Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint Subject The topic of the resource climate change; fuel efficiency; greenhouse gas emissions; machine learning Description An account of the resource Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors. Creator An entity primarily responsible for making the resource Dhyan R.; Joy H.K.; Sridevi R.; Electa Alice J.A.; Vanusha D. Source A related resource from which the described resource is derived International Journal of Environmental Impacts, Vol-7, No. 2, pp. 287-292. Publisher An entity responsible for making the resource available International Information and Engineering Technology Association Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.18280/ijei.070213" target="_blank" rel="noreferrer noopener">https://doi.org/10.18280/ijei.070213</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201631162&amp;doi=10.18280%2Fijei.070213&amp;partnerID=40&amp;md5=205c7868572c386ae4be4b41d8dbd976" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201631162&amp;doi=10.18280%2fijei.070213&amp;partnerID=40&amp;md5=205c7868572c386ae4be4b41d8dbd976</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 23982640 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dhyan R., Department of Computer Science, Christ University, Bangalore, 560029, India; Joy H.K., Department of Computer Science, Christ University, Bangalore, 560029, India; Sridevi R., Department of Computer Science, Christ University, Bangalore, 560029, India; Electa Alice J.A., Department of Electronics and Communication Engineering, KS Institute of Technology, Bangalore, 560019, India; Vanusha D., Department of Computer Science and Engineering, SRM Institute of Technology, Chennai, 603203, India