… Therefore, this study investigated how quickly and accurately hybrid deep learning (DL)
methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In …

… We developed several hybrid deep learning-based crop yield prediction models and … of
machine learning and deep learning algorithms for estimating crop production and built hybrid …

Recent advancements in deep learning (DL) frameworks based on deep neural networks (DNN)
drastically improved accuracy in image recognition, natural language processing and …

… Therefore, this paper proposes a hybrid deep learning (stacked auto-encoders, SAE) and …
on hybrid EMD and computational intelligent methods and on deep learning approaches. …

… used to build the hybrid deep learning model. The hybrid model consists of a set of neural
network layers, which combines SRUs and GRUs to form the deep SRUs-GRUs neural …

… a hybrid deep learning framework for shortterm traffic flow forecasting. It is built by the multilayer
integration deep learning … other traditional shallow and deep learning models for traffic …

… fusing deep learning algorithms were able to improve overall performance. The best hybrid
deep learning … and hybrid deep learning algorithms can improve the overall performances. …

… • We propose a hybrid deep learning framework for traffic flow prediction, in which GCN is
to capture the spatial relationships of traffic flow between adjacent traffic observation stations, …

… In this paper, the optimized hybrid deep learning model has been developed for waste …
Compared to the individual learners model, this proposed optimized hybrid deep learning …

… In this paper, we presented a hybrid deep learning technique that captures the spatial-spectral
features of images for the classification of distraction postures. Our architecture …