Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study
A Mohammadshirazi, VA Kalkhorani, J Humes… - Building and …, 2022 - Elsevier
Prediction of indoor airborne pollutant concentrations can enable a smart indoor air quality
control strategy that potentially reduces building energy use and improves occupant comfort …
control strategy that potentially reduces building energy use and improves occupant comfort …
GraphTTE: Travel time estimation based on attention-spatiotemporal graphs
This letter proposes a new travel time estimation model based on graph neural network
(GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer …
(GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer …
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key
to improving traffic safety, enabling proactive measures to be taken before a crash occurs …
to improving traffic safety, enabling proactive measures to be taken before a crash occurs …
33 A Machine Learning-Based
DN El Attar Chaimae, A Manar… - … and Digital Money, 2024 - books.google.com
Nowadays, 50% of the world's population is living in cities, and by 2050, this percentage will
increase to 70%, according to a new United Nations report [1], highlighting the need for …
increase to 70%, according to a new United Nations report [1], highlighting the need for …
A Machine Learning-Based Recommendation System for Smart Mobility Trip Planning in Morocco
Smart mobility is one of the major challenges of smart cities. The growth of cities' population
and the urban extensions lead to complex urban transport issues caused by congestion …
and the urban extensions lead to complex urban transport issues caused by congestion …