Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions
IH Sarker - SN computer science, 2021 - Springer
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …
A review of artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering
R Yan, J Liao, J Yang, W Sun, M Nong, F Li - Expert Systems with …, 2021 - Elsevier
Effective air quality forecasting models are helpful for timely prevention and control of air
pollution. However, the spatiotemporal distribution characteristics of air quality have not …
pollution. However, the spatiotemporal distribution characteristics of air quality have not …
Air-pollution prediction in smart city, deep learning approach
Over the past few decades, due to human activities, industrialization, and urbanization, air
pollution has become a life-threatening factor in many countries around the world. Among …
pollution has become a life-threatening factor in many countries around the world. Among …
PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data
In recent years, air pollution has become an important public health concern. The high
concentration of fine particulate matter with diameter less than 2.5 µm (PM2. 5) is known to …
concentration of fine particulate matter with diameter less than 2.5 µm (PM2. 5) is known to …
A survey on long short-term memory networks for time series prediction
Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been
investigated intensively in recent years due to their ability to model and predict nonlinear …
investigated intensively in recent years due to their ability to model and predict nonlinear …
A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power
RA Rajagukguk, RAA Ramadhan, HJ Lee - Energies, 2020 - mdpi.com
Presently, deep learning models are an alternative solution for predicting solar energy
because of their accuracy. The present study reviews deep learning models for handling …
because of their accuracy. The present study reviews deep learning models for handling …
Evaluation of deep learning models for multi-step ahead time series prediction
R Chandra, S Goyal, R Gupta - Ieee Access, 2021 - ieeexplore.ieee.org
Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …
few decades. Given the recent deep learning revolution, there has been much attention in …
A hybrid model for spatiotemporal forecasting of PM2. 5 based on graph convolutional neural network and long short-term memory
Y Qi, Q Li, H Karimian, D Liu - Science of the Total Environment, 2019 - Elsevier
Increasing availability of data related to air quality from ground monitoring stations has
provided the chance for data mining researchers to propose sophisticated models for …
provided the chance for data mining researchers to propose sophisticated models for …
Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems
Currently, most real-world time series datasets are multivariate and are rich in dynamical
information of the underlying system. Such datasets are attracting much attention; therefore …
information of the underlying system. Such datasets are attracting much attention; therefore …