… deep learning is introduced into the classification of islanding and grid disturbance for the
first time. A novel deep learning … The deep learning based method proposed in this paper can …

… slice failures, machine learning-enabled reconfigurable wireless network solutions are …
, we propose a hybrid deep learning model that consists of convolution neural network (CNN) …

… performance of the hybrid deep learning model to predict 5- and 10-min wind power
generation of the Boco Rock wind farm. The performance of the deep learning model is enhanced …

… In this paper, a novel hybrid deep learning model is proposed to improve the prediction …
generation for the Bodangora wind farm located in New South Wales, Australia. The hybrid …

… This paper presents a hybrid deep learning neural network for 24 h-ahead wind power …
power generation forecasting, this work proposes a hybrid deep learning neural network method. …

… conventional methods, machine learning … deep learning model consist of the load and
generation unit productions, while the outputs are the switching operations of the power network. It …

… we propose a hybrid deep learning approach based on convolutional neural network (CNN) …
This paper addresses the short-term prediction problem in PV power generation systems …

… , allowing for fast generation of solutions on demand. This study presents a hybrid approach
to time-frame scheduling in a high frequency domain. A GA approach is used to generate a …

… to reject incomplete network traffic that either violates the TCP/IP standard or generation
policy … used in SVM learning. Lastly, we demonstrate the effectiveness of the Enhanced SVM …

… In this section, we propose a hybrid deep learning-based collaborative filtering model
which integrates the functionalities of Bayesian stacked auto-denoising encoder (BSADE) and …