… In this study, a serio-parallel CL deep learning architecture is proposed as the feature
extractor for constructing the source farm wind power prediction model. It is observed from Fig. 2 …

… embedding and deep learning for identifying SNARE proteins. Our structure is a combination
between fastText (to train vectors model) and 1D CNN (to train deep learning model from …

… analysis of seven types of deep learning models in terms of … under many different architecture
configurations and training … provide the most extensive deep learning study for time series …

… deep learning. We then describe two main components of deep learning, ie, deep learning
architectures … Subsequently, some examples are demonstrated for deep learning applications…

In this paper we address the problem of differentiating between malignant and benign tumors
based on their appearance in the CC and MLO mammography views. Classification of …

… Deep learning has helped a lot in this endeavor. This paper deals with the application of
deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically …

… This paper introduces a deep learning architecture for fully automated blood vessel …
contextual information to the deeper levels of the architecture. Deep networks may perform better if …

… In conclusion, we present a deep learning approach to leaf counting with a neural network.
Trained with examples of images and corresponding plant leaf counts, our approach can …

… Classical machine learning approaches use hand-crafted … , however of late, deep learning
approaches have shown greater … learning (ML) classifiers, algorithms, and deep learning (DL) …

… deep learning to combine PAN and MS information still to cope with land cover classification.
To the best of our knowledge, no deep Learning architecture has … As regards deep learning …