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Titlebook: Applications of Computational Intelligence in Biology; Current Trends and O Tomasz G. Smolinski,Mariofanna G. Milanova,Aboul-E Book 2008 Sp

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978-3-642-09730-0Springer-Verlag Berlin Heidelberg 2008
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Statistically Based Pattern Discovery Techniques for Biological Data Analysish-risk decision making systems, a milieu familiar to many biologically-related problem domains, is the likely area of application for this technique. An analysis of the performance of this technique on a series of biologically relevant data distributions is presented, and the relative merits and weaknesses of this technique are discussed.
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Rough Sets In Data Analysis: Foundations and Applicationso the notion nor to its complement. The central tool in realizing this idea in rough sets is the relation of uncertainty based on the classical notion of indiscernibility due to Gottfried W. Leibniz: objects are indiscernible when no operator applied to each of them yields distinct values.
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Contour Matching for Fish Species Recognition and Migration Monitoringhallenges involved in the design of this system, both hardware and software, and we present results from a field test of the system at Prosser Dam in Prosser, Washington. In tests with up to four distinct species, the algorithm correctly determines the species with greater than 90 percent accuracy.
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Cell-Free MIMO Systems for UDNss. The application of integrated computational and biological approaches may help to achieve a better system-based understanding of biological processes in different environments. This will help to fully access valuable information regarding the evolution of genes and genomes in the wide diversity of organisms.
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