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Complex Pattern Detection with Entropy

Ritu Sindhu1 , Neha Gehlot2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-10 , Page no. 201-205, Oct-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i10.201205

Online published on Oct 31, 2019

Copyright © Ritu Sindhu, Neha Gehlot . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Ritu Sindhu, Neha Gehlot, “Complex Pattern Detection with Entropy,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.201-205, 2019.

MLA Style Citation: Ritu Sindhu, Neha Gehlot "Complex Pattern Detection with Entropy." International Journal of Computer Sciences and Engineering 7.10 (2019): 201-205.

APA Style Citation: Ritu Sindhu, Neha Gehlot, (2019). Complex Pattern Detection with Entropy. International Journal of Computer Sciences and Engineering, 7(10), 201-205.

BibTex Style Citation:
@article{Sindhu_2019,
author = {Ritu Sindhu, Neha Gehlot},
title = {Complex Pattern Detection with Entropy},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2019},
volume = {7},
Issue = {10},
month = {10},
year = {2019},
issn = {2347-2693},
pages = {201-205},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4922},
doi = {https://doi.org/10.26438/ijcse/v7i10.201205}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.201205}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4922
TI - Complex Pattern Detection with Entropy
T2 - International Journal of Computer Sciences and Engineering
AU - Ritu Sindhu, Neha Gehlot
PY - 2019
DA - 2019/10/31
PB - IJCSE, Indore, INDIA
SP - 201-205
IS - 10
VL - 7
SN - 2347-2693
ER -

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Abstract

Pattern recognition techniques are important component of intelligent systems. It used for both data pre-processing and decision making. Broadly speaking, pattern recognition is the science that concerns the description or classification of measurements. And its algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking consideration of their statistical variation in every approach that we are taking in account. This is just opposite to pattern matching algorithms, which compare the input with pre-existing patterns for exact matches.A very common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in given text data and is included in the search capabilities

Key-Words / Index Term

Pattern Recognition,Artificial Intellegence,Fuzzy Logic,Neural Network

References

[1]. Majida Ali Abed , Ahmad Nasser Ismail andZubadiMatizHazi, “Pattern recognition UsingGenetic Algorithm”,International Journal of Computer and Electrical Engineering, Vol. 2, No. 3, June, 2010. Ahmad, T.,Jameel, [2]. A.Ahmad, “Pattern recognition using statistical and neural techniques”, International Conference onComputer Networks and Information Technology (ICCNIT), 2011.
[3] Mohammad S. Alam,Mohammad A. Karim,“Advances in Pattern Recognition Algorithms,Architectures and Devices,” Optical Engineering,Vol. 43 No. 8, August 2004.
[4] Sebastien ´ Gadat,Laurent Younes, “AStochastic Algorithm for Feature Selection in Pattern Recognition”, Journal of Machine Learning Research 8 (2007) 509-547.