Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Lamani, Dharmannaa; * | Shanthi, T.S.b | Kirubakaran, M.K.c | Roopa, R.d
Affiliations: [a] Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India | [b] Department of Computer Science, S.A.V Sahaya Thai Arts and Science (Women) College, Tirunelveli, Tamil Nadu, India | [c] Department of Artificial Intelligence and Data Science, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India | [d] Department of Computer Science & Engineering (Data Science), Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
Correspondence: [*] Corresponding author. Dharmanna Lamani, Email: dharmannalamaniamit@gmail.com.
Abstract: Accurately classifying products in e-commerce is critical for enhancing user experience, but it remains challenging due to data quality issues and the dynamic nature of product categories. Customers are increasingly relying on visual information to make informed purchasing decisions, emphasizing the importance of accurate product classification using images. In this paper, an innovative approach called SSWSO_LeNet is proposed for product image classification in e-commerce. The method involves preprocessing the input images using Region of Interest (RoI) and Adaptive Wiener Filters to improve image quality and reduce unwanted distortions. Data augmentation techniques are then applied to increase the diversity of the dataset and the robustness of the model. To address this, we propose SSWSO_LeNet, integrating Squirrel Search Algorithm (SSA) and War Strategy Optimization (WSO) with LeNet. SSA mimics southern flying squirrels’ foraging behavior to find global optima efficiently, while WSO balances exploration and exploitation stages, enhancing classification accuracy. Experimental results show SSWSO_LeNet outperforms state-of-the-art models with an impressive accuracy of 0.976, sensitivity of 0.877, and specificity of 0.857. By leveraging SSA, WSO, and LeNet, SSWSO_LeNet not only improves classification accuracy but also reduces reliance on human editors, decreasing both cost and time in e-commerce product classification.
Keywords: E-commerce, SSA, WSO, SSWSO_LeNet, product classification
DOI: 10.3233/JIFS-241682
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl