3220 Slices
Medium 9781601323118

Review of Research on Biological Literature Text Mining

Hamid R. Arabnia, Quoc-Nam Tran, Mary Q. Yang, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Bioinformatics and Computational Biology | BIOCOMP'14 |


Review of Research on Biological literature text mining

Kong Meijing1,Zhang Le2,3,Wang Jun4

College of Computer and Information Science ,Southwest University, Chongqing 400715, China

Department of mathematical sciences, Michigan Technological University, Houghton, MI 49931, U.S.A.


College of Computer and Information Science, Southwest University, Chongqing, 400715, China.

1a369@swu.edu.cn, 2zhanglcq@swu.edu.cn, 4kingjun@swu.edu.cn



Abstract - Currently, the field of biomedical research is booming, a lot of biomedical knowledge in unstructured form in all forms of text file, and now it is the exponential trend increase, how to solve the contradictions between massive growth of information and knowledge of text slowly, in a credible way to find useful patterns in the text is a challenge. In recent years, biomedical text mining technology which is one branch of an efficient automatic access to new exploration-related knowledge, has great progress. This review describes the major methods and results of biomedical text mining research. namely information retrieval and literature search tool and the main aspects of biological text mining biomedical named entity recognition , text categorization, abbreviations and synonyms of the word recognition, relationship extraction, forming hypotheses and integrated framework for the above work, and finally the recent developments in the field is summarized and discussed.

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Medium 9788131805220


Manish Goyal Laxmi Publications PDF


Numerical Solution of Partial

Differential Equations


We often encounter partial differential equations in science and engineering, especially in problems involving wave phenomena, heat conduction in homogeneous solids and potential theory. The analytical treatment of these equations requires application of advanced mathematical methods. On the other hand, it is easier to produce sufficiently approximate solutions by simple and efficient numerical methods. Of the various numerical methods available for solving partial differential equations, the method of finite differences is commonly used.

In this method, the derivatives appearing in the equation and the boundary conditions are replaced by their finite difference approximations. Then the given equation is changed into a system of linear equations which are solved by iterative procedures.



Consider a linear partial differential equation of second order in two independent variables as

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Medium 9781601322623

Session - Developing Reconfigurable Heterogeneous Systems

Toomas P. Plaks (Editor) Mercury Learning and Information PDF
Medium 9781601323125

Comparison of interval and Monte Carlo simulation for uncertainty propagation in atmospheric dispersion model

Hamid R. Arabnia, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Scientific Computing | CSC'14 |


Comparison of interval and Monte Carlo simulation for uncertainty propagation in atmospheric dispersion model

El Abed El Safadi, Olivier Adrot, Jean-Marie Flaus

Laboratory: Grenoble–Sciences pour la Conception, l’Optimisation et la Production

(G-SCOP) 46, avenue Félix Viallet - 38031 Grenoble Cedex 1 France,

Email: Abed.Safadi@grenoble-inp.fr, Olivier.Adrot@grenoble-inp.fr, Jean-marie.Flaus@grenoble-inp.fr

Abstract— In this paper, the problem of tackling uncertainty propagation in the estimation of the atmospheric dispersion of a toxic gas release is analyzed in order to assess the risk at the event of an accident. This estimation is based on an effect model associated with the studied dangerous phenomenon where some input variables and model parameters are known with imprecision. Two simulation approaches,

Monte Carlo and interval analysis method, are applied and compared for estimating the confidence interval of risk intensity. Interval analysis method is superior in estimating all the possible values of intensity relative to the Monte Carlo simulation. A sensitivity analysis based on Sobol indices is applied in order to reduce the number of uncertain variables while conserving an acceptable precision of effect model.

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Medium 9781601323262

Processing NOAA Observation Data over Hybrid Computer Systems for Comparative Climate Change Analysis

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF


Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

Processing NOAA Observation Data over Hybrid

Computer Systems for Comparative Climate Change


Xuan Shi1,, Dali Wang 2

Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA


Environmental Science Division, Oak Ridge National Lab Oak Ridge TN 37831, USA


Abstract - With the rapid development of weather monitoring system, numerous observational data are available. For example, NOAA provides Global Surface Summary of Day

(GSOD) data that incorporates daily weather measurements from over 9000 weather stations around the world. In this paper, a comprehensive workflow and methodology is presented to elaborate how to transform GSOD data into a new and useful format so as to generate interpolated product of daily, monthly and annual mean surface temperature datasets by using advanced computation platforms. The quality of this gridded, high resolution (at ¼ degree) daily product is further examined by comparing to an existing global climate dataset. A preliminary comparison on global surface temperature shows a consistent agreement between these two datasets, with the major differences located in a few regions. The interpolated GSOD data products are supplementary to existing datasets by providing new gridded, high resolution observation-based daily temperature information over three decades (1982-2011), which are very useful for decadal climate change researches.

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