Fish classification using machine learning / Ahmad Syahmi Shahriman

Shahriman, Ahmad Syahmi (2021) Fish classification using machine learning / Ahmad Syahmi Shahriman. Degree thesis, Universiti Teknologi MARA, Perak.

Abstract

Fish classification may be identified manually from its characteristics such as the scale of the fish, shape of the head of a fish, the size of tail, the size of the body and more, which can be confusing to non-professional people. The purpose of this project is to assist people that are non-expert, to identify the type of fish based on the image given to the project. The project will consist of a fish dataset of commonly sold fish in Malaysia. The user is able to use the application which is available for the Android operating system, and able to detect the type of fish the user has given to the application. The other purpose of the project is to educate people about fish, as most people in Malaysia lack of knowledge about fisheries.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Shahriman, Ahmad Syahmi
2020994875
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Isawasan, Pradeep
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Mobile computing
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Operating systems (Computers) > Android
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
Divisions: Universiti Teknologi MARA, Perak > Tapah Campus > Faculty of Computer and Mathematical Sciences
Programme: Computer Science
Keywords: Fish classification; machine learning
Date: July 2021
URI: https://ir.uitm.edu.my/id/eprint/59446
Edit Item
Edit Item

Download

[thumbnail of 59446.pdf] Text
59446.pdf

Download (179kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:

ID Number

59446

Indexing

Statistic

Statistic details