… is to assess landslide susceptibility by integrating convolutional neural network (CNN) with
… mentioned can be effectively improved by integrating the CNN technique. Therefore, the …

… Convolutional neural networks (CNNs) are the most … a single image and the integration of
information from various sources… be recognized by convolutional neural networks from a single …

… In this work we show how to integrate prior statistical … a convolutional neural network in
order to obtain robust predictions even when dealing with corrupted or noisy data. Our network …

… In this paper, we study the performance of a system that integrates a DL model as a trade-off
between the accuracy and the complexity. At the same time, we relate the complexity to the …

… Therefore, we call such an approach performance integration in this paper. The
proposed performance integration can be realised according to Eq. 2 and 3 as follows. …

Atrial fibrillation (AF) can cause a variety of heart diseases and its detection is insufficient in
outside hospital. We proposed three methods for AF diagnosis in ambulatory settings. The …

… integration convolutional neural network (ICNN) for person re-identification (re-ID) in camera
networks, … local horizontal average pooling on the convolutional maps. Afterwards, we …

… through 3D convolutional neural networks and to integrate it … by 14.7%, whereas integrating
the malignancy model itself … lung cancer datasets, integrating predictive models of nodule …

… [3] designed a multivariable hybrid deep network model which … , convolutional neural
network (CNN), long short-term memory (LSTM) network, and multilayer perceptron network. …

… representation of the integration was produced utilizing the values of the three omics of each
sample. Then, the colored 2D maps were fed into a convolutional neural network (CNN) to …