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Predicting Complex System Behavior U...

Gude Divya Sampath, Vinayaka Nagendra Harikishan.

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Predicting Complex System Behavior Using Hybrid Modeling and Computational Intelligence.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Complex System Behavior Using Hybrid Modeling and Computational Intelligence./
Author:	Gude Divya Sampath, Vinayaka Nagendra Harikishan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	132 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Contained By:	Dissertations Abstracts International82-03B.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28025986
ISBN:	9798672110431

Predicting Complex System Behavior Using Hybrid Modeling and Computational Intelligence.
Gude Divya Sampath, Vinayaka Nagendra Harikishan.

Predicting Complex System Behavior Using Hybrid Modeling and Computational Intelligence. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 132 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2020.

This item must not be sold to any third party vendors.

Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors.

ISBN: 9798672110431Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Deep learning

Predicting Complex System Behavior Using Hybrid Modeling and Computational Intelligence.
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300 $a 132 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
500 $a Advisor: Corns, Steven.
502 $a Thesis (Ph.D.)--Missouri University of Science and Technology, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors.
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653 $a Hybrid modeling
653 $a Traffic simulation
653 $a Uncertainty
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