Soumya Dhol1
, Itu Chakraborty2
Section:Review Paper, Product Type: Journal Paper
Volume-07 ,
Issue-18 , Page no. 39-43, May-2019
Online published on May 25, 2019
Copyright © Soumya Dhol, Itu Chakraborty . 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: Soumya Dhol, Itu Chakraborty, “Artificial Neural Networks in Compute: A Review,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.39-43, 2019.
MLA Style Citation: Soumya Dhol, Itu Chakraborty "Artificial Neural Networks in Compute: A Review." International Journal of Computer Sciences and Engineering 07.18 (2019): 39-43.
APA Style Citation: Soumya Dhol, Itu Chakraborty, (2019). Artificial Neural Networks in Compute: A Review. International Journal of Computer Sciences and Engineering, 07(18), 39-43.
BibTex Style Citation:
@article{Dhol_2019,
author = {Soumya Dhol, Itu Chakraborty},
title = {Artificial Neural Networks in Compute: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {18},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {39-43},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1331},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1331
TI - Artificial Neural Networks in Compute: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Soumya Dhol, Itu Chakraborty
PY - 2019
DA - 2019/05/25
PB - IJCSE, Indore, INDIA
SP - 39-43
IS - 18
VL - 07
SN - 2347-2693
ER -
Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy.
Processing,Networks,neurons(keywords)
[1]. DAWSON, CHRISTIAN W (1998). "An artificial neural network approach to rainfall-runoff modeling". Hydrological Sciences Journal.
