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[HTML][HTML] RNN-LSTM: From applications to modeling techniques and beyond—Systematic review
Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …
algorithm known for its ability to effectively analyze and process sequential data with long …
[HTML][HTML] Multi-source information fusion: Progress and future
Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …
based on modern information technology, has gained significant research value and …
Attention-based interval aided networks for data modeling of heterogeneous sampling sequences with missing values in process industry
In complex process industries, multivariate time sequences are omnipresent, whose
nonlinearities and dynamics present two major challenges for soft sensing of important …
nonlinearities and dynamics present two major challenges for soft sensing of important …
DAFA-BiLSTM: Deep autoregression feature augmented bidirectional LSTM network for time series prediction
Time series forecasting models that use the past information of exogenous or endogenous
sequences to forecast future series play an important role in the real world because most …
sequences to forecast future series play an important role in the real world because most …
[HTML][HTML] Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models
The study investigates the impact of data normalization on the prediction of electricity
consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms …
consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms …
A multi-state fusion informer integrating transfer learning for metal tube bending early wrinkling prediction
Wrinkling is one of the most fatal defects of metal tube bending, which may seriously affect
the forming quality and even lead to forming failure. Traditional wrinkling prediction methods …
the forming quality and even lead to forming failure. Traditional wrinkling prediction methods …
[HTML][HTML] A systematic survey of air quality prediction based on deep learning
The impact of air pollution on public health is substantial, and accurate long-term predictions
of air quality are crucial for early warning systems to address this issue. Air quality prediction …
of air quality are crucial for early warning systems to address this issue. Air quality prediction …
Exploring the intersection of artificial intelligence and clinical healthcare: a multidisciplinary review
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the
advantages that it brings when used, such as 24/7 availability, a very low percentage of …
advantages that it brings when used, such as 24/7 availability, a very low percentage of …
Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption
Accurate energy consumption prediction models can bring tremendous benefits to building
energy efficiency, where the use of data-driven models allows models to be trained based …
energy efficiency, where the use of data-driven models allows models to be trained based …
[HTML][HTML] Forecasting bitcoin: Decomposition aided long short-term memory based time series modeling and its explanation with Shapley values
Bitcoin price volatility fascinates both researchers and investors, studying features that
influence its movement. This paper expends on previous research and examines time series …
influence its movement. This paper expends on previous research and examines time series …