Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Mark Abraham Magumba"'
Publikováno v:
Data in Brief, Vol 54, Iss , Pp 110433- (2024)
This paper is a description of a bird vocalisation dataset containing electronic recordings of birds in Uganda. The data was collected from 7 locations namely Bwindi impenetrable forest, Kibale forest national park, Matheniko game reserve, Moroto dis
Externí odkaz:
https://doaj.org/article/18f23883ad58430cacf95d8710bdf104
Publikováno v:
Data in Brief, Vol 50, Iss , Pp 109601- (2023)
This dataset highlights some of the water quality issues in Uganda. The rationale for collecting the water samples was to test and ascertain the level and source of contamination. A total of one hundred and eighty five samples were collected from six
Externí odkaz:
https://doaj.org/article/ac37f768c0c84668b23f2b0efdaee096
Autor:
Mark Abraham Magumba, Peter Nabende
Publikováno v:
Journal of Big Data, Vol 8, Iss 1, Pp 1-17 (2021)
Abstract Twitter and social media as a whole have great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Most deployed systems employ a
Externí odkaz:
https://doaj.org/article/5b50ad04732747759b910cde2077afcf
Publikováno v:
Journal of Big Data, Vol 5, Iss 1, Pp 1-19 (2018)
Abstract This paper presents an ontology based deep learning approach for extracting disease names from Twitter messages. The approach relies on simple features obtained via conceptual representations of messages to obtain results that out-perform th
Externí odkaz:
https://doaj.org/article/66f2098fea4744d49dfc0aaa7c429dfe
Autor:
Mark Abraham Magumba, Hasifah Kasujja Namatovu, Swaib Kyanda Kaawaase, Agnes Rwashana Semwanga
Publikováno v:
Health Informatics - An International Journal. 10:1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e75737663086f4af813bcde5938c8871
https://doi.org/10.5121/hiij.2021.10301
https://doi.org/10.5121/hiij.2021.10301
There is growing interest in the rate of eHealth uptake resulting from the increased potential to advance the quality of healthcare services in both the developed and developing countries. Although the implementation of information and communication
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2659::7adcceebab1be889bd8677d719a9ae9e
https://zenodo.org/record/5493104
https://zenodo.org/record/5493104
Publikováno v:
Online Journal of Public Health Informatics
The social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale. The utility of these developments to public health use cases like disease surveillance, information dissemination, outbrea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a10ffd2cf68680a39827342177d83534
https://pubmed.ncbi.nlm.nih.gov/30349632
https://pubmed.ncbi.nlm.nih.gov/30349632
Publikováno v:
Journal of Big Data, Vol 5, Iss 1, Pp 1-19 (2018)
This paper presents an ontology based deep learning approach for extracting disease names from Twitter messages. The approach relies on simple features obtained via conceptual representations of messages to obtain results that out-perform those from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b4fcd81359f30cef7de0c6f7cfa8d10
https://doi.org/10.1186/s40537-018-0139-2
https://doi.org/10.1186/s40537-018-0139-2
Publikováno v:
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA).
Twitter and social media as a whole has great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Current methods for disease surveillance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fa6b615ad97c9fe9d18f892ff9edfe6
https://doi.org/10.1109/ciapp.2017.8167182
https://doi.org/10.1109/ciapp.2017.8167182
Autor:
Mark Abraham Magumba, Peter Nabende
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319596495
HAIS
HAIS
In this paper, we present an ontology of disease related concepts that is designated for detection of disease incidence in tweets. Unlike previous key word based systems and topic modeling approaches, our ontological approach allows us to apply more
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50cac322ee61b20d30506542065c88c0
https://doi.org/10.1007/978-3-319-59650-1_4
https://doi.org/10.1007/978-3-319-59650-1_4