Mobile application: math question solver for primary school students / Ikmal Hakim Badrulhaisham

Badrulhaisham, Ikmal Hakim (2021) Mobile application: math question solver for primary school students / Ikmal Hakim Badrulhaisham. Degree thesis, Universiti Teknologi MARA, Perak.

Abstract

The purpose of the research is to help students, mainly primary school students, to learn and understand the mathematical problem-solving questions that are written in English language passages. This research presents the performance and work flow of the mathematics problem solving mobile application that allows user to input the mathematic question text or image, then the app will convert the text into the text arithmetic equation. The suitable software, hardware, and dataset to use for the project is studied to gain the best output and performance in general. The methodology that is used is being studied to give the best project development and research flow. The method used in the research are OCR (Optical Character Recognition), RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network). The research determines its result by user testing the application after it has been completed. For result acquired, the system feature could be added in the future research to enable user to experience better application performance.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Badrulhaisham, Ikmal Hakim
2020963859
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Khairudin, Nurkhairizan
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Elementary mathematics. Arithmetic
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: Faculty of Computer Science (Hons.)
Keywords: Mobile Application; Arithmetic Equation; OCR (Optical Character Recognition; RNN (Recurrent Neural Network); CNN (Convolutional Neural Network)
Date: July 2021
URI: https://ir.uitm.edu.my/id/eprint/59175
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