShingle	Expert systems (Computer science) Fuzzy expert systems Neural networks (Computer science) Expert systems (Computer science) Fuzzy expert systems Ahmad Tarmizi, Muhammad Danial Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi
description	Snake known as most dangerous reptile as it threaten our live and can deal fatal wound for human. Mostly people cannot differentiate between venomous and non-venomous snake because most of non-venomous snake are likely to look like venomous one. This kind of problem can be solved using Artificial Intelligence approach. This paper aims to discuss about the project built for encountering that problems to detect and classify the snakes. Objectives of this project is to design the flow of the system, developed in Window application and test the functionality of the system and reliability for the predictive model. This project uses Convolutional Neural Network algorithms which is one the best algorithms for image processing. The algorithm is built using Tensorflow software. The development of the project is based on Waterfall methodology. Waterfall methodology consists of 6 phases starting from requirement analysis, system design, implementation, testing, deployment and maintenance. The predictive model success in detecting and classifying the snake. The model achieve 96.89 % of accuracy percentage in training set and 96% of accuracy from testing set. This project can be improved by employing in the less consumption hardware like Jetson Nano and using light sensitive camera to improve the image quality while detecting snake.
format	Thesis
qualification_level	Bachelor degree
author	Ahmad Tarmizi, Muhammad Danial
author_facet	Ahmad Tarmizi, Muhammad Danial
author_sort	Ahmad Tarmizi, Muhammad Danial
title	Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi
title_short	Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi
title_full	Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi
title_fullStr	Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi
title_full_unstemmed	Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi
title_sort	snake detection system using convolutional neural network / muhammad danial ahmad tarmizi
granting_institution	Universiti Teknologi MARA, Cawangan Melaka
granting_department	Faculty of Computer and Mathematical Science
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/35680/1/35680.pdf
_version_	1783734311093010432

Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi

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