01943nas a2200253   4500000000100000000000100001008004100002260002300043653004500066653003300111653001800144653003200162100001600194700001900210700002000229700001700249700002500266700002700291245009500318856014400413490000700557520110500564020002001669       2021                            d  c2021/07//undefined10aDeep convolutional neural network (DCNN)10aIndian classical dance (ICD)10aNatya Shastra10aResidual network (ResNet50)1 aNikita Jain1 aVibhuti Bansal1 aDeepali Virmani1 aVedika Gupta1 aLorenzo Salas-Morera1 aLaura Garcia-Hernandez00aAn Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110114045&doi=10.3390%2fapp11146253&partnerID=40&md5=23f40debdaf0de660fdbbc312b85b5160 v113 aIndian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches. Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911.  a20763417 (ISSN)