Hsinchun Chen

Hsinchun Chen

Professor, Management Information Systems
Regents Professor
Member of the Graduate Faculty
Professor, BIO5 Institute
Primary Department
Contact
(520) 621-4153

Research Interest

Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.

Publications

Yang, C. C., Chen, H., Wactlar, H., Combi, C. K., & Tang, X. (2012). SHB 2012: International workshop on smart health and wellbeing. ACM International Conference Proceeding Series, 2762-2763.

Abstract:

The Smart Health and Wellbeing workshop is organized to develop a platform for authors to discuss fundamental principles, algorithms or applications of intelligent data acquisition, processing and analysis of healthcare data. We are particularly interested in information and knowledge management papers, in which the approaches are accompanied by an in-depth experimental evaluation with real world data. This paper provides an overview of the workshop and the accepted contributions. © 2012 Authors.

J., C., & Chen, H. (2008). Botnets, and the cybercriminal underground. IEEE International Conference on Intelligence and Security Informatics, 2008, IEEE ISI 2008, 206-211.

Abstract:

An underground community of cyber criminals has grown in recent years with powerful technologies capable of inflicting serious economic and infrastructural harm in the digital age. This paper serves as an introduction to the world of botnets and to the efforts of the nonprofit group "The Shad-owServer Foundation" to track them. A data mining exploration is performed on ShadowServer's datasets to investigate possible classification mechanisms for threat assessment. ©2008 IEEE.

Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems, 27(2).

Abstract:

Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We applied our analysis to estimate a discrete stock price twenty minutes after a news article was released. Using a support vector machine (SVM) derivative specially tailored for discrete numeric prediction and models containing different stock-specific variables, we show that the model containing both article terms and stock price at the time of article release had the best performance in closeness to the actual future stock price (MSE 0.04261), the same direction of price movement as the future price (57.1% directional accuracy) and the highest return using a simulated trading engine (2.06% return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.

Chen, H., Roco, M. C., Xin, L. i., & Lin, Y. (2008). Trends in nanotechnology patents. Nature Nanotechnology, 3(3), 123-125.

PMID: 18654475;Abstract:

An analysis of 30 years of data on patent publications from the US Patent and Trademark Office, the European Patent Office and the Japan Patent Office confirms the dominance of companies and selected academic institutions from the US, Europe and Japan in the commercialization of nanotechnology. © 2008 Nature Publishing Group.

Chung, W., Elhourani, T., Bonillas, A., Lai, G., Wei, X. i., & Chen, H. (2005). Supporting information seeking in multinational organizations: A knowledge portal approach. Proceedings of the Annual Hawaii International Conference on System Sciences, 272-.

Abstract:

As multinational organizations increasingly use the Web to seek information, there is a need for better support of searching the Web across different regions. However, support for Internet searching in non-English speaking regions is much weaker than that in English-speaking regions. To alleviate the problems, we propose a knowledge portal approach to supporting cross-regional searching of multinational organizations. The approach was used to build two Web portals in the Spanish business and Arabic medical domains. Experimental results show that our portals achieved significantly better performance (in terms of search accuracy and user satisfaction) than existing search engines in the corresponding domains. The encouraging findings point to a promising future of the approach to facilitating cross-regional searching in multinational organizations.