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.
Full text not available from this repository.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.
| 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: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 13 Oct 2021 03:59 |
| Last Modified: | 13 Oct 2021 03:59 |
| URI: | https://kc.umn.ac.id/id/eprint/18712 |
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