Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14966
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dc.contributor.authorSungheetha, Akey-
dc.contributor.authorBharathi, B-
dc.contributor.authorGanesan, D-
dc.contributor.authorKarthikeyan, T-
dc.contributor.authorBindu Madhavi, N-
dc.contributor.authorChairma Lakshmi, K R-
dc.date.accessioned2024-03-30T10:11:00Z-
dc.date.available2024-03-30T10:11:00Z-
dc.date.issued2023-
dc.identifier.isbn9.79835E+12-
dc.identifier.urihttps://doi.org/10.1109/RMKMATE59243.2023.10369718-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14966-
dc.description.abstractThis study presents a comprehensive analysis of the e-commerce business model success in urban areas through the integration of AI distributed machine learning techniques. The rapid growth of e-commerce in urban settings has led to increased competition among businesses striving to capture market share. To achieve a competitive edge, companies are increasingly turning to AI and machine learning to enhance various aspects of their operations. This research examines the symbiotic relationship between e-commerce success and AI technologies, specifically focusing on distributed machine learning role in optimizing operations, personalizing user experiences, and predicting market trends. By leveraging real-world case studies and empirical data, this study sheds light on the mechanisms through which AI distributed machine learning contributes to the sustainable development of e-commerce enterprises in urban environments. The findings of this study provide valuable insights for e-commerce businesses, urban planners, and policymakers to formulate strategies that foster a conducive ecosystem for e-commerce success in urban areas. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisher2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectAien_US
dc.subjectBusiness Modelen_US
dc.subjectDistributed Machine Learningen_US
dc.subjectE-Commerceen_US
dc.subjectUrban Areasen_US
dc.titleE-Commerce Business Model Analysis and Success in Urban Areas Using Ai-Distributed Machine Learningen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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