Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition

Autor: Tahmina Zebin, Patricia J. Scully, Niels Peek, Alexander J. Casson, Krikor B. Ozanyan

Publikováno v: IEEE Access, Vol 7, Pp 133509-133520 (2019)

Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these te

Externí odkaz: https://doaj.org/article/b84fe845087642618f3b8fd184209fa1

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Autor: Tahmina Zebin, Shahadate Rezvy, Wei Pang

Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative case

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::de2b4e2adf9b981eef7599b3cb373628
https://doi.org/10.21203/rs.3.rs-34534/v1

A deep learning approach for length of stay prediction in clinical settings from medical records

Autor: Thierry J. Chaussalet, Tahmina Zebin, Shahadate Rezvy

Publikováno v: CIBCB

Deep neural networks are becoming an increasingly popular solution for predictive modeling using electronic health records because of their capability of learning complex patterns and behaviors from large volumes of patient records. In this paper, we

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c933f87a987eb8a14576f99d058a8ea
https://ueaeprints.uea.ac.uk/id/eprint/73012/

Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition

Autor: Niels Peek, Krikor B. Ozanyan, Alexander J. Casson, Ertan Balaban, Tahmina Zebin

Publikováno v: BHI
Zebin, T, Balaban, E, Ozanyan, K, Casson, A & Peek, N 2019, Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition . in IEEE-EMBS BHI 2019 . IEEE-EMBS International Conference on Biomedical and Health Informatics, Chicago, United States, 19/05/19 . https://doi.org/10.1109/BHI.2019.8834480

In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6371bccc597d4e93b03426a27b6858b1
https://doi.org/10.1109/bhi.2019.8834480

Intrusion Detection and Classification with Autoencoded Deep Neural Network

Autor: Miltos Petridis, Aboubaker Lasebae, Shahadate Rezvy, Tahmina Zebin

Publikováno v: Innovative Security Solutions for Information Technology and Communications ISBN: 9783030129415
SecITC

A Network Intrusion Detection System is a critical component of every internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as denial of service attacks, malware, a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4ec3ba24bd2a281a5248f5789f0b4db
https://doi.org/10.1007/978-3-030-12942-2_12