… potential to effectively perform supervised classification. In … a SVM and a conventional deep
neural network. After that, in [22]… The convolution layer uses different filters to extract features …

… [12], decision trees [13], ensemble learning [14], support vector … In this paper, we introduce
a simple yet effective … guide the architecture design, Auto-CNN uses the gradient descent …

… land image processing—often called remote sensing image … Ensembles of learning algorithms
have been effectively used … for classification purposes is better than just using the image …

… representation approaches for cloud image classification: hand-… A successful approach to
overcome the high variance … apply a model average ensemble to the designed CNN model in …

… ) (or deep neural networks (DNNs)) for image classification has … into a supervised learning
model for classification purposes. … a good classifier, the features extracted from the image are …

… The purpose of this research is to design an NN ensemble … Hitherto, efforts have been
made to design ensemble by … NNs can produce a good ensemble that distribute errors over …

… There are at least two possible reasons: First, functions rep… We train BENN on the image
classification task with CNN block … To demonstrate the effectiveness of BENN, we compare our …

… neural networks, where we exploit the parallel processing of … Ensemble learning is beneficial
for several reasons 37 ; if the … blind inference and image classification accuracies of 61.14 …

… The model is based on Artificial Neural Networks and uses the linear … neural networks and
models based on a support vector machines (SVM) promote good execution, and have good …

… Adam optimization variants on image classification generally. … efficient network. The grouped
convolutions are 1 × 1 and are … For this purpose, we tested our approach on some medical …