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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


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