dc.contributor.author |
Wijesinghe, N |
|
dc.contributor.author |
Perera, R |
|
dc.contributor.author |
Sellahewa, N |
|
dc.contributor.author |
Talagala, P |
|
dc.date.accessioned |
2023-12-29T04:57:37Z |
|
dc.date.available |
2023-12-29T04:57:37Z |
|
dc.date.issued |
2023 |
|
dc.identifier.issn |
2815-0082 |
en_US |
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/21995 |
|
dc.description.abstract |
We define an anomaly as an unlikely occurrence
that deviates from a typical behavior [1]. An anomaly
could be a defect in a production line, sudden stock
market fluctuations or natural disasters such as
deforestation, volcanic eruptions, or floods [2] [3].
The assistance of an intelligent system to identify
such disturbances would be very beneficial to
initiate methods to prevent such situations in
the early stages. This study forwards an AI based
anomaly detection system and its testing stages
primarily focused on the detection of deforestation,
where when deforestation occurs, it shows an
anomalous scenario which deviates from the
typical sights of lush green forests. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Moratuwa |
en_US |
dc.subject |
novel anomaly detection framework |
en_US |
dc.subject |
prevent deforestation |
en_US |
dc.title |
Anomaly detection in image streams with explainable AI |
en_US |
dc.type |
Article-Full-text |
en_US |
dc.identifier.year |
2023 |
en_US |
dc.identifier.journal |
Bolgoda Plains Research Magazine |
en_US |
dc.identifier.issue |
2 |
en_US |
dc.identifier.volume |
3 |
en_US |
dc.identifier.pgnos |
pp. 23-27 |
en_US |
dc.identifier.doi |
https://doi.org/10.31705/BPRM.v3(2).2023.5 |
en_US |