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Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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EVision: An AI-Driven, Machine Learning and Data Analytics Enhanced Personalized Electric Vehicle Recommendation Framework

Authors: Kanishka Sharma1, Dr. Sapna Jain2;

EVision: An AI-Driven, Machine Learning and Data Analytics Enhanced Personalized Electric Vehicle Recommendation Framework

Abstract

EVision: An AI-Driven, Machine Learning and Data Analytics Enhanced Personalized Electric Vehicle Recommendation Framework. Now that the global electric vehicle market is gaining momentum, personalized recommendations are even more crucial to meet varied consumer needs and guide them toward decisions. Based on this crucial need, this paper introduces EVision: new recommendation platform that takes advantage of machine learning, data analysis, and artificial intelligence to present well-personalized EV vehicle suggestions. Overcomes All Limitations Compared to Traditional Recommendation Systems. Such a system combined a recommendation engine, data analytics for understanding real consumer needs, and an NLP-based chatbot [1], [2]. The framework thus contributes to a sustainable ecosystem of EV and aims to support the increased participation of users and decision-making. Experimental results demonstrate that EVision is effective at boosting the accuracy of suggestions and improving user satisfaction with excellent potential for future adoption of EV

Keywords

Electric Vehicles, Artificial Intelligence, Machine Learning, Recommendation Systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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