CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks

by Renu Bhandari, Jasmeen Gill
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 3
Year of Publication: 2016
Authors: Renu Bhandari, Jasmeen Gill
10.5120/ijca2016907770

Renu Bhandari, Jasmeen Gill . An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks. International Journal of Computer Applications. 133, 3 ( January 2016), 13-16. DOI=10.5120/ijca2016907770

@article{ 10.5120/ijca2016907770,
author = { Renu Bhandari, Jasmeen Gill },
title = { An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 3 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number3/23765-2016907770/ },
doi = { 10.5120/ijca2016907770 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:39.807430+05:30
%A Renu Bhandari
%A Jasmeen Gill
%T An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 3
%P 13-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic teller machine (ATM) is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Accurate ATM forecasting for the future is one of the most important attributes to forecast because business sector, daily needs of people are highly largely dependent on this. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. Every financial institution (large or small) faces the same daily challenge. While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand. In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. In this research article an integrated BP/GA technique is proposed for accurate ATM forecasting. The results are very encouraging. The comparison of proposed technique with the previous one clarifies that the proposed model outperforms the previous models.

References
  1. Lu, C. and Shi, B. 2007.Hybrid back propagation/ GA for Multilayer Feed forward Neural Networks. Institute of Microelectronics. Tsinghua University Beijing China IEEE.
  2. Darwish, S.M. 2013.A Methodology to Improve Cash Demand Forecasting for ATM Network . International Journal of Computer and Electrical Engineering. 5(4), pp 405-409.
  3. Ghodrati, A., Abyak, H., and Sharifihosseini, A. 2013.ATM management using genetic algorithm. Management Science Letters. 3, 2007–2014.
  4. Holland, J. H. 1975.Adaptation in Natural and Artificial Systems. University of Michigan Press. Ann Arbor. MA.
  5. Ioan, I., Rotar, C. and Incze, A. 2004. The Optimization of Feedforward Neural Networks Structure using Genetic Algorithms. Proceedings of the International Conference on Theory and Applications of Mathematics and Informatics - ICTAMI 2004, Thessaloniki, Greece.
  6. Rajasekaran, S. and Vijayalakshmi P. 2004.Neural networks, Fuzzy Logic and Genetic Algorithms. New Delhi: Prentice Hall of India.
  7. Rao, H. S., Ghorpade, V. G. and Mukherjee A.2006. A genetic algorithm based back propagation network for simulation of stressstrain response of ceramic-matrix-composites. Computers and Structures. Publisher: Pergamon Press, Inc. 84, 5-6.
  8. Chamnan, R. and Chongstitvatana P.2014.Optimal Cash Management for ATM Center by Genetic Algorithm. The 29th International Technical Conference on Circuit/Systems Computers and Communications (ITC-CSCC), Phuket, Thailand.
  9. Rojas R. 1996. Neural Networks- A Systematic Introduction. published by Springer-Verlag, Berlin. 3(5), 152-187.
  10. Saad M. D. 2013.A Methodology to Improve Cash Demand Forecasting for ATM Network.International Journal of Computer and Electrical Engineering. 5, pp. 405-409.
  11. Simutis, D. Dilijonas, L. Bastina, J. Friman, and P. Drobinov.2007.Optimization of cash management for ATM network.Information Technology And Control, Kaunas, Technologija. 36(1), 117 – 121.
  12. Singh S. and Gill J. 2014.Temporal Weather Prediction using Back Propagation based Genetic Algorithm Technique. SCI journal: International Journal of Intelligent Systems and Applications (IJISA). 12, 55-61.
  13. Singh S., Bhambri P. and Gill J. 2011.Time Series based Temperature Prediction using Back Propagation with Genetic Algorithm Technique. International Journal of Computer Science Issues. 8, 5(3), 28-32.
  14. Singh S. and Gill J. 2014.An optimized Neural Network Model for Relative Humidity Prediction. International Journal of Research in Advent Technology. 2(4), 381-385.
Index Terms

Computer Science
Information Sciences

Keywords

ATM Forecasting ANN Back propagation Algorithm Genetic Algorithms Hybrid Techniques.

Powered by PhDFocusTM