Title: An optimised soft computing-based approach for multimedia data mining

Authors: M. Ravi; M. Ekambaram Naidu; G. Narsimha

Addresses: Department of CSE, JNTU Hyderabad, Hyderabad – 500085, India; Department of CSE, CMR Institute of Technology, Hyderabad – 501401, India ' SRK Institute of Technology, Vijayawada – 521108, India ' Jawaharlal Nehru Technical University, Hyderabad – 500085, India

Abstract: Multimedia mining is a sub-field of information mining which is exploited to discover fascinating data of certain information from interactive media information bases. The information mining is ordered into two general classifications, such as – static media and dynamic media. Static media possesses text and pictures, while dynamic media consists of audio and video. Multimedia mining alludes to investigation of huge measure of mixed media data so as to extricate design patterns dependent on their factual connections. Multimedia mining frameworks can find significant data or image design patterns from a colossal assortment of imageries. In this paper, a hybrid method is proposed which exploits statistical and applied soft computing-based primitives and building blocks, i.e., a novel feature engineering algorithm, aided with convolutional neural networks-based efficient modelling procedure. The optimal parameters are chosen such as – number of filters, kernel size, strides, input shape and nonlinear activation function. Experiments are performed on standard web multimedia data (here, image dataset is exploited as multimedia data) and achieved multi-class image categorisation and analysis. Our obtained results are also compared with other significant existing methods and presented in the form of an intensive comparative analysis.

Keywords: knowledge discovery; supervised learning; multimedia databases; image data; soft computing; feature engineering.

DOI: 10.1504/IJBIDM.2023.130599

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.4, pp.410 - 433

Received: 27 Jun 2021
Accepted: 16 Oct 2021
Published online: 01 May 2023 *

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