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020 _a9783110697216
024 7 _a10.1515/9783110697216
_2doi
035 _a(DE-B1597)546521
035 _a(OCoLC)1328137295
040 _aDE-B1597
_beng
_cDE-B1597
_erda
041 0 _aeng
044 _agw
_cDE
072 7 _aCOM004000;BISACCOM032000
_2bisacsh
082 0 4 _a004
_qDE-101
245 0 0 _aNoise Filtering for Big Data Analytics /
_ced. by Souvik Bhattacharyya, Koushik Ghosh.
264 1 _aBerlin ;
_aBoston :
_bDe Gruyter,
_c[2022]
264 4 _c©2022
300 _a1 online resource (VIII, 156 p.)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aDe Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences ,
_x2626-5427 ;
_v12
505 0 0 _tFrontmatter --
_tPreface --
_tContents --
_tAbout the Editors --
_tApplication of discrete domain wavelet filter for signal denoising --
_tSecret sharing scheme in defense and big data analytics --
_tRecent advances in digital image smoothing: A review --
_tDouble exponential smoothing and its tuning parameters: A re-exploration --
_tEffect of smoothing on big data governed by polynomial memory --
_tHeteroskedasticity in panel data: A big challenge to data filtering --
_tImportance and use of digital filters in digital image processing --
_tSmart filter and smoothing: A new approach of data denoising --
_tAcknowledgement --
_tIndex
506 0 _arestricted access
_uhttp://purl.org/coar/access_right/c_16ec
_fonline access with authorization
_2star
520 _aThis 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.
530 _aIssued also in print.
538 _aMode of access: Internet via World Wide Web.
546 _aIn English.
588 0 _aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 29. Mai 2023)
650 4 _aAngewandte Mathematik.
_964805
650 4 _aBig Data.
_94174
650 4 _aKünstliche Intelligenz.
_975263
650 4 _aMaschinelles Lernen.
_975264
650 7 _aCOMPUTERS / Information Technology.
_2bisacsh
_977386
700 1 _aAcharjee, Santanu,
_econtributor.
_4ctb
_4https://id.loc.gov/vocabulary/relators/ctb
_977387
700 1 _aBhattacharyya, Souvik,
_econtributor.
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_973560
700 1 _aBhattacharyya, Souvik,
_eeditor.
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_973560
700 1 _aChaudhuri, Dipta,
_econtributor.
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_977388
700 1 _aDawud Adebayo, Agunbiade,
_econtributor.
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_977389
700 1 _aGhosh, Koushik,
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_977390
700 1 _aGhosh, Koushik,
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_977390
700 1 _aIndu, Pabak,
_econtributor.
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_977391
700 1 _aKhan, Samarpita,
_econtributor.
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700 1 _aKhondekar, Mofazzal H.,
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700 1 _aMukherjee, Moloy,
_econtributor.
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700 1 _aNureni Olawale, Adeboye,
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700 1 _aPaul, Rimi,
_econtributor.
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700 1 _aPurkait, Souvik,
_econtributor.
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700 1 _aSaha, Gokul,
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700 1 _aSamadder, Swetadri,
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700 1 _aSengupta, Anindita,
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700 1 _aSharma, Vivek,
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_977401
700 1 _aSingh, Vijai,
_econtributor.
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_4https://id.loc.gov/vocabulary/relators/ctb
_977402
773 0 8 _iTitle is part of eBook package:
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_tDG Plus DeG Package 2022 Part 1
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