RETRACTED: Empowering modern E-commerce consumers: Optimized deep learning for product image classification in home shopping. | Marketing | Journal of Intelligent & Fuzzy Systems | EBSCOhost

Abstract

The article focuses on the development and evaluation of SSWSO LeNet, an optimized deep learning model for product image classification in e-commerce home shopping platforms. SSWSO LeNet integrates the LeNet convolutional neural network architecture with a hybrid optimization algorithm combining the Squirrel Search Algorithm (SSA) and War Strategy Optimization (WSO) to enhance classification accuracy. The methodology includes image preprocessing (Region of Interest extraction and Adaptive Wiener filtering), feature extraction (using CNN, statistical, GLCM, and PHOG features), data augmentation, and classification. Experimental results on a diverse e-commerce product image dataset demonstrate that SSWSO LeNet outperforms existing models in accuracy (0.976), sensitivity (0.877), and specificity (0.857), indicating its effectiveness for improving product categorization and user experience. The framework also shows potential adaptability to other domains such as healthcare, legal document classification, social media content moderation, computer vision, audio processing, and time series analysis.

Additional Information

• Source:Journal of Intelligent & Fuzzy Systems. 2024/11, Vol. 47, p245
• Document Type:Article
• Subject Area:Marketing
• Publication Date:2024
• ISSN:1064-1246
• DOI:10.3233/JIFS-241682
• Accession Number:181971973
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