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Ying Wah Teh
Researcher at Information Technology University
Publications - 16
Citations - 1268
Ying Wah Teh is an academic researcher from Information Technology University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 8, co-authored 12 publications receiving 716 citations. Previous affiliations of Ying Wah Teh include University of Malaya.
Papers
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Journal ArticleDOI
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
TL;DR: The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition, and categorise the studies into generative, discriminative and hybrid methods.
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Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Henry Friday Nweke,Henry Friday Nweke,Ying Wah Teh,Ghulam Mujtaba,Ghulam Mujtaba,Mohammed Ali Al-Garadi +5 more
TL;DR: The focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices.
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Stock market co-movement assessment using a three-phase clustering method
Saeed Aghabozorgi,Ying Wah Teh +1 more
TL;DR: A novel three-phase clustering model is proposed to categorize companies based on the similarity in the shape of their stock markets, which has good performance in efficiency and effectiveness compared to existing conventional clustering algorithms.
Journal ArticleDOI
A review of subsequence time series clustering.
TL;DR: Various state-of-the-art approaches in performing subsequence time series clustering are discussed, and the strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
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Text-Independent Speaker Identification through Feature Fusion and Deep Neural Network
Rashid Jahangir,Ying Wah Teh,Nisar Ahmed Memon,Ghulam Mujtaba,Mahdi Zareei,Uzair Ishtiaq,Muhammad Zaheer Akhtar,Ihsan Ali +7 more
TL;DR: A novel fusion of MFCC and time-based features (MFCCT) is proposed, which combines the effectiveness ofMFCC andTime-domain features to improve the accuracy of text-independent speaker identification (SI) systems.