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All Journal JOIV : International Journal on Informatics Visualization
Abdulwahab Anaam, Elham
Unknown Affiliation
Computer Science & IT

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An Automated Face Detection and Recognition for Class Attendance Horn Boe, Chang; Ng, Kok-Why; Haw, Su-Cheng; Naveen, Palanichamy; Abdulwahab Anaam, Elham
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2967

Abstract

Class attendance is a crucial indicator of students' seriousness towards learning. Many institutions continue to use manual methods, which are usually error-prone and unproductive. By leveraging computer vision algorithms, the system accurately captures and verifies the identity of students attending class. This paper aims to investigate and create an automated facial recognition system for classroom attendance to increase the precision and effectiveness of the attendance tracking system. To achieve this, we propose a system using computer vision technologies, namely Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) for face detection and deep Convolutional Neural Networks (CNN) for face identification. The facial recognition system simplifies attendance recording, requiring participants to only gaze into the camera for the system to record their presence automatically. The system is rigorously tested and evaluated, and its accuracy is compared to our institution's current QR code attendance method. The study results reveal that the recommended approach is more accurate and competent than the existing procedures. The system allows for precise attendance records with real-time face detection and recognition capabilities. This technology ensures accurate and reliable attendance data, empowering organizations to make informed decisions, effectively manage resources, and provide a seamless experience for all students. In addition, a similar attendance system can be deployed for any event in an organization, thereby enhancing overall operational efficiency.
Social Platforms in the Deepfake Age: Navigating Media Trust through Media Literacy Lee, Fong Yee; Kumaresan, S Prabha; Abdulwahab Anaam, Elham; Chee Kong, Wong
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3490

Abstract

The issues with social media landscape are proliferation of disinformation, misinformation, and misinformation. The widespread of deepfakes makes is harder to distinguish between authentic content and fabricated content. The mediating effect of media literacy on news credibility has been understudied in previous research; the objective of the study is to investigate how much media literacy, news skepticism and fear of missing out (FOMO) influencing users' trust in the news disseminated on social media platforms. To achieve this, a survey was conducted to assess trust in and skepticism towards social media news, FOMO levels, and media literacy associated with deepfake news content. Educational efforts and media literacy initiatives are crucial in fostering informed and discerning news consumption. Furthermore, news organizations continue to prioritize transparency and accuracy to maintain credibility on social media since the news is easily accessible in the era of an information overload. The limitation of the study was the lack of assessment on evaluating effectiveness of media literacy in combating fabricated news content on social media. It is suggested to broaden scope by studying additional factors to combat fake news such as journalistic standards, fact-checking and verification are important to build reader’s trust. Future studies should also measure the effectiveness of media literacy initiatives ensure they really make a difference. The generalizability of future study can be strengthened with the inclusion of diverse age groups especially vulnerable populations.
Co-Authors Chee Kong, Wong Horn Boe, Chang Kumaresan, S Prabha Lee, Fong Yee Naveen, Palanichamy Ng, Kok-Why Su-Cheng Haw