Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission
Predictive analytics utilizing machine learning algorithms play a pivotal role in various
domains, including the profiling of carbon dioxide (CO2) emissions. This research paper …
domains, including the profiling of carbon dioxide (CO2) emissions. This research paper …
Emerging trends in intelligent vehicles: The IEEE TIV perspective
This article is focused on bibliographic analysis and collaboration pattern analysis of the text
papers published in the IEEE Transactions on Intelligent Vehicles (TIV) from January 2019 …
papers published in the IEEE Transactions on Intelligent Vehicles (TIV) from January 2019 …
Short-term traffic prediction using deep learning long short-term memory: Taxonomy, applications, challenges, and future trends
This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN
G Li, H Wu, H Yang - Environmental Science and Pollution Research, 2024 - Springer
As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot
topic of discussion. Accurate carbon emissions prediction is an important foundation to …
topic of discussion. Accurate carbon emissions prediction is an important foundation to …
ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for
businesses around the world. Yet the lack of a single standardised method of measurement …
businesses around the world. Yet the lack of a single standardised method of measurement …
Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model
AH Al-Nefaie, THH Aldhyani - Sustainability, 2023 - mdpi.com
Burning fossil fuels results in emissions of carbon dioxide (CO2), which significantly
contributes to atmospheric changes and climate disturbances. Consequently, people are …
contributes to atmospheric changes and climate disturbances. Consequently, people are …
CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data
P Linardatos, V Papastefanopoulos… - Scientific reports, 2023 - nature.com
Carbon Dioxide (CO 2) is a significant contributor to greenhouse gas emissions and one of
the main drivers behind global warming and climate change. In spite of the global economic …
the main drivers behind global warming and climate change. In spite of the global economic …
A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information
A parallel supervision system is built in this paper in order to accurately estimate vehicle
emissions. Only on-board diagnostics (OBD)-independent information is used, making the …
emissions. Only on-board diagnostics (OBD)-independent information is used, making the …
A data-driven method to monitor carbon dioxide emissions of coal-fired power plants
S Zhou, H He, L Zhang, W Zhao, F Wang - Energies, 2023 - mdpi.com
Reducing CO 2 emissions from coal-fired power plants is an urgent global issue. Effective
and precise monitoring of CO 2 emissions is a prerequisite for optimizing electricity …
and precise monitoring of CO 2 emissions is a prerequisite for optimizing electricity …