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Ismini Psychoula
Researcher at De Montfort University
Publications - 18
Citations - 425
Ismini Psychoula is an academic researcher from De Montfort University. The author has contributed to research in topics: Information privacy & Activity recognition. The author has an hindex of 6, co-authored 17 publications receiving 193 citations.
Papers
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Book ChapterDOI
Human Activity Recognition Using Recurrent Neural Networks
Deepika Singh,Erinc Merdivan,Ismini Psychoula,Johannes Kropf,Sten Hanke,Matthieu Geist,Andreas Holzinger +6 more
TL;DR: A deep learning model that learns to classify human activities without using any prior knowledge is introduced and it is shown that the proposed approach outperforms the existing ones in terms of accuracy and performance.
Journal ArticleDOI
Security and privacy issues of physical objects in the IoT: Challenges and opportunities
TL;DR: Considering the development of IoT technologies, potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized and possible directions for dealing with these challenges are pointed out.
Proceedings ArticleDOI
Users' Privacy Concerns in IoT Based Applications
TL;DR: It is found that quite a large number of participants would still decide to have the offered IoT service if they find it useful and practical for their daily lives despite the infringement on their privacy.
Proceedings ArticleDOI
A Deep Learning Approach for Privacy Preservation in Assisted Living
Ismini Psychoula,Erinc Merdivan,Deepika Singh,Liming Chen,Feng Chen,Sten Hanke,Johannes Kropf,Andreas Holzinger,Matthieu Geist +8 more
TL;DR: This paper focuses on a Long Short Term Memory (LSTM) Encoder-Decoder, which is a principal component of deep learning, and proposes a new encoding technique that allows the creation of different AAL data views, depending on the access level of the end user and the information they require access to.
Book ChapterDOI
Human Activity Recognition using Recurrent Neural Networks
Deepika Singh,Erinc Merdivan,Ismini Psychoula,Johannes Kropf,Sten Hanke,Matthieu Geist,Andreas Holzinger +6 more
TL;DR: In this article, a deep learning model that learns to classify human activities without using any prior knowledge is introduced. And the results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.