Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Alex Chadwick"'
Publikováno v:
ACM Transactions on Embedded Computing Systems. 20:1-26
This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e0e5d6de354ee06c11133ff0ee8febd
https://doi.org/10.1145/3431815
https://doi.org/10.1145/3431815
Autor:
Alex Chadwick, John L. Fitzgerald, Karen Hay, Judith Sheridan, Haris M. Haqqani, Andrew Burke
Publikováno v:
Heart, Lung and Circulation. 29:1476-1483
Background Patients with serious mental illness (SMI) have an increased risk of sudden death. Higher rates of signal-averaged electrocardiogram (SAECG) abnormal late potentials (LP), which may be a predictor of sudden death risk, have been shown in p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3c5ebda744b1bafcb35f0e3cbf35359
https://doi.org/10.1016/j.hlc.2020.02.012
https://doi.org/10.1016/j.hlc.2020.02.012
Publikováno v:
Internal Medicine Journal. 48:433-438
Background People with severe mental illness (SMI) have a lower life expectancy in part due to a higher prevalence of cardiac and metabolic disease. Less is known of the prevalence of respiratory disease in this group. Aims This cross sectional, obse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5bd8acfd119a008dcc870b51c6bd2dbf
https://doi.org/10.1111/imj.13594
https://doi.org/10.1111/imj.13594
Publikováno v:
IML
Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract important information to help advance healthcare, make our cities smarter, and innovate in smart home technology. Deep convolutional neura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f080dcc7d3bae6e1857562db4dc9eb2e
https://doi.org/10.1145/3109761.3109804
https://doi.org/10.1145/3109761.3109804