J
Jenq-Haur Wang
Researcher at National Taipei University of Technology
Publications - 98
Citations - 1205
Jenq-Haur Wang is an academic researcher from National Taipei University of Technology. The author has contributed to research in topics: Computer science & Digital library. The author has an hindex of 15, co-authored 91 publications receiving 924 citations. Previous affiliations of Jenq-Haur Wang include National Taiwan University & Academia Sinica.
Papers
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Journal ArticleDOI
Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
Xiong Luo,Jiankun Sun,Long Wang,Weiping Wang,Wenbing Zhao,Jinsong Wu,Jenq-Haur Wang,Zijun Zhang +7 more
TL;DR: An enhanced SELM is developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance.
Proceedings ArticleDOI
Translating unknown queries with web corpora for cross-language information retrieval
TL;DR: An online translation approach to determine effective translations for unknown query terms via mining of bilingual search-result pages obtained from Web search engines is proposed.
Journal ArticleDOI
Vulnerable community identification using hate speech detection on social media
Zewdie Mossie,Jenq-Haur Wang +1 more
TL;DR: This paper proposes a hate speech detection approach to identify hatred against vulnerable minority groups on social media and can successfully identify the Tigre ethnic group as the highly vulnerable community in terms of hatred compared with Amhara and Oromo.
Proceedings Article
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings.
TL;DR: The experimental results showed that deep learning methods can effectively learn the word usage in context of social media given enough training data.
Journal ArticleDOI
User behavior prediction in social networks using weighted extreme learning machine with distribution optimization
TL;DR: A novel data-driven method is proposed using extreme learning machine (ELM) using a single layer feedforward network to enhance ELM by considering the distribution of data through the use of L 2 norm, which can achieve better performance in balanced datasets and imbalanced datasets.