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38156.pdf (2.33 MB)
ETD Abstract Container
Abstract Header
Bacteria Growth Modeling using Long-Short-Term-Memory Networks
Author Info
Shojaee, Ali, B.S.
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441
Abstract Details
Year and Degree
2021, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Abstract
Modeling of bacteria growth under different environmental conditions provides a useful tool to predict food and consumer goods safety. This study introduces a flexible, unique, and data-driven model to predict the bacteria growth under different pH conditions, using a one-to-many Long-Short-Term Memory (LSTM) model. When compared with a benchmark model the proposed model showed a good predictive power for different bacteria behaviors. In addition to its predictive ability, the model architecture is flexible and can be adapted for different bacteria behavior patterns without additional prior assumptions.
Committee
Anca Ralescu, Ph.D. (Committee Chair)
Kenneth Berman, Ph.D. (Committee Member)
Mark Maupin, Ph.D. (Committee Member)
Dan Ralescu, Ph.D. (Committee Member)
Pages
62 p.
Subject Headings
Computer Science
Keywords
Deep Learning
;
Bacterial Growth Modeling
;
LSTM
;
Gamma Model
;
Neural Networks
;
RNN
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Citations
Shojaee, A. (2021).
Bacteria Growth Modeling using Long-Short-Term-Memory Networks
[Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441
APA Style (7th edition)
Shojaee, Ali.
Bacteria Growth Modeling using Long-Short-Term-Memory Networks.
2021. University of Cincinnati, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441.
MLA Style (8th edition)
Shojaee, Ali. "Bacteria Growth Modeling using Long-Short-Term-Memory Networks." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441
Chicago Manual of Style (17th edition)
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Document number:
ucin1617105038908441
Download Count:
166
Copyright Info
© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.