{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:09:50Z","timestamp":1769717390577,"version":"3.49.0"},"reference-count":19,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>The study addresses the challenges of human action recognition and analysis in computer vision, with a focus on classifying Indian dance forms. The complexity of these dance styles, including variations in body postures and hand gestures, makes classification difficult. Deep learning models require large datasets for good performance, so standard data augmentation techniques are used to increase model generalizability. The study proposes the Indian Classical Dance Generative Adversarial Network (ICD-GAN) for augmentation and the quantum-based Convolutional Neural Network (QCNN) for classification. The research consists of three phases: traditional augmentation, GAN-based augmentation, and a combination of both. The proposed QCNN is introduced to reduce computational time. Different GAN variants DC-GAN, CGAN, MFCGAN are employed for augmentation, while transfer learning-based CNN models VGG-16, VGG-19, MobileNet-v2, ResNet-50, and new QCNN are implemented for classification. The study demonstrates that GAN-based augmentation outperforms traditional methods, and QCNN reduces computational complexity while improving prediction accuracy. The proposed method achieves a precision rate of 98.7% as validated through qualitative and quantitative analysis. It provides a more effective and efficient approach compared to existing methods for Indian dance form classification.<\/jats:p>","DOI":"10.3233\/jifs-231183","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T11:15:22Z","timestamp":1689938122000},"page":"6107-6125","source":"Crossref","is-referenced-by-count":0,"title":["Generative adversarial network based data augmentation and quantum based convolution neural network for the classification of Indian classical dance forms"],"prefix":"10.1177","volume":"45","author":[{"given":"Challapalli","family":"Jhansi Rani","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Andhra Pradesh, Vaddeswaram, India"}]},{"given":"Nagaraju","family":"Devarakonda","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India"}]}],"member":"179","reference":[{"issue":"7553","key":"10.3233\/JIFS-231183_ref1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"2","key":"10.3233\/JIFS-231183_ref3","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1007\/s11063-018-09976-2","article-title":"Recent deep learning techniques, challenges and its applications for medical healthcare system: a review","volume":"50","author":"Pandey","year":"2019","journal-title":"Neural Process Lett"},{"issue":"2","key":"10.3233\/JIFS-231183_ref4","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TPAMI.2019.2932058","article-title":"Hierarchical deep click feature prediction for fine-grained image recognition","volume":"44","author":"Yu","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"10.3233\/JIFS-231183_ref5","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TNNLS.2019.2908982","article-title":"Spatial pyramid-enhanced netvlad with weighted triplet loss for place recognition","volume":"31","author":"Yu","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"11","key":"10.3233\/JIFS-231183_ref6","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"10.3233\/JIFS-231183_ref7","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11063-019-10115-8","article-title":"Traffic signs detection for realworld application of an advanced driving assisting system using deep learning","volume":"51","author":"Ayachi","year":"2020","journal-title":"Neural Process Lett"},{"issue":"7","key":"10.3233\/JIFS-231183_ref8","doi-asserted-by":"crossref","first-page":"3952","DOI":"10.1109\/TII.2018.2884211","article-title":"Multimodal face-pose estimation with multitask manifold deep learning","volume":"15","author":"Hong","year":"2018","journal-title":"IEEE Trans Industr Inf"},{"issue":"28","key":"10.3233\/JIFS-231183_ref9","doi-asserted-by":"crossref","first-page":"8258","DOI":"10.1364\/AO.57.008258","article-title":"Deep learning-based object classification through multimode fiber via a cnn-architecture specklenet","volume":"57","author":"Wang","year":"2018","journal-title":"Appl Opt"},{"issue":"2","key":"10.3233\/JIFS-231183_ref10","first-page":"100028","article-title":"Deep learning based semantic personalized recommendation system","volume":"1","author":"Harma","year":"2021","journal-title":"International Journal of Information Management Data Insights"},{"key":"10.3233\/JIFS-231183_ref11","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.neucom.2018.01.007","article-title":"Fine-grained attention mechanism for neural machine translation","volume":"284","author":"Choi","year":"2018","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-231183_ref21","doi-asserted-by":"crossref","first-page":"6","DOI":"10.5897\/JFSA2015.0031","article-title":"Bharatanatyam and Mathematics: Teaching Geometry Through Dance","volume":"5","author":"Kalpana","year":"2015","journal-title":"J Fine Studio Art"},{"key":"10.3233\/JIFS-231183_ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/6204742","article-title":"Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion","volume":"2017","author":"Kumar","year":"2017","journal-title":"Math Probl Eng"},{"key":"10.3233\/JIFS-231183_ref29","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.phpro.2012.03.160","article-title":"AdaBoost for Feature Selection, Classification and Its Relation with SVM","volume":"25","author":"Wang","year":"2012","journal-title":"A Review. 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