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https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14966
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sungheetha, Akey | - |
dc.contributor.author | Bharathi, B | - |
dc.contributor.author | Ganesan, D | - |
dc.contributor.author | Karthikeyan, T | - |
dc.contributor.author | Bindu Madhavi, N | - |
dc.contributor.author | Chairma Lakshmi, K R | - |
dc.date.accessioned | 2024-03-30T10:11:00Z | - |
dc.date.available | 2024-03-30T10:11:00Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9.79835E+12 | - |
dc.identifier.uri | https://doi.org/10.1109/RMKMATE59243.2023.10369718 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14966 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.publisher | 2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023 | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.subject | Ai | en_US |
dc.subject | Business Model | en_US |
dc.subject | Distributed Machine Learning | en_US |
dc.subject | E-Commerce | en_US |
dc.subject | Urban Areas | en_US |
dc.title | E-Commerce Business Model Analysis and Success in Urban Areas Using Ai-Distributed Machine Learning | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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