ORCID Profile
0000-0002-3281-5955
Current Organisations
Universiti Malaya
,
Universiti Kebangsaan Malaysia
,
The Children's Hospital of Philadelphia
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Publisher: Oxford University Press (OUP)
Date: 10-2018
DOI: 10.1093/JAMIA/OCY114
Abstract: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data. We organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3) and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks. Among 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming in idual systems. Among in idual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1). Data imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (0.17632/rxwfb3tysd.1).
Publisher: Springer Science and Business Media LLC
Date: 05-11-2015
Publisher: IOP Publishing
Date: 17-06-2013
Publisher: Trans Tech Publications, Ltd.
Date: 11-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.832.149
Abstract: The source of fossil fuel is decreasing. The price increased rapidly. Population and demand of energy increased significantly over the years. Carbon pollution and global warming are becoming major issues. The best way to overcome this problem is by changing to renewable source of energy. One of it is solar thermal energy. However, a solar technology is currently still expensive, low in efficiency and takes up a lot of space. Nanofluid is recognized as a solution to overcome this problem. Due to the high thermal conductivity of nanofluids, the thermal efficiency of a solar collector can be increased and thus decreasing the size of the system. This paper analyzes the efficiency of using the Al 2 O 3 nanofluid as absorbing medium in flat-plate solar collector and estimated the potential of size reduction. When applying the same output temperature of Al 2 O 3 nanofluid as with water, it can be observed that the collectors size can be reduced up to 24% of its original size.
Publisher: Elsevier BV
Date: 12-2013
Location: United States of America
No related grants have been discovered for Sifei Han.