UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 7
July-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2007455


Registration ID:
235735

Page Number

1200-1203

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Title

Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

Authors

Abstract

AI strategies are as a rule generally used to build up an interruption identification framework (IDS) for distinguishing and grouping cyber attacks at the system level and the host-level in an auspicious and programmed way. In any case, numerous difficulties emerge since malevolent assaults are consistently changing and are happening in enormous volumes requiring an adaptable arrangement. There are diverse malware datasets accessible openly for additional exploration by digital security network. In any case, no current investigation has demonstrated the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets accessible freely are to be refreshed deliberately and benchmarked. In this paper, a profound neural system (DNN), a kind of profound learning model, is investigated to build up a flexible and successful IDS to distinguish and order unanticipated and erratic cyber attacks.

Key Words

CNN -Conventional Networks Neural

Cite This Article

"Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.1200-1203, July 2020, Available :http://www.jetir.org/papers/JETIR2007455.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp1200-1203, July 2020, Available at : http://www.jetir.org/papers/JETIR2007455.pdf

Publication Details

Published Paper ID: JETIR2007455
Registration ID: 235735
Published In: Volume 7 | Issue 7 | Year July-2020
DOI (Digital Object Identifier):
Page No: 1200-1203
Country: Gingee, Tamil Nadu, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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