Shot-Net

dc.contributor.author	Foysal, Md. Ferdouse Ahmed
dc.contributor.author	Islam, Mohammad Shakirul
dc.contributor.author	Karim, Asif
dc.contributor.author	Neehal, Nafis
dc.date.accessioned	2021-11-09T07:17:22Z
dc.date.available	2021-11-09T07:17:22Z
dc.date.issued	2019-07-20
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6352
dc.description.abstract	Artificial 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.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Communications in Computer and Information Science, Springer	en_US
dc.subject	Cricket shot classification	en_US
dc.subject	Convolution neural network	en_US
dc.subject	Deep learning	en_US
dc.title	Shot-Net	en_US
dc.title.alternative	a Convolutional Neural Network for Classifying Different Cricket Shots	en_US
dc.type	Article	en_US