Knowledge Management System (KMS) is one way to organize and documented the knowledge of institution. The purpose of this study is to develop a prototype KMS in organizing and documenting the knowledge in the universi...
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The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range...
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A multivariate modelling may involve a set of multivariate functions. A vector valued function structure can be used to mathematically express the given problem and each multivariate function can be considered as an e...
A multivariate modelling may involve a set of multivariate functions. A vector valued function structure can be used to mathematically express the given problem and each multivariate function can be considered as an element of this vector. This work aims to construct a new approach representing the elements of this vector structure in terms of less‐variate functions to reduce the computational complexity. For this purpose, a new method based on the plain High Dimensional Model Representation (HDMR) philosophy is developed. The basic concepts of this method and several illustrative numerical implementations are given here.
Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clus...
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Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clus...
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Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.
Errors in diagnosing the disease is a critical risk that must be faced by any person giving treatment to the hospital. Medical treatment can not always be done with perfect accuracy. Lung cancer is one of the most dea...
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Artificial Immune Systems (AISs) are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the bind...
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