Analysing neural network models for detecting panic attacks with uncertainty analysis

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Bibliographic Details
Main Authors:	Tahmid, Ahnaf, Zamil, Rafsan, Mubin, MD. Muhimenul, Mohammad, Nafis
Other Authors:	Noor, Jannatun
Format:	Thesis
Language:	English
Published:	Brac University 2024
Subjects:	Uncertainty analysis Neural network Monte-Carlo dropout Panic attack Neural networks (Computer science)
Online Access:	http://hdl.handle.net/10361/22773

id	10361-22773
record_format	dspace
spelling	10361-227732024-05-08T21:03:39Z Analysing neural network models for detecting panic attacks with uncertainty analysis Tahmid, Ahnaf Zamil, Rafsan Mubin, MD. Muhimenul Mohammad, Nafis Noor, Jannatun Department of Computer Science and Engineering, Brac University Uncertainty analysis Neural network Monte-Carlo dropout Panic attack Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-40). In our society and around the world a lot of people suffer from panic attacks. These panic attacks can be mild or very intense physical stimulations that may incapacitate an individual at the spot when the panic attack occurs. The problem in this case is, if the person suffers from a panic attack outside their house and loses control over themselves, they might be subjected to external environmental hazard such as getting into a car accident, etc. Therefore, if we can effectively track and detect whether a person had a panic attack via their spatiotemporal and biometric data, steps can be taken to help them recover from the panic attack or send help to them, as quickly as possible. Keeping this in our mind, in this study we analysed the performance of different neural network models and techniques to detect panic attacks of individuals from their spatiotemporal and biometric data. Since detection of panic attacks is an emergency use-case, model reliability is essential. To ensure model reliability, we also represented the uncertainty analysis of these neural network models using Monte Carlo Dropout. During our study, we found that among all the models that were used, GRU (Gated Recurrent Unit) had the highest accuracy of 95.56%, and GRU also had one of the least amount of uncertainty. However, the ensemble model had the least amount of uncertainty among all the models that were used. Ahnaf Tahmid Rafsan Zamil MD. Muhimenul Mubin Nafis Mohammad B.Sc. in Computer Science 2024-05-08T04:54:16Z 2024-05-08T04:54:16Z ©2024 2024-01 Thesis ID: 20101555 ID: 20101342 ID: 20101112 ID: 20101371 http://hdl.handle.net/10361/22773 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 50 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Uncertainty analysis Neural network Monte-Carlo dropout Panic attack Neural networks (Computer science)
spellingShingle	Uncertainty analysis Neural network Monte-Carlo dropout Panic attack Neural networks (Computer science) Tahmid, Ahnaf Zamil, Rafsan Mubin, MD. Muhimenul Mohammad, Nafis Analysing neural network models for detecting panic attacks with uncertainty analysis
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Noor, Jannatun
author_facet	Noor, Jannatun Tahmid, Ahnaf Zamil, Rafsan Mubin, MD. Muhimenul Mohammad, Nafis
format	Thesis
author	Tahmid, Ahnaf Zamil, Rafsan Mubin, MD. Muhimenul Mohammad, Nafis
author_sort	Tahmid, Ahnaf
title	Analysing neural network models for detecting panic attacks with uncertainty analysis
title_short	Analysing neural network models for detecting panic attacks with uncertainty analysis
title_full	Analysing neural network models for detecting panic attacks with uncertainty analysis
title_fullStr	Analysing neural network models for detecting panic attacks with uncertainty analysis
title_full_unstemmed	Analysing neural network models for detecting panic attacks with uncertainty analysis
title_sort	analysing neural network models for detecting panic attacks with uncertainty analysis
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22773
work_keys_str_mv	AT tahmidahnaf analysingneuralnetworkmodelsfordetectingpanicattackswithuncertaintyanalysis AT zamilrafsan analysingneuralnetworkmodelsfordetectingpanicattackswithuncertaintyanalysis AT mubinmdmuhimenul analysingneuralnetworkmodelsfordetectingpanicattackswithuncertaintyanalysis AT mohammadnafis analysingneuralnetworkmodelsfordetectingpanicattackswithuncertaintyanalysis
_version_	1814309233612554240

Analysing neural network models for detecting panic attacks with uncertainty analysis

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