Cover Image

An Educational Android App For Identifying Animals In Zoo

Zoo has been one of the favourite learning spots for children when it comes to animal study. In the outdoor learning session, teachers will often find it difficult to tutor their students while ensuring their safety. The advent of modern technology aided teachers in educating students without their...

Full description

Saved in:
Bibliographic Details
Main Author: Voon, Cherng Jyh
Format: Final Year Project / Dissertation / Thesis
Published: 2020
Subjects:
QA76 Computer software
Online Access:http://eprints.utar.edu.my/4204/1/1601883_VOON_CHERNG_JYH.pdf
http://eprints.utar.edu.my/4204/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utar-eprints.4204
record_format eprints
spelling my-utar-eprints.42042021-08-09T13:34:02Z An Educational Android App For Identifying Animals In Zoo Voon, Cherng Jyh QA76 Computer software Zoo has been one of the favourite learning spots for children when it comes to animal study. In the outdoor learning session, teachers will often find it difficult to tutor their students while ensuring their safety. The advent of modern technology aided teachers in educating students without their presence. The project explored the possibility of using smartphones as a medium to help kids to learn about animals in zoos, even without the guidance of teachers. The objective of this project is to build an Android mobile application for kids that automatically recognizes images of an animal using Convolutional Neural Network and Transfer Learning. Phased development methodology was used for application development. The developed application consists of three major modules: animal recognition module that is able to recognize 18 different animals, gamification module that attracts the interest of kids and lastly quiz module that broadens the kid’s knowledge on animals. Apart from that, experiments were performed on three pretrained models, which is MobileNet, EffNet and NASNet to determine the most suitable model to be used for Transfer Learning. The models were analyzed from the aspect of accuracy, output model size, speed and power consumption. MobileNet was observed to be the least power-hungry (38.2 million FLOPs), fastest (0.018 Million parameters) and smallest in size (12.859 MB) among the three. Considering the time spent in zoo, Mobilenet was integrated into the system for image recognition. The mobile application was successfully designed, developed and tested throughout the project. Three pretrained models were compared and MobileNet was selected as the image recognition model. It is believed that the product will aid zoos and educators in teaching the kids about wildlife. 2020 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4204/1/1601883_VOON_CHERNG_JYH.pdf Voon, Cherng Jyh (2020) An Educational Android App For Identifying Animals In Zoo. Final Year Project, UTAR. http://eprints.utar.edu.my/4204/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic QA76 Computer software
spellingShingle QA76 Computer software
Voon, Cherng Jyh
An Educational Android App For Identifying Animals In Zoo
description Zoo has been one of the favourite learning spots for children when it comes to animal study. In the outdoor learning session, teachers will often find it difficult to tutor their students while ensuring their safety. The advent of modern technology aided teachers in educating students without their presence. The project explored the possibility of using smartphones as a medium to help kids to learn about animals in zoos, even without the guidance of teachers. The objective of this project is to build an Android mobile application for kids that automatically recognizes images of an animal using Convolutional Neural Network and Transfer Learning. Phased development methodology was used for application development. The developed application consists of three major modules: animal recognition module that is able to recognize 18 different animals, gamification module that attracts the interest of kids and lastly quiz module that broadens the kid’s knowledge on animals. Apart from that, experiments were performed on three pretrained models, which is MobileNet, EffNet and NASNet to determine the most suitable model to be used for Transfer Learning. The models were analyzed from the aspect of accuracy, output model size, speed and power consumption. MobileNet was observed to be the least power-hungry (38.2 million FLOPs), fastest (0.018 Million parameters) and smallest in size (12.859 MB) among the three. Considering the time spent in zoo, Mobilenet was integrated into the system for image recognition. The mobile application was successfully designed, developed and tested throughout the project. Three pretrained models were compared and MobileNet was selected as the image recognition model. It is believed that the product will aid zoos and educators in teaching the kids about wildlife.
format Final Year Project / Dissertation / Thesis
author Voon, Cherng Jyh
author_facet Voon, Cherng Jyh
author_sort Voon, Cherng Jyh
title An Educational Android App For Identifying Animals In Zoo
title_short An Educational Android App For Identifying Animals In Zoo
title_full An Educational Android App For Identifying Animals In Zoo
title_fullStr An Educational Android App For Identifying Animals In Zoo
title_full_unstemmed An Educational Android App For Identifying Animals In Zoo
title_sort educational android app for identifying animals in zoo
publishDate 2020
url http://eprints.utar.edu.my/4204/1/1601883_VOON_CHERNG_JYH.pdf
http://eprints.utar.edu.my/4204/
_version_ 1709672664486903808
score 13.211869

Similar Items