Neuro-fuzzy systems modeling tools for bacterial growth

Emad A. El-Sebakhy; I. Raharja; S. Adem; Y. Khaeruzzaman

doi:10.1109/AICCSA.2007.370908

Neuro-fuzzy systems modeling tools for bacterial growth

Emad A. El-Sebakhy^*, I. Raharja, S. Adem, Y. Khaeruzzaman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Many techniques have been used in classification of bacterial growth-non-growth database are network based. This paper proposes adaptive neuro-fuzzy System for classifying the bacterial growth/non-growth and modeling the growth history. A brief description of the neuro-fuzzy intelligent systems scheme is proposed. The performance of neuro-fuzzy system is investigated for their quality and accuracy in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most common used statistics and data mining classifiers was carried out. The neuro-fuzzy system classifier was found to do better than both linear/nonlinear regression and multilayer neural networks. Results show bright future in implementing it in food science and medical industry.

Original language	English
Title of host publication	2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007
Pages	374-380
Number of pages	7
DOIs	https://doi.org/10.1109/AICCSA.2007.370908
State	Published - 2007

Publication series

Name	2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007

Keywords

Adaptive neurofuzzy system
Bacterial growth
Logistic regression
Multilayer perceptron
Support vector machines

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Signal Processing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/AICCSA.2007.370908

Cite this

El-Sebakhy, E. A., Raharja, I., Adem, S., & Khaeruzzaman, Y. (2007). Neuro-fuzzy systems modeling tools for bacterial growth. In 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007 (pp. 374-380). Article 4230983 (2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007). https://doi.org/10.1109/AICCSA.2007.370908

@inproceedings{075835f9bc604a3aae34e12df866064b,

title = "Neuro-fuzzy systems modeling tools for bacterial growth",

abstract = "Many techniques have been used in classification of bacterial growth-non-growth database are network based. This paper proposes adaptive neuro-fuzzy System for classifying the bacterial growth/non-growth and modeling the growth history. A brief description of the neuro-fuzzy intelligent systems scheme is proposed. The performance of neuro-fuzzy system is investigated for their quality and accuracy in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most common used statistics and data mining classifiers was carried out. The neuro-fuzzy system classifier was found to do better than both linear/nonlinear regression and multilayer neural networks. Results show bright future in implementing it in food science and medical industry.",

keywords = "Adaptive neurofuzzy system, Bacterial growth, Logistic regression, Multilayer perceptron, Support vector machines",

author = "El-Sebakhy, {Emad A.} and I. Raharja and S. Adem and Y. Khaeruzzaman",

year = "2007",

doi = "10.1109/AICCSA.2007.370908",

language = "English",

isbn = "1424410312",

series = "2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007",

pages = "374--380",

booktitle = "2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007",

}

Neuro-fuzzy systems modeling tools for bacterial growth. / El-Sebakhy, Emad A.; Raharja, I.; Adem, S. et al.
2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007. 2007. p. 374-380 4230983 (2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Neuro-fuzzy systems modeling tools for bacterial growth

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AU - Raharja, I.

AU - Adem, S.

AU - Khaeruzzaman, Y.

PY - 2007

Y1 - 2007

N2 - Many techniques have been used in classification of bacterial growth-non-growth database are network based. This paper proposes adaptive neuro-fuzzy System for classifying the bacterial growth/non-growth and modeling the growth history. A brief description of the neuro-fuzzy intelligent systems scheme is proposed. The performance of neuro-fuzzy system is investigated for their quality and accuracy in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most common used statistics and data mining classifiers was carried out. The neuro-fuzzy system classifier was found to do better than both linear/nonlinear regression and multilayer neural networks. Results show bright future in implementing it in food science and medical industry.

AB - Many techniques have been used in classification of bacterial growth-non-growth database are network based. This paper proposes adaptive neuro-fuzzy System for classifying the bacterial growth/non-growth and modeling the growth history. A brief description of the neuro-fuzzy intelligent systems scheme is proposed. The performance of neuro-fuzzy system is investigated for their quality and accuracy in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most common used statistics and data mining classifiers was carried out. The neuro-fuzzy system classifier was found to do better than both linear/nonlinear regression and multilayer neural networks. Results show bright future in implementing it in food science and medical industry.

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KW - Bacterial growth

KW - Logistic regression

KW - Multilayer perceptron

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SN - 9781424410316

T3 - 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007

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BT - 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007

ER -

Neuro-fuzzy systems modeling tools for bacterial growth

Abstract

Publication series

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this