Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review
The goal of managing smart cities and societies is to maximize the efficient use of finite
resources while enhancing the quality of life. To establish a sustainable urban existence …
resources while enhancing the quality of life. To establish a sustainable urban existence …
Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media
The ability to explain why the model produced results in such a way is an important problem,
especially in the medical domain. Model explainability is important for building trust by …
especially in the medical domain. Model explainability is important for building trust by …
FedStack: Personalized activity monitoring using stacked federated learning
Recent advances in remote patient monitoring (RPM) systems can recognize various human
activities to measure vital signs, including subtle motions from superficial vessels. There is a …
activities to measure vital signs, including subtle motions from superficial vessels. There is a …
Graph neural networks for road safety modeling: datasets and evaluations for accident analysis
We consider the problem of traffic accident analysis on a road network based on road
network connections and traffic volume. Previous works have designed various deep …
network connections and traffic volume. Previous works have designed various deep …
St-gat: A spatio-temporal graph attention network for accurate traffic speed prediction
Spatio-temporal models, which combine GNNs (Graph Neural Networks) and RNNs
(Recurrent Neural Networks), have shown state-of-the-art accuracy in traffic speed …
(Recurrent Neural Networks), have shown state-of-the-art accuracy in traffic speed …
ASNN-FRR: A traffic-aware neural network for fastest route recommendation
C Wang, C Li, H Huang, J Qiu, J Qu, L Yin - GeoInformatica, 2023 - Springer
Fastest route recommendation (FRR) is an important task in urban computing. Despite some
efforts are made to integrate A∗ algorithm with neural networks to learn cost functions by a …
efforts are made to integrate A∗ algorithm with neural networks to learn cost functions by a …
[PDF][PDF] Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities.
Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities
that helps in reducing traffic congestion and enhancing traffic quality. With the help of big …
that helps in reducing traffic congestion and enhancing traffic quality. With the help of big …
Learning to effectively model spatial-temporal heterogeneity for traffic flow forecasting
M Xu, X Li, F Wang, JS Shang, T Chong, W Cheng… - World Wide Web, 2023 - Springer
Traffic forecasting is crucial for location-based services. Recent studies tend to utilize
dynamic graph neural networks to capture spatial-temporal correlations. However, urban …
dynamic graph neural networks to capture spatial-temporal correlations. However, urban …
[HTML][HTML] Hybrid deep learning-based traffic congestion control in IoT environment using enhanced arithmetic optimization technique
Abstract The Internet of Things (IoT) is essential in several Internet application areas and
remains a key technology for communication technologies. Shorter delays in transmission …
remains a key technology for communication technologies. Shorter delays in transmission …
Spatio-Temporal Feature Engineering for Deep Learning Models in Traffic Flow Forecasting
H Mu, N Aljeri, A Boukerche - IEEE Access, 2024 - ieeexplore.ieee.org
In the past decade, modern transportation systems have employed various cutting-edge
deep-learning approaches for traffic flow prediction. Due to its significant temporal …
deep-learning approaches for traffic flow prediction. Due to its significant temporal …