Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots

dc.contributor.author	Foysal, Md. Ferdouse Ahmed
dc.contributor.author	Islam, Mohammad Shakirul
dc.date.accessioned	2019-07-02T04:42:20Z
dc.date.available	2019-07-02T04:42:20Z
dc.date.issued	2018-11-01
dc.identifier.uri	http://hdl.handle.net/123456789/2605
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	Daffodil International University	en_US
dc.relation.ispartofseries	;P11716
dc.subject	Computer Science	en_US
dc.subject	Artificial Intelligence	en_US
dc.subject	Data Mining	en_US
dc.subject	Neural Network	en_US
dc.subject	Machine Learning	en_US
dc.title	Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots	en_US
dc.type	Working Paper	en_US