Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly

Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning have designed many algorithms to solve diverse human problems. The prevailing transfer learning method in recent years enables resear...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd Nazly, Dania Qistina
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Mobile computing
Online Access:	https://ir.uitm.edu.my/id/eprint/59253/1/59253.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.59253
record_format	uketd_dc
spelling	my-uitm-ir.592532023-10-26T00:06:36Z Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly 2021-03 Mohd Nazly, Dania Qistina Mobile computing Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning have designed many algorithms to solve diverse human problems. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. Due to a lack of exposure in image processing and the numerous designs of Malaysia coins, mobile coin identification technology is still not widely employed in Malaysia. Moreover, the procedure of classifying coins of various values is time-consuming since the number of coins in Malaysia is a lot. It is necessary to develop a system that is capable of accurately recognizing and identifying coins in a short amount of time.Therefore, this paper outlines the steps involved in creating a mobile application for Coin Identification using Machine Learning. Besides that, it introduces the many designs of Malaysian coins. The three machine learning types and classification techniques such as Support Vector Machine, K-Nearest Neighbors, Neural Network, and Naive-Bayes Classifier are defined and studied. In Literature review, the features from the already existing coin identification mobile application are compared by their advantages and limitations. The coin dataset will be trained and tested using the chosen technique. In methodology, the design and application architecture take place to determine the optimal model to deploy it in the mobile application. The official design and implementation of the application will also be stated to state the working progress for the final year project. This includes the limitations of the mobile application that were not able to be fulfilled and future improvements that could be added in the near future. 2021-03 Thesis https://ir.uitm.edu.my/id/eprint/59253/ https://ir.uitm.edu.my/id/eprint/59253/1/59253.pdf text en public degree Universiti Teknologi MARA, Perak Faculty of Computer and Mathematical Sciences Isawasan, Pradeep
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Isawasan, Pradeep
topic	Mobile computing
spellingShingle	Mobile computing Mohd Nazly, Dania Qistina Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly
description	Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning have designed many algorithms to solve diverse human problems. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. Due to a lack of exposure in image processing and the numerous designs of Malaysia coins, mobile coin identification technology is still not widely employed in Malaysia. Moreover, the procedure of classifying coins of various values is time-consuming since the number of coins in Malaysia is a lot. It is necessary to develop a system that is capable of accurately recognizing and identifying coins in a short amount of time.Therefore, this paper outlines the steps involved in creating a mobile application for Coin Identification using Machine Learning. Besides that, it introduces the many designs of Malaysian coins. The three machine learning types and classification techniques such as Support Vector Machine, K-Nearest Neighbors, Neural Network, and Naive-Bayes Classifier are defined and studied. In Literature review, the features from the already existing coin identification mobile application are compared by their advantages and limitations. The coin dataset will be trained and tested using the chosen technique. In methodology, the design and application architecture take place to determine the optimal model to deploy it in the mobile application. The official design and implementation of the application will also be stated to state the working progress for the final year project. This includes the limitations of the mobile application that were not able to be fulfilled and future improvements that could be added in the near future.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Nazly, Dania Qistina
author_facet	Mohd Nazly, Dania Qistina
author_sort	Mohd Nazly, Dania Qistina
title	Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly
title_short	Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly
title_full	Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly
title_fullStr	Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly
title_full_unstemmed	Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly
title_sort	mobile application: coin identification using machine learning / dania qistina mohd nazly
granting_institution	Universiti Teknologi MARA, Perak
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/59253/1/59253.pdf
_version_	1783735023521759232

Mobile application: coin identification using machine learning / Dania Qistina Mohd Nazly

Similar Items