Shot-Net

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dc.contributor.authorFoysal, Md. Ferdouse Ahmed
dc.contributor.authorIslam, Mohammad Shakirul
dc.contributor.authorKarim, Asif
dc.contributor.authorNeehal, Nafis
dc.date.accessioned2021-11-09T07:17:22Z
dc.date.available2021-11-09T07:17:22Z
dc.date.issued2019-07-20
dc.description.abstractArtificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different Machine Learning and Computer Vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as “Shot-Net” in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6352
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6352
dc.language.isoen_US
dc.publisherCommunications in Computer and Information Science, Springer
dc.sourceDIU Institutional Repository
dc.subjectCricket shot classification
dc.subjectConvolution neural network
dc.subjectDeep learning
dc.titleShot-Net
dc.title.alternativea Convolutional Neural Network for Classifying Different Cricket Shots
dc.typeArticle

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