Rahman, YeaminurMahfuza, RezwanaHai, Md. Abdul2026-07-092026-07-092021-01-01Y. Rahman, R. Mahfuza and M. A. Hai, "An Efficient Sports Classification Technique Incorporating CNN Transfer Learning Models," 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 2021, pp. 649-653, doi: 10.1109/ISPCC53510.2021.9609380.2643861597816654255202-s2.0-85123008816https://hdl.handle.net/10361/28496Data must be accessible and transferable within time constraints; yet, due to the massive increase of sophisticated data in recent years, categorizing data appropriately has become increasingly challenging. Furthermore, with the enormous popularity of many sports genres in recent times, it has become imperative to categorize the data and improve the user's search experience through the internet or other media with traditional machine learning approaches. This paper aims to develop an effective approach with deep learning techniques for classifying eight distinct forms of sports using a real-world data set derived from diverse sports videos. To identify the most suitable model, a comparison of four notable transfer learning models of convolutional neural networks, VGG16, VGG19, DenseNet2Ol, and InceptionV3, was performed with DenseNet2Ol yielding the most promising outcome of 99.08%. In addition, users can upload a sports video, and the sports-related tags will be generated automatically by a web application developing a magnificent recommendation process in the suggested system model.649-653en-USfalseCNNDenseNet2OlInceptionV3Sports classificationVGG16VGG19Web applicationSports records--Databases.Web databases.Conference Proceeding10.1109/ISPCC53510.2021.9609380