MobileNet Model for Classifying Local Birds of Bangladesh from Image Content Using Convolutional Neural Network

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dc.contributor.authorIslam, Md. Romyull
dc.contributor.authorTasnim, Nishat
dc.contributor.authorShuvo, Shaon Bhatta
dc.date.accessioned2021-08-19T08:59:17Z
dc.date.available2021-08-19T08:59:17Z
dc.date.issued2019-12-30
dc.description.abstractTo classify bird species is quite a challenging task due to complex interdependence on various factors. There have been numerous attempts at perfecting classification. The aim of our work is to classify bird species from image data with a computer vision classification system. In this paper, we put forward a MobileNet model, which gives an amazing accuracy of up to 100%. This is the first work relating to local bird species classification. The proposed model explores a systematic approach to classification. The outcomes prove the efficiency of the model.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6011
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6011
dc.language.isoen_US
dc.publisher10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE
dc.sourceDIU Institutional Repository
dc.subjectComputational modeling
dc.subjectTask analysis
dc.subjectComputer vision
dc.subjectConvolutional neural networks
dc.subjectImage classification
dc.titleMobileNet Model for Classifying Local Birds of Bangladesh from Image Content Using Convolutional Neural Network
dc.typeArticle

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