Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The collection of studies explores the application of
advanced machine learning techniques to enhance the accuracy
of CO2 emission predictions from vehicles, with a focus on
promoting sustainable transportation practices. By employing
ensemble methods, dynamic adjustment models, and personalized
algorithms, researchers have developed frameworks that adapt to
real-time driving conditions and individual vehicle characteristics. The integration of electric vehicle (EV) data and mobile applications further supports real-time emission tracking and user
engagement. Additionally, hybrid models that consider urban
dynamics and public transport options provide comprehensive
insights into emissions in smart city environments. The findings
underscore the potential of these innovative approaches to inform
urban planning, reduce carbon footprints, and foster eco-friendly
behaviors among users.
"A SURVEY ON CARBON FOOTPRINT DETECTION", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 2, page no.a265-a270, February-2025, Available :http://www.ijrti.org/papers/IJRTI2502031.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator