To address challenges encountered in traditional Elman neural networks (ENNs), such as
low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient …

Long short-term memory (LSTM) neural network is an efficient method to analyze the
nonlinear dynamical response of structures. However, once it is applied to a long-duration …

Anomaly detection is a challenging problem in science and engineering that appeals to
numerous scholars. It is of great relevance to detect anomalies and analyze their potential …

Ensemble deep learning methods have demonstrated significant improvements in
forecasting the solar panel power generation using historical time-series data. Although …

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-
GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly …

As one of the most significant components of Intelligent Transportation Systems (ITS), traffic
prediction has gained much popularity given its enormous application value in vehicular …

At present, wind power is one of the most promising clean energy sources. Due to its high
variability, accurate wind speed forecasting is an essential part of the wind power industry …

Feature selection is one of the hottest topics in the field of machine learning and data
mining. In 2016, the feature selection using forest optimization algorithm (FSFOA) was …

As network and autonomous driving technologies rapidly advance, traffic flow prediction has
become a crucial area of research. It plays a significant role in optimizing urban traffic …

With the increasing complexity of urban transportation system, serious traffic congestion
brings inconvenience to travel. It is also very difficult to predict and control traffic congestion …