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Your search keyword '"Zakaria, Noor Hidayah"' showing total 19 results
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19 results on '"Zakaria, Noor Hidayah"'

3. Real-time smart driver sleepiness detection by eye aspect ratio using computer vision.

Author

Simon Chong Kai Yuen, Zakaria, Noor Hidayah, Goh Eg Su, Hassan, Rohayanti, Kasim, Shahreen, and Sutikno, Tole

Subjects
COMPUTER vision, RASPBERRY Pi, DROWSINESS, WAKEFULNESS, EAR, EYELIDS
Abstract

The purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Comparative studies of facial emotion detection in online learning.

Author

Ahmad, Asraful Syifaa', Hassan, Rohayanti, Zakaria, Noor Hidayah, and Moi, Sim Hiew

Subjects
ONLINE education, VIRTUAL classrooms, EMOTIONS, LEARNING, FACIAL expression, EMOTICONS & emojis, DIGITAL cameras, CAMERAS
Abstract

Due to the COVID-19 outbreak that hits everyone globally, every person is affected, including students. Starting with this, no one can refuse the importance of a smart online learning system being embedded in the education system anymore. Still, the emotional engagement between teachers and students is in doubt. Teachers tend to teach without knowing whether the students understand it. It is due to the limitation of having an online class instead of a physical one, but can be overcome by embedding emotion detection using facial expressions during the course. However, the next challenge is to overcome the system's limitations. Even though people have a well-equipped tool for themselves in this situation, it is hard to determine the exact emotion of the students during the learning process. The facial image captured in the real world usually has multiple issues, including (1) an obstacle in front of the face, (2) a different distance between the head and camera, and (3) low-resolution images due to the low bandwidth, causing the low accuracy of emotion classification. Thus, this paper took the initiative to compare two different datasets tested on four classifiers: CNN, DCNN, Transfer Learning, and Multiple Pipeline. Four different algorithm sets were used to test two datasets: FER2013 and our dataset. The result shows that the data captured in a real-world situation using an independent device setting contains some issues with the slightly low accuracy of emotion classification and leads to false classification compared to FER2013 data. Thus, this can be improved by having a powerful emotion detection system that can capture all the issues in a real-world situation [ABSTRACT FROM AUTHOR]

Published
2023
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16. Effect of pH Value on the Synthesis of NiO Nanoparticles and Microstructure of NiO Based Composite Anode

Author

Zakaria, Noor Hidayah Aniza and Osman, Nafisah

Abstract

NiO nanoparticle was synthesized by a sol-gel method with three different pH values namely pH=1, 7 and 11, and then calcined at temperature of 450 ᵒC. The influence of different pH values on the physical properties of NiO nanoparticles were investigated by a particle size analyzer (PSA), field emission scanning electron microscope (FESEM) and X-ray diffractometer (XRD). Structural analysis confirmed that a cubic structure of NiO nanoparticle was obtained without any secondary phase for NiO powders prepared with pH=1, while the peak of secondary phase (Ni) appeared for NiO powders prepared with pH= 7 and 11. Morphological observation showed that the NiO nanoparticles prepared with pH=7 and 11 tend to form more agglomerates compared to one prepared with pH=1. The average diameter of NiO nanoparticles with pH 1, 7 and 11 were approximately in the range of 19-26 nm, 21-28 nm, and 24-30 nm, respectively. NiO powder that was synthesized with pH=1 was further used to prepare composite anode of NiO Nps-BaCe0.54Zr0.36Y0.1O2.95 (BCZY) powder. Unfortunately a composite of NiO Nps-BaCeO3-BaZrO3 was obtained instead of BCZY and governed by agglomerates with size in the range of 70-300 nm.

Published
2021
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