Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Information Technology and Computer Science International Journal of Applied Sciences and Smart Technologies
Oladimeji, Oladosu
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Promoting Interest in Learning Yorùbá Language Using Mobile Game Oladimeji, Oladosu; Olorunfemi, Temitope; Oladimeji, Olayanju
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (917.533 KB) | DOI: 10.25126/jitecs.202053232

Abstract

This paper describes acute areas in which technology plays a role in language and culture revitalization. It was discovered that in order for people to learn a new language, they must express interest in that language. This work presents a new way of arousing the interest of people in learning Yorùbá language through the use of mobile game thereby promoting and revitalizing Yorùbá language and culture. The mobile application was evaluated using questionnaire to selected participants who have the mobile game developed installed on their phones and explored the application, and then rated based on some criteria such as extensibility, ease of use and user interest in learning Yorùbá Language after playing the game. The results showed that 76% of respondents rated the game ease of use as above average, 70% and 90% of the respondents rated the extensibility of the game and interest in learning Yorùbá after playing game above average respectively. This technology-based application will serve as an interesting and fun-filled approach of getting people to express interest in learning native indigenous language individually and as a group.
A Deep Learning Model for Identical National Flag Recognition in Selected African Countries Aworinde, Halleluyah Oluwatobi; Oladimeji, Oladosu; Adebayo, Segun; Akinwunmi, Akinwale; Sakpere, Aderonke Busayo; Oladimeji, Olayanju
International Journal of Applied Sciences and Smart Technologies Volume 05, Issue 02, December 2023
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v5i2.6452

Abstract

The national flags are among the symbolic representations of a country. They make us understand the country of interest in a particular issue. Therefore, they are commonly used in both private and government organizations. It has been discovered in recent times that the younger generation mostly and idly and spend its time online; hence, knowing little about national flags. Additionally, some national flags (particularly in West Africa) are identical in nature. The likeness is in terms of layout, colours, shapes and objects on the national flags. Hence, there is a need to have a model for flag recognition. In this paper, national flag images of some West African countries were gathered to form a dataset. After this, the images were preprocessed by cropping out the irrelevant parts of the images. VGG-16 was used to extract necessary features and to develop the deep learning model. This contrasted with the existing handcrafted feature extraction and traditional machine learning techniques used on this subject matter. It was observed from this study that the proposed approach performed excellently well in predicting national flags; with an Accuracy of 98.20%, and an F1 score of 98.16%. In the future, it would be interesting to incorporate the national flag recognition into Human-Computer Interaction System. For instance, it could be used as flag recognition in some mobile and web applications for individuals with colour blindness. This research work presents a robust model because of nature of the dataset used in this work compared to previous works.
Co-Authors Adebayo, Segun Akinwunmi, Akinwale Aworinde, Halleluyah Oluwatobi Oladimeji, Olayanju Olorunfemi, Temitope Sakpere, Aderonke Busayo