Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs

Norun Nabi, Md Amran Hossen Pabel, Mohammad Anisur Rahman, Abu Sufian Mozumder, Md Al-Imran, Murshid Reja Sweet, Md Zahidul Islam, Mohammed Nazmul, Islam Miah, Refat Naznin, Mohammad Kawsur Sharif
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引用次数: 1

Abstract

This research examines the potential of Convolutional Neural Networks (CNNs), including VGG16, ResNet50, and InceptionV3, in predicting ecommerce profits. Emphasizing the importance of high-quality datasets, the study showcases the superior performance of CNN models over traditional algorithms, particularly noting a notable accuracy rate of 92.55% with CNN (VGG16). These results highlight deep learning's capability to extract actionable insights from complex ecommerce data, offering significant opportunities for revenue optimization and operational efficiency improvement. The conclusion underscores the need for investment in infrastructure and expertise for successful CNN integration, alongside ethical and privacy considerations. This research contributes valuable insights to the discourse on deep learning in ecommerce, offering guidance to businesses navigating the competitive global market landscape.
释放深度学习:利用 CNN 改变电子商务利润预测
本研究探讨了卷积神经网络(CNN)(包括 VGG16、ResNet50 和 InceptionV3)在预测电子商务利润方面的潜力。研究强调了高质量数据集的重要性,展示了 CNN 模型优于传统算法的性能,尤其是 CNN (VGG16) 的准确率高达 92.55%。这些结果凸显了深度学习从复杂的电子商务数据中提取可行见解的能力,为优化收入和提高运营效率提供了重要机会。结论强调,要成功整合 CNN,除了考虑道德和隐私问题外,还需要在基础设施和专业知识方面进行投资。这项研究为电子商务中的深度学习讨论提供了宝贵的见解,为企业在竞争激烈的全球市场环境中航行提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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