research-article

Synthetic Behavior Sequence Generation Using Generative Adversarial Networks

Authors:

Kamran Sartipi,

Norm ArcherAuthors Info & Claims

ACM Transactions on Computing for Healthcare, Volume 4, Issue 1

Article No.: 7, Pages 1 - 23

https://doi.org/10.1145/3563950

Published: 27 February 2023 Publication History

Abstract

Due to the increase in life expectancy in advanced societies leading to an increase in population age, data-driven systems are receiving more attention to support the older people by monitoring their health. Intelligent sensor networks provide the ability to monitor their activities without interfering with routine life. Data collected from smart homes can be used in a variety of data-driven analyses, including behavior prediction. Due to privacy concerns and the cost and time required to collect data, synthetic data generation methods have been considered seriously by the research community. In this article, we introduce a new Generative Adversarial Network (GAN) algorithm, namely, BehavGAN, that applies GAN to the problem of behavior sequence generation. This is achieved by learning the features of a target dataset and utilizing a new application for GANs in the simulation of older people’s behaviors. We also propose an effective reward function for GAN back-propagation by incorporating n-gram-based similarity measures in the reinforcement mechanism. We evaluate our proposed algorithm by generating a dataset of human behavior sequences. Our results show that BehavGAN is more effective in generating behavior sequences compared to MLE, LeakGAN, and the original SeqGAN algorithms in terms of both similarity and diversity of generated data. Our proposed algorithm outperforms current state-of-the-art methods when it comes to generating behavior sequences consisting of limited-space sequence tokens.

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Cited By

Sindiramutty SJhanjhi NAkbar RHussain MRay SAmsaad F(2024)Generative AI for Threat Hunting and Behaviour AnalysisUtilizing Generative AI for Cyber Defense Strategies10.4018/979-8-3693-8944-7.ch007(235-286)Online publication date: 13-Sep-2024
https://doi.org/10.4018/979-8-3693-8944-7.ch007
Xu SXu XGao HXiao F(2023)TLS-WGAN-GP: A Generative Adversarial Network Model for Data-Driven Fault Root Cause LocationIEEE Transactions on Consumer Electronics10.1109/TCE.2023.330044269:4(850-861)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3300442

Index Terms

Synthetic Behavior Sequence Generation Using Generative Adversarial Networks
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 4, Issue 1

January 2023

217 pages

EISSN:2637-8051

DOI:10.1145/3582897

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA
,
Gang Zhou
College of William and Mary, USA

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Publication History

Published: 27 February 2023

Online AM: 29 September 2022

Accepted: 29 August 2022

Revised: 26 July 2022

Received: 10 January 2022

Published in HEALTH Volume 4, Issue 1

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Cited By

Sindiramutty SJhanjhi NAkbar RHussain MRay SAmsaad F(2024)Generative AI for Threat Hunting and Behaviour AnalysisUtilizing Generative AI for Cyber Defense Strategies10.4018/979-8-3693-8944-7.ch007(235-286)Online publication date: 13-Sep-2024
https://doi.org/10.4018/979-8-3693-8944-7.ch007
Xu SXu XGao HXiao F(2023)TLS-WGAN-GP: A Generative Adversarial Network Model for Data-Driven Fault Root Cause LocationIEEE Transactions on Consumer Electronics10.1109/TCE.2023.330044269:4(850-861)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3300442

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