Zobrazeno 1 - 10 of 21 pro vyhledávání: '"Jasy Liew Suet Yan"'
1
Akademický článek
Translation and trans-cultural adaptation to the Malay version of the COVID-19 vaccine hesitancy questionnaire among healthcare workers in Malaysia.
Autor: Siti Nur Aisyah Zaid, Azidah Abdul Kadir, Norhayati Mohd Noor, Basaruddin Ahmad, Muhamad Saiful Bahri Yusoff, Anis Safura Ramli, Jasy Liew Suet Yan
Publikováno v: PLoS ONE, Vol 19, Iss 4, p e0302237 (2024)
IntroductionHealthcare workers play a crucial role in supporting COVID-19 vaccination as they are the most trusted source of information to the public population. Assessing the healthcare workers' hesitancy towards COVID-19 vaccination is pertinent,
Externí odkaz: https://doaj.org/article/42d79f5368e8488badee97dc74e5e63e
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2
Akademický článek
A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers
Autor: Mohamed Fadl Idris, Je Sen Teh, Jasy Liew Suet Yan, Wei-Zhu Yeoh
Publikováno v: IEEE Access, Vol 9, Pp 104205-104216 (2021)
One of the main security requirements for symmetric-key block ciphers is resistance against differential cryptanalysis. This is commonly assessed by counting the number of active substitution boxes (S-boxes) using search algorithms or mathematical so
Externí odkaz: https://doaj.org/article/4fc812a4efc341078ddaa81c213e7f7f
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3
Akademický článek
Lightweight Block Cipher Security Evaluation Based on Machine Learning Classifiers and Active S-Boxes
Autor: Ting Rong Lee, Je Sen Teh, Norziana Jamil, Jasy Liew Suet Yan, Jiageng Chen
Publikováno v: IEEE Access, Vol 9, Pp 134052-134064 (2021)
Machine learning has recently started to gain the attention of cryptographic researchers, notably in block cipher cryptanalysis. Most of these machine learning-based approaches are black box attacks that are cipher-specific. Thus, more research is re
Externí odkaz: https://doaj.org/article/a186b00c60774dc6abd34377a4da708f
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6
Lightweight Block Cipher Security Evaluation Based on Machine Learning Classifiers and Active S-Boxes
Autor: Jasy Liew Suet Yan, Jiageng Chen, Norziana Jamil, Ting Rong Lee, Je Sen Teh
Publikováno v: IEEE Access, Vol 9, Pp 134052-134064 (2021)
Machine learning has recently started to gain the attention of cryptographic researchers, notably in block cipher cryptanalysis. Most of these machine learning-based approaches are black box attacks that are cipher-specific. Thus, more research is re
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::caded010bbf1221e1065daa1ad323a77
https://doi.org/10.1109/access.2021.3116468
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7
Fine-grained Emotion Classification: Class Imbalance Effects on Classifier Performance
Autor: Howard R. Turtle, Jasy Liew Suet Yan
Publikováno v: 2021 International Conference on Computer & Information Sciences (ICCOINS).
We explore a set of machine learning experiments in fine-grained emotion classification to test different proportion of positive and negative samples in the training data with the goal to examine if class imbalance affects classifier performance. The
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7feefdd018f3c350703a182b8e9bce1e
https://doi.org/10.1109/iccoins49721.2021.9497181
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8
Going Big and Deep: Using Convolutional Neural Network to Leverage Training Data from Multiple Domains for Cross-Domain Sentiment Classification on Product Reviews
Autor: Jasy Liew Suet Yan, Cheah Yu-N, Aditi Gupta
Publikováno v: IICAIET
Training a classifier for sentiment polarity detection in product reviews when labeled data is not available for a particular domain poses a challenge, which can be addressed through cross-domain sentiment analysis. We experimented with Convolutional
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::470ff9822629ebaa91840af13d2f3661
https://doi.org/10.1109/iicaiet49801.2020.9257815
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9
A Text Augmentation Approach using Similarity Measures based on Neural Sentence Embeddings for Emotion Classification on Microblogs
Autor: Jasy Liew Suet Yan, Yong Kuan Shyang
Publikováno v: IICAIET
Machine learning models for fine-grained emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus for training is labor-intensive and time-consuming. While distant supervision provides a viable
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::407a2ee79c47f4e0ddc0c850e0bd1a11
https://doi.org/10.1109/iicaiet49801.2020.9257826
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10
Effect of Sampling Strategies on Fine-grained Emotion Classification in Microblog Text
Autor: Howard R. Turtle, Jasy Liew Suet Yan
Publikováno v: 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS).
This study investigates the effect of diverse training samples on machine learning model performance for fine-grained emotion classification. Using four different sampling strategies (random sampling, sampling by topic and two variations of sampling
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2d1ddb47fc389ac0eb02ad5394fbfb61
https://doi.org/10.1109/aidas47888.2019.8970953
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