… This paper proposes a novel optimal hybrid strategy for building load prediction that combines
bilateral long short-term memory (BiLSTM), convolution neural networks (CNN), and grey …

… a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network
… well with noisy fitness function values provided by deep learning models, and 2) can use …

… This phenomenon benefits the deep learning models to learn the pattern of ozone generating
and dispersing processes, and to achieve better performance. Cross reference monitoring …

… The main objective of this study is to evaluate the performance of proposed deep learning
methods as potential solutions for PV power generation forecasting and the deployment of a …

… hugely impact the generation and scheduling of energy resources and efficient utilization
of renewable energy resources. This paper proposed a novel hybrid deep learning model that …

… energy profiles implemented by a hybrid deep learning model. The predicted values over the
… Thus, in this section, a hybrid deep learning model based on convolutional neural network (…

… Section 3 offers the IoTR-DS dataset creation and RPL attacks modeling. The design … hybrid
IDS deep learning-based model for detecting and classifying cyber-attacks for IoT networks …

… This section introduces the relevant hybrid deep learning approaches proposed in this …
for the RUL estimation is constructed based on deep learning methods. In the prediction stage, …

… algorithms based on mathematical optimisation and deep feedforward networks (DFNs). …
generation into their ensemble learning approach. Since this study adopts deep neural networks…

… In this work, we propose a hybrid method that comprises two major stages, namely, the
filter stage and the wrapper stage, for feature variable selection, as illustrated in Fig. 2. …