A review on smart city-IoT and deep learning algorithms, challenges

V Rajyalakshmi, K Lakshmanna - International journal of …, 2022 - inderscienceonline.com
Recent improvements in the IoT are giving rise to the explosion of interconnected devices,
empowering many smart applications. IoT devices engender massive data that requires …

Spatio-attention embedded recurrent neural network for air quality prediction

Y Huang, JJC Ying, VS Tseng - Knowledge-Based Systems, 2021 - Elsevier
Predicting the air quality index (AQI) has been regarded as a critical problem for
environmental control management. Many factors over time and space may relate to the …

Gravity-inspired graph autoencoders for directed link prediction

G Salha, S Limnios, R Hennequin, VA Tran… - Proceedings of the 28th …, 2019 - dl.acm.org
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful
node embedding methods. In particular, graph AE and VAE were successfully leveraged to …

A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks

P Ferrer-Cid, JM Barcelo-Ordinas… - arXiv preprint arXiv …, 2024 - arxiv.org
The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …

Simple and effective graph autoencoders with one-hop linear models

G Salha, R Hennequin, M Vazirgiannis - … 14–18, 2020, Proceedings, Part I, 2021 - Springer
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE)
emerged as powerful node embedding methods, with promising performances on …

Micro and macro level graph modeling for graph variational auto-encoders

K Zahirnia, O Schulte, P Naddaf… - Advances in Neural …, 2022 - proceedings.neurips.cc
Generative models for graph data are an important research topic in machine learning.
Graph data comprise two levels that are typically analyzed separately: node-level properties …

Keep it simple: Graph autoencoders without graph convolutional networks

G Salha, R Hennequin, M Vazirgiannis - arXiv preprint arXiv:1910.00942, 2019 - arxiv.org
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful
node embedding methods, with promising performances on challenging tasks such as link …

Spatiotemporal air quality inference of low-cost sensor data: Evidence from multiple sensor testbeds

J Hofman, TH Do, X Qin, ER Bonet, W Philips… - … Modelling & Software, 2022 - Elsevier
Recent advances in sensor and IoT technologies allow for denser and mobile air quality
measurements. These measurements are still spatiotemporally sparse at city-level, but can …

Graph convolutional neural networks with node transition probability-based message passing and DropNode regularization

TH Do, DM Nguyen, G Bekoulis, A Munteanu… - Expert Systems with …, 2021 - Elsevier
Graph convolutional neural networks (GCNNs) have received much attention recently,
owing to their capability in handling graph-structured data. Among the existing GCNNs …

Graph-deep-learning-based inference of fine-grained air quality from mobile IoT sensors

TH Do, E Tsiligianni, X Qin, J Hofman… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices
and communication technologies to collect a large amount of data for various applications …