A review on the long short-term memory model
Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …
neurocomputing fields. According to several online sources, this model has improved …
How can artificial intelligence impact sustainability: A systematic literature review
We need a proper mechanism to manage issues related to our environment, economy, and
society to proceed toward sustainability. Many researchers have worked for sustainable …
society to proceed toward sustainability. Many researchers have worked for sustainable …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Photovoltaic power forecasting based LSTM-Convolutional Network
K Wang, X Qi, H Liu - Energy, 2019 - Elsevier
The volatile and intermittent nature of solar energy itself presents a significant challenge in
integrating it into existing energy systems. Accurate photovoltaic power prediction plays an …
integrating it into existing energy systems. Accurate photovoltaic power prediction plays an …
Data science and big data analytics: A systematic review of methodologies used in the supply chain and logistics research
Data science and big data analytics (DS &BDA) methodologies and tools are used
extensively in supply chains and logistics (SC &L). However, the existing insights are …
extensively in supply chains and logistics (SC &L). However, the existing insights are …
Real-time crash risk prediction on arterials based on LSTM-CNN
Real-time crash risk prediction is expected to play a crucial role in preventing traffic
accidents. However, most existing studies only focus on freeways rather than urban arterials …
accidents. However, most existing studies only focus on freeways rather than urban arterials …
The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis
Due to the burgeoning demand for freight movement, freight related road safety threats have
been growing substantially. In spite of some research on the factors influencing freight truck …
been growing substantially. In spite of some research on the factors influencing freight truck …
Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting
The volatile behavior of solar energy is the biggest challenge in its successful integration
with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can …
with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can …
An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors
Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment.
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …