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Stanley HE

As seen in: PeerJ
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Articles by Stanley HE

Emotional drivers of misinformation consumption and toxicity on YouTube

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By Stanley HE, Lee S-I, Mohammad SM, Di Marco N
| PeerJ
In this study, we investigate the emotional content of toxic language produced by two opposing online communities: misinformation-prone and mainstream-prone users. The online debate is driven by an emotionally and politically polarizing topic–COVID-19.
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Evidence of an upper entrainment limit for walking with fractal auditory stimuli

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By Stanley HE, Mitchell SL, Taylor WR, Ravi DK
| PeerJ
Evidence of an upper entrainment limit for walking with fractal auditory stimuli 1School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada 2School of Allied Health Professions, Keele University, Keele, Staffordshire, United Kingdom DOI 10.7717/peerj.20176 Academic Editor Marcus Vieira Subject Areas Kinesiology, Psychiatry and Psychology, Biomechanics, Sports Medicine Keywords Gait analysis, Gait variability, Detrended fluctuation analysis, Fractal scaling index,...
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A progressive attention-based cross-modal fusion network for cardiovascular disease detection using synchronized electrocardiogram and phonocardiogram signals

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By Mark RG, Stanley HE, Roy AG, Juan RA
| PeerJ
A progressive attention-based cross-modal fusion network for cardiovascular disease detection using synchronized electrocardiogram and phonocardiogram signals 1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia 2Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia 3School of Instrument Science and Engineering, Southeast University, Nanjing, JiangSu, China 4Department of...
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Vision-based approach to knee osteoarthritis and Parkinson’s disease detection utilizing human gait patterns

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By El Maachi I, Mark RG, Stanley HE, Howard AG
| PeerJ
Vision-based approach to knee osteoarthritis and Parkinson’s disease detection utilizing human gait patterns 1Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, Pakistan 2Department of Data Science, Duksung Women’s University, Seoul, Republic of South Korea 3Department of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan 4Department of Software, Sejong University, Seoul, Republic of South Korea...
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TurkMedNLI: a Turkish medical natural language inference dataset through large language model based translation

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By De Oliveira L, Kim SS, Mark RG, Stanley HE
| PeerJ
Natural language inference (NLI) is a fundamental task in natural language processing (NLP) that involves determining the relationship between two sentences named premise and hypothesis. These relationships are typically classified as entailment, contradiction, or neutrality.
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Sleep stage prediction using multimodal body network and circadian rhythm

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By Jackson CL, Mark RG, Stanley HE, Albert SM
| PeerJ
Sleep is a crucial part of the human growth and healing process and typically takes one third of our life (Colten, Altevogt & Institute of Medicine (US) Committee on Sleep Medicine and Research, 2006). Scientists recommend 7 or more hours of sleep for adults, and this range of brackets varies with age groups (Hirshkowitz et al., 2015).
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The application of the propensity score matching method in stock prediction among stocks within the same industry

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By Shengnan Li, Kim HY, Stanley HE, Lei Xue
| PeerJ
The stock market is an indispensable component of the modern socio-economic landscape and serves as a crucial avenue for contemporary businesses to raise capital. The price of stocks, formed during public market transactions, is subject to fluctuations influenced by market supply and demand dynamics.
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Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

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By Junhui Gao, Lloyd RM, Mark RG, Stanley HE
| PeerJ
Sleep staging is a critical part of sleep medicine as it involves classifying sleep into distinct stages using EEG signals, which enables the evaluation of sleep quality and the identification of sleep disorders. The application of deep learning techniques to automate sleep staging has gained significant attention in recent times owing to its potential to enhance the accuracy and efficiency of the process.
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