Due to the complexity of predicting future earthquakes, machine learning algorithms have been used by several researchers increase Accuracy forecast. However, concentration previous studies has chiefly on temporal rather than spatial parameters. Additionally, less correlated variables were typically eliminated in feature analysis and did not enter model. This study introduces investigates effec...

Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of enhances chances recovery, which helps reduce mortality. It worth noting that X-rays are one most important diagnostic tools for diagnosing pneumonia. Chest X-ray widely used in diagnosis many lung diseases ...

Abstract In recent years, deep Recurrent Neural Network (RNN) has been applied to predict daily runoff, as its wonderful ability of dealing with the high nonlinear interactions among complex hydrology factors. However, most existing studies focused on model structure and computational load, without considering impact from selection multiple input variables prediction. This article presents a st...

In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward network estimation of the conditional mean, building work Farrell et al. (2021), explore latent patterns cross-section. use proposed estimators to forecast progression new COVID-19 cases across G7 countries during pandemic. find significant forecasting...

Over the past years, numerous studies demonstrated vulnerability of deep neural networks (DNNs) to make correct classifications in presence small noise. This motivated formal analysis DNNs ensure they delineate acceptable behavior. However, case DNN’s behavior is unacceptable for desired application, these qualitative approaches are ill-equipped determine precise degree which DNN behaves unacce...

Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...

We develop a technique using deep for human facial expression recognition. Images of preprocessed with photometric normalization manipulation to remove illumination variance. F then extracted by convolving each preprocessed Gabor filters. Kernel PCA is applied to feature them into the deep neural network that consists o hidden layers and a softmax classifier. The deep n using greedy layer-wise ...