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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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期刊全稱Applications of Computational Intelligence in Biology
期刊簡稱Current Trends and O
影響因子2023Tomasz G. Smolinski,Mariofanna G. Milanova,Aboul-E
視頻videohttp://file.papertrans.cn/160/159340/159340.mp4
發(fā)行地址Presents current applications of Computational Intelligence in Biology
學(xué)科分類Studies in Computational Intelligence
圖書封面Titlebook: Applications of Computational Intelligence in Biology; Current Trends and O Tomasz G. Smolinski,Mariofanna G. Milanova,Aboul-E Book 2008 Sp
影響因子Computational Intelligence (CI) has been a tremendously active area of - search for the past decade or so. There are many successful applications of CI in many sub elds of biology, including bioinformatics, computational - nomics, protein structure prediction, or neuronal systems modeling and an- ysis. However, there still are many open problems in biology that are in d- perate need of advanced and e cient computational methodologies to deal with tremendous amounts of data that those problems are plagued by. - fortunately, biology researchers are very often unaware of the abundance of computational techniques that they could put to use to help them analyze and understand the data underlying their research inquiries. On the other hand, computational intelligence practitioners are often unfamiliar with the part- ular problems that their new, state-of-the-art algorithms could be successfully applied for. The separation between the two worlds is partially caused by the use of di erent languages in these two spheres of science, but also by the relatively small number of publications devoted solely to the purpose of fac- itating the exchange of new computational algorithms and methodolog
Pindex Book 2008
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https://doi.org/10.1007/978-981-15-9793-0uch data are called signals. Classical methods of analysing and classifying data, like decision trees, are not suitable for signals as they ignore time nature of data..We propose a novel method, called Local Classifiers, for analysing and classifying signals. The method is a genuine combination of w
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Channel Estimation in RIS-Aided Networkscate experimental results. It is now being used for exploratory analysis where users rapidly determine significant trends and features by working with visual projections of data. A basic workflow is to a) record experimental results or simulate some biological system, b) form hypotheses, c) verify h
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