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Neural networks and artificial intelligence

Authors:

V. K. SondakAuthors Info & Claims

SIGCSE '89: Proceedings of the twentieth SIGCSE technical symposium on Computer science education

Pages 241 - 245

https://doi.org/10.1145/65293.71221

Published: 01 February 1989 Publication History

Abstract

Neural networks have been called “more important than the atomic bomb” and have received a major funding commitment from DARPA. Nevertheless, it is difficult to find even a mention of neural network concepts and applications in many computer science or information systems curricula. In fact, few computer science or information systems faculty are aware of the profound implications of neurocomputing on the future of their field. This paper contends that neural networks must be a significant part of any artificial intelligence course. It illustrates how neural network concepts can be integrated into traditional artificial intelligence course material. Two programming packages for simulating neural networks on personal computers are recommended.

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Cited By

van Assen MMuscogiuri ETessarin GDe Cecco C(2022)Artificial Intelligence: A Century-Old StoryArtificial Intelligence in Cardiothoracic Imaging10.1007/978-3-030-92087-6_1(3-13)Online publication date: 22-Apr-2022
https://doi.org/10.1007/978-3-030-92087-6_1
Lu JShen JYan WBačić B(2018)An Empirical Study for Human Behavior AnalysisComputer Vision10.4018/978-1-5225-5204-8.ch095(2166-2183)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5204-8.ch095
Day MLin Y(2017)Deep Learning for Sentiment Analysis on Google Play Consumer Review2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.79(382-388)Online publication date: Aug-2017
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Index Terms

Neural networks and artificial intelligence
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Model curricula

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Published In

cover image ACM Conferences

SIGCSE '89: Proceedings of the twentieth SIGCSE technical symposium on Computer science education

February 1989

309 pages

ISBN:0897912985

DOI:10.1145/65293

Editors:
Robert A. Barrett
Indiana Univ.:8NPurdue Univ. at Fort Wayne, Fort Wayne
,
Maynard J. Mansfield
Indiana Univ.-8NPurdue Univ. at Fort Wayne, Fort Wayne

ACM SIGCSE Bulletin Volume 21, Issue 1
Feb. 1989
282 pages
ISSN:0097-8418
DOI:10.1145/65294
Editor:
James E. Miller
Univ. of Southern Mississippi, Hattiesburg
Issue’s Table of Contents

Copyright © 1989 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 1989

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SIGCSE89: 1989 SIGCSE Technical Symposium

February 23 - 24, 1989

Kentucky, Louisville, USA

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Cited By

van Assen MMuscogiuri ETessarin GDe Cecco C(2022)Artificial Intelligence: A Century-Old StoryArtificial Intelligence in Cardiothoracic Imaging10.1007/978-3-030-92087-6_1(3-13)Online publication date: 22-Apr-2022
https://doi.org/10.1007/978-3-030-92087-6_1
Lu JShen JYan WBačić B(2018)An Empirical Study for Human Behavior AnalysisComputer Vision10.4018/978-1-5225-5204-8.ch095(2166-2183)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5204-8.ch095
Day MLin Y(2017)Deep Learning for Sentiment Analysis on Google Play Consumer Review2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.79(382-388)Online publication date: Aug-2017
https://doi.org/10.1109/IRI.2017.79
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https://dl.acm.org/doi/10.1145/2980179.2982433
Cao CRen ZSchissler CManocha DZhou K(2016)Interactive sound propagation with bidirectional path tracingACM Transactions on Graphics10.1145/2980179.298243135:6(1-11)Online publication date: 5-Dec-2016
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Serrano AGutierrez DMyszkowski KSeidel HMasia B(2016)An intuitive control space for material appearanceACM Transactions on Graphics10.1145/2980179.298024235:6(1-12)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/2980179.2980242
Yan FNan LWonka P(2016)Block assembly for global registration of building scansACM Transactions on Graphics10.1145/2980179.298024135:6(1-11)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/2980179.2980241
Yen SLee Y(2009)Cluster-based under-sampling approaches for imbalanced data distributionsExpert Systems with Applications: An International Journal10.1016/j.eswa.2008.06.10836:3(5718-5727)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1016/j.eswa.2008.06.108
Newman TBarker BWyglinski AAgah AEvans JMinden G(2007)Cognitive engine implementation for wireless multicarrier transceiversWireless Communications and Mobile Computing10.1002/wcm.4867:9(1129-1142)Online publication date: 17-May-2007
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Yen SLee YLin CYing J(2006)Investigating the Effect of Sampling Methods for Imbalanced Data Distributions2006 IEEE International Conference on Systems, Man and Cybernetics10.1109/ICSMC.2006.384787(4163-4168)Online publication date: Oct-2006
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