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 …
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 …
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 …
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 …
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 …
Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G
The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the
realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in …
realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in …
A Parallel Supervision System for Vehicle 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 …
Machine-learning-based carbon dioxide concentration prediction for hybrid vehicles
The current understanding of CO2 emission concentrations in hybrid vehicles (HVs) is
limited, due to the complexity of the constant changes in their power-train sources. This …
limited, due to the complexity of the constant changes in their power-train sources. This …
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 …