Neuro-Fuzzy Systems Modeling Tools for Bacterial Growth

El-Sebakhy, Emad Ahmed and Raharja, Putu D. and Adem, S. and Khaeruzzaman, Yaman (2007) Neuro-Fuzzy Systems Modeling Tools for Bacterial Growth. 2007 IEEE/ACS International Conference on Computer Systems and Applications.

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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.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming
500 Science and Mathematic > 570 Biology > 579 Natural History of Microorganisms, Fungi, Algae
Divisions: Fakultas Teknik Informatika > Program Studi Informatika
Depositing User: mr admin umn
Date Deposited: 13 Oct 2021 03:59
Last Modified: 13 Oct 2021 03:59

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