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dc.contributor.authorWijewantha
dc.contributor.authorCT
dc.contributor.authorPemarathne
dc.contributor.authorWPJ
dc.contributor.authorIlmini
dc.contributor.authorWMKS
dc.date.accessioned2019-11-22T12:38:52Z
dc.date.available2019-11-22T12:38:52Z
dc.date.issued2019
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/2280
dc.description.abstractElephant-human conflicts are a significant issue causing death and injury to elephants and humans. Elephants raiding human plants, unexpected humanelephant encounters as elephants cross their paths, elephants crossing railway lines are the major problems for these injuries. According to the report tabled in Department of Wild Life Conservation 279 elephant’s deaths have been reported in the last year (2018). The proposed system helps to identify elephants in day time as well as night time. This paper presents design and implementation of Deep Learning and PIR sensor-based solution to detect elephants in day time as well as night time and send a message to the user with the correct distance between elephants and train. In the implementation of deep learning model transfer learning has been applied with InceptionV3 model. The deep learning model gained 99.13% of training accuracy, 98.98% of validation accuracy and 98.86% of test accuracy with 0.0001 learning rate, 0.9 momentum and 4300 training images
dc.language.isoenen_US
dc.subjectDeep Learningen_US
dc.subjectTransfer Learningen_US
dc.subjectElephant Identificationen_US
dc.subjectPIR Fenceen_US
dc.titleReducing Frequent Killing of Elephants in Train Collisions Using Machine Learningen_US
dc.typeArticle Full Texten_US
dc.identifier.journalKDUIRC-2019en_US
dc.identifier.pgnos397-402en_US


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