Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Sevvandi Kandanaarachchi"'
Autor:
Jennifer A Flegg, Sevvandi Kandanaarachchi, Philippe J Guerin, Arjen M Dondorp, Francois H Nosten, Sabina Dahlström Otienoburu, Nick Golding
Publikováno v:
PLoS Computational Biology, Vol 20, Iss 4, p e1012017 (2024)
Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-r
Externí odkaz:
https://doaj.org/article/9ec0c0576cc24b4cb0f6d116201b66f8
Publikováno v:
Malaria Journal, Vol 22, Iss 1, Pp 1-30 (2023)
Abstract Background Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally in
Externí odkaz:
https://doaj.org/article/b051540aa382463ab42d8f8dd96e45a0
Publikováno v:
PLoS ONE, Vol 15, Iss 8, p e0236331 (2020)
This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event ext
Externí odkaz:
https://doaj.org/article/6c2ba8a7da184b879b219a99ca220438
Autor:
Catherine Leigh, Sevvandi Kandanaarachchi, James M McGree, Rob J Hyndman, Omar Alsibai, Kerrie Mengersen, Erin E Peterson
Publikováno v:
PLoS ONE, Vol 14, Iss 8, p e0215503 (2019)
Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collectio
Externí odkaz:
https://doaj.org/article/fe3a6cb8e01a42ab8a1110d961f5a31d
Publikováno v:
Hydrological Processes. 37
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.
Autor:
Priyanga Dilini Talagala, Mario A. Muñoz, Sevvandi Kandanaarachchi, Rob J. Hyndman, Kate Smith-Miles
This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary streaming time series data. We define an anomaly as an observation that is very unlikely given the recent distribution of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d2554963db84e83ecd098cd6575c447
This paper demonstrates that the performance of various outlier detection methods is sensitive to both the characteristics of the dataset, and the data normalization scheme employed. To understand these dependencies, we formally prove that normalizat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db3948de56a19e74e691cd4912bc5940
Publikováno v:
Expert Systems with Applications. 201:117073
With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. The ability of Network Anomaly Detection Systems (NADS) to identify unusual behavior makes them useful in predicting