… , a hybrid signal and image-based deep learning model is designed for AMC in CR. A
convolutional neural network (CNN) is applied in both the deep learning … a hybrid deep learning …

… This research developed a hybrid deep learning (DL) model to predict evapotranspiration
(ET) by coupling the improved Penman-Monteith (PM) method to the loss function of the DL …

… This research describes a novel hybrid semi-supervised learning architecture that enhances
the performance of supervised lesion detection models by learning from unannotated, raw …

… Therefore, in this paper, we proposed a hybrid deep learning … fed into a CNN-LSTM deep
learning model, where the long-… results show that the proposed hybrid approach can achieve …

… can lead to new hybrid methods with considerable performance. … with a focus on employing
hybrid methods for forecasting the … models and deep learning (DL) or machine learning (ML) …

… , such as machine learning, deep learning and hybrid models. This … in two paths: standalone
and hybrid models where hourly-… In terms of robustness and accuracy, the proposed hybrid …

… models, machine learning methods and non-deep neural networks, … In this study, a hybrid
deep learning neural network … , the proposed hybrid deep learning neural network outperforms …

… In this article, we propose a hybrid DL framework, called LAE-BLSTM, for efficient botnet
detection in IoT networks using LAE and deep BLSTM algorithms. The main contributions of this …

… system that employs machine and deep learning techniques and uses … the efficiency of
using Deep Learning (DL) techniques, … The hybrid model is proposed that combines the use of …

… The objective of this study is to develop a novel hybrid deep learning-… Specifically, we seek
to: (1) devise a hybrid deep learning-… In this study, we devise a hybrid deep learning modeling …