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 | |
| State | Published - 2007 |
Publication series
| Name | 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007 |
|---|
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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
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