Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches-Reference-Cited by-同舟云学术

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Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Published:2019-11-20 Issue:1 Volume:40 Page:
ISSN:0149-6085
Container-title:Journal of Food Safety
language:en
Short-container-title:J Food Saf

Author:

Polat Hasan¹,Topalcengiz Zeynal²^ORCID,Danyluk Michelle D.³

Affiliation:

1. Department of Electrical and Electronics Engineering, Faculty of Engineering and ArchitectureMuş Alparslan University Muş Turkey

2. Department of Food Engineering, Faculty of Engineering and ArchitectureMuş Alparslan University Muş Turkey

3. Citrus Research and Education CenterUniversity of Florida Lake Alfred Florida

Funder

Muş Alparslan Üniversitesi

Publisher

Wiley

Subject

Microbiology,Food Science,Parasitology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jfs.12733

Reference39 articles.

1. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research

2. Occurrence of generic Escherichia coli, E. coli O157 and Salmonella spp. in water and sediment from leafy green produce farms and streams on the Central California coast

3. Belattar K. &Mostefai S.(2015).Similarity measures for content‐based dermoscopic image retrieval:A comparative study. 2015 First International Conference on New Technologies of Information and Communication (NTIC) 3.https://doi.org/10.1109/NTIC.2015.7368761.

4. Supervised pattern recognition in food analysis

5. Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles

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