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Adaptable Web Search User Interface Model for the Elderly
Khalid Krayz allah,Nor Azman Ismail,Layla Hasan,Wad Ghaban,Nadhmi A. Gazem,Maged Nasser 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.9
The elderly population is rapidly increasing worldwide, but many face challenges in using digital tools like the Internet due to health and incapacity issues. Existing online search user interfaces (UIs) often overlook the specific usability needs of the elderly. This study proposes an adaptable web search UI model for the elderly, based on their perspectives, to enhance search performance and usability. The proposed UI model is evaluated through comparative usability testing with 20 participants, comparing it to the Google search UI. Effectiveness, efficiency, and satisfaction are measured using task completion time, error rate, and subjective preferences. The results show significant differences (p > 0.05) between the proposed web search UI model and the Google search UI. The proposed UI model achieves higher subjective satisfaction levels, indicating better alignment with the needs and preferences of elderly users. It also reduces task completion time, indicating improved efficiency, and decreases the error rate, suggesting enhanced effectiveness. These findings emphasize the importance of considering the unique usability needs of the elderly when designing search UIs. The proposed adaptable web search UI model offers a promising approach to enhance the digital experiences of elderly users. This study lays the groundwork for further development and refinement of adaptable web search UI models that cater to the specific needs of elderly users, enabling designers to create more inclusive and user-friendly search interfaces for the growing elderly population.
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Web-based University Classroom Attendance System Based on Deep Learning Face Recognition
Nor Azman Ismail,Cheah Wen Chai,Hussein Samma,Md Sah Salam,Layla Hasan,Nur Haliza Abdul Wahab,Farhan Mohamed,Wong Yee Leng,Mohd Foad Rohani 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.2
Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 – 45 degrees) and left (30 – 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.
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