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
{"title":"Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs","authors":"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","doi":"10.32996/jbms.2024.6.2.12","DOIUrl":null,"url":null,"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.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"8 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business and Management Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32996/jbms.2024.6.2.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.