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Noise Filtering for Big Data Analytics (De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences)

By: Koushik Ghosh (Edited by) , Souvik Bhattacharyya (Edited by)

Extended Catalogue

Ksh 26,500.00

Format: Hardback or Cased Book

ISBN-10: 3110697092

ISBN-13: 9783110697094

Collection / Series: De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences

Collection Type: Publisher collection

Publisher: De Gruyter

Imprint: De Gruyter

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Jun 21st, 2022

Publication Status: Active

Product extent: 164 Pages

Weight: 544.00 grams

Dimensions (height x width x thickness): 17.50 x 24.60 x 1.60 cms

Product Classification / Subject(s): Data capture & analysis
Artificial intelligence

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  • Description

  • Reviews

This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.

This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.


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