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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization Emerging Science Journal
Leow, Meng-Chew
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Computer Science & IT Electrical & Electronics Engineering Environmental Science

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SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition Ha, Nicole Yah Yie; Ong, Lee-Yeng; Leow, Meng-Chew
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-024

Abstract

Visual Speech Recognition (VSR), commonly referred to as automated lip-reading, is an emerging technology that interprets speech by visually analyzing lip movements. A challenge in VSR where visually distinct words produce similar lip movements is known as homopheme problem. Visemes are the basic visual units of speech that are produced by the lip movements and positions. Furthermore, visemes are typically having shorter durations than words. Consequently, there is less temporal information for distinguishing between different viseme classes, leading to increased visual ambiguity during classification. To address this challenge, viseme classification must not only extract lip image spatial features, but also to handle visemes of varying durations and temporal features. Therefore, this study proposed a new deep learning approach SlowFast-TCN. SlowFast network is used as the frontend architecture to extract the spatio-temporal features of the slow and fast pathways. Temporal Convolutional Network (TCN) is used as the backend architecture to learn the features from the frontend to perform the classification. A comparative ablation analysis to dissect each component of the proposed SlowFast-TCN is performed to evaluate the impact of each component. This study utilizes a benchmark dataset, Lip Reading in Wild (LRW), that focuses on English language. Two subsets of the LRW dataset, comprising of homopheme words and unique words, represent the homophemic and non-homophemic dataset, respectively. The proposed approach is evaluated on varying lighting conditions to assess its performance in real-world scenarios. It was found that illumination can significantly affect the visual data. Key performance metrics, such as accuracy and loss are used to evaluate the effectiveness of the proposed approach. The proposed approach outperforms traditional baseline models in accuracy while maintaining competitive execution time. Its dual-pathway architecture effectively captures both long-term dependencies and short-term motions, leading to better performance in both homophemic and non-homophemic datasets. However, it is less robust when dealing with non-ideal lighting scenarios, indicating the need for further enhancements to handle diverse lighting scenarios. Doi: 10.28991/ESJ-2024-08-06-024 Full Text: PDF
Analyzing Course Selection by MBTI Personality Types Goo, Cui-Ling; Leow, Meng-Chew; Ong, Lee-Yeng
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.9.1.2937

Abstract

This research project explores the relationship between course selection and Myers-Briggs Type Indicator (MBTI) personality types. It focuses on a private university’s IT Faculty students pursuing AI, BIA, BIO, DCN, and ST courses. In higher education, there is a limited understanding of the influence of personality types on course selection. This research aims to determine the statistically significant differences between courses with personality profiles. To achieve this, data collected from the survey is systematically analyzed to provide useful insights into the distribution of course selection among various personality types through descriptive analysis and inferential statistics tests, such as the Kruskal-Wallis Test. These assessments help examine the statistically significant difference between courses for each personality profile, supported by a p-value < 0.05. Descriptive analysis shows INFJ typically occurred in every course, showing the wide distribution of this personality type among students. Besides, the result shows INF_ types predominantly appear in median personalities across all courses among the participants. The majority of the participants have INTP personality types. The inferential statistical results show statistically significant differences in the distribution of courses for 8 MBTI personality types, while the remaining MBTI is not statistically significant. The results also show statistically significant differences between courses for each personality dimension. These results can be used to provide suggestions to students on course selection. Future research could expand this study by including a more diverse range of universities and courses and incorporating additional personality assessments.
A comparative study of deep learning-based network intrusion detection system with explainable artificial intelligence Kai, Tan Juan; Ong, Lee-Yeng; Leow, Meng-Chew
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4109-4119

Abstract

In the rapidly evolving landscape of cybersecurity, robust network intrusion detection systems (NIDS) are crucial to countering increasingly sophisticated cyber threats, including zero-day attacks. Deep learning approaches in NIDS offer promising improvements in intrusion detection rates and reduction of false positives. However, the inherent opacity of deep learning models presents significant challenges, hindering the understanding and trust in their decision-making processes. This study explores the efficacy of explainable artificial intelligence (XAI) techniques, specifically Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), in enhancing the transparency and trustworthiness of NIDS systems. With the implementation of TabNet architecture on the AWID3 dataset, it is able to achieve a remarkable accuracy of 99.99%. Despite this high performance, concerns regarding the interpretability of the TabNet model's decisions persist. By employing SHAP and LIME, this study aims to elucidate the intricacies of model interpretability, focusing on both global and local aspects of the TabNet model's decision-making processes. Ultimately, this study underscores the pivotal role of XAI in improving understanding and fostering trust in deep learning -based NIDS systems. The robustness of the model is also being tested by adding the signal-to-noise ratio (SNR) to the datasets.
Co-Authors Goo, Cui-Ling Ha, Nicole Yah Yie Kai, Tan Juan Ong, Lee-Yeng