With the rapid development of machine learning, a possibility is provided for high-precision
prediction of time-series. This paper proposes a new unit which is called New Gate Control …

Daily hydrological modelling is among the most challenging tasks in water resource
management, particularly in terms of streamflow prediction in semi-arid areas. Various …

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency
and cost-effective nature. From this signal, several biomarkers can be collected, such as …

As an extensively used model for time series prediction, the Long–Short Term Memory
(LSTM) neural network suffers from shortcomings such as high computational cost and large …

In this paper, a new hybrid time series forecasting model based on the complete ensemble
empirical mode decomposition with adaptive noise (CEEMDAN) and a temporal …

This review explores the application of convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) in food safety detection and risk prediction. This paper highlights …

The prediction of remaining useful life (RUL) is one of the important measures to ensure the
safety and reliability of mechanical equipment. Aiming at the low accuracy of residual life …

Solar energy is one of the world's clean and renewable source of energy and it is an
alternative power with the ability to serve a greater proportion of rising demand needs. The …

Abstract Gated Recurrent Neural Networks (RNNs) such as LSTM and GRU have been
highly effective in handling sequential time series data in recent years. Although Gated …

In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural
Networks) to learn and represent nonlinear integral operators that appear in nonlinear …