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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Education and Learning (EduLearn)
Ujir, Hamimah
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Humanities Computer Science & IT Education Engineering Library & Information Science Social Sciences Other

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Chelonia mydas detection and image extraction from field recordings Amir Zakry, Khalif; Syahiran Soria, Mohamad; Hipiny, Irwandi; Ujir, Hamimah; Hassan, Ruhana; Hardi, Richki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2354-2363

Abstract

Wildlife videography is an essential data collection method for conducting. The video recording process of an animal like the Chelonia mydas sea turtles in its habitat requires setting up special camera or by performing complex camera movement whilst the camera operator maneuvers over its complicated habitat. The result is hours of footage that contains only some good data that can be used for further animal research but still requires human input in filtering it out This presents a problem that artificial intelligence models can assist, especially to automate extracting any good data. This paper proposes usage of machine learning models to crop images of endangered Chelonia mydas turtles to help prune through hundreds and thousands of frames from several video footages. By human supervision, we extracted and curated a dataset of 1,426 good data from our video dataset and used it to perform transfer learning on a you only look once (YOLO)v7 pre-trained model. Our paper shows that the retrained YOLOv7 model when run through our remaining video dataset with various confidence scores can crop images in the field video recordings of Chelonia mydas turtles with up to 99.89% of output correctly cropped thus automating the data extraction process.
Assessing digital competence and its impact on academic performance: insights from Universiti Malaysia Sarawak undergraduates Victor, Ajibol Omoniyi; Ujir, Hamimah
Journal of Education and Learning (EduLearn) Vol 19, No 4: November 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v19i4.23002

Abstract

Information and communication technology (ICT) has become an essential part of the daily lives of tertiary students. However, research into assessing digital competency and its effects on academic performance is still limited. This paper explores students’ needs for digital competence, the impact of digital access on academic performance, and the relationship between digital competence and educational success, focusing on undergraduates at Universiti Malaysia Sarawak (UNIMAS). Using a model with 64 measurement items and nine variables, the study identifies significant correlations between information and data literacy (IDL), safety and security (SS), and problem-solving (PS) proficiency with digital competence. Conversely, communication and collaboration (CC) and digital content creation (DCC) show statistically insignificant correlations. Additionally, while digital resource availability has a minor inverse correlation, digital usage is significantly and positively related to digital competence. The findings suggest that digital competence strongly predicts academic performance and that most undergraduates exhibit advanced proficiency in essential digital skills. This research highlights the crucial role of digital competence in enhancing educational outcomes and offers insights into key competencies linked to digital effectiveness.
Co-Authors Amir Zakry, Khalif Hassan, Ruhana Hipiny, Irwandi Richki Hardi Syahiran Soria, Mohamad Victor, Ajibol Omoniyi