… In this study, we provide a classification of the studied approaches in terms of network
overlays, information structures, and network coding. To differentiate between the discussed …

… network is shown to be an efficient denoiser of seismic data contaminated by random noise
… By demonstrating that blind-spot networks are an efficient suppressor of random noise, we …

… with asphalt-based overlays since the acoustic properties of the overlay system and the …
using artificial intelligence. Artificial neural networks (ANNs) are a branch of artificial intelligence …

… artificial neural networks (ANNs) in different domains, intense research has been recently
centered on changing the networks … and complex structure of artificial neural networks (ANNs), …

… Moreover, the noise data used in this study is collected by the geophones without artificial
source excitation. We build a receiver array in Tarim Basin, according to the requirement of …

… Optical communication networks offer several metrics such as high transmission capacity, …
To satisfy the increasing demands of optical networks, effective network resource utilization …

… Gaussian and Poisson distribution noise n ″ as our train data and the noisy data y as our …
the noisy data y into the well-trained CNN model for noise attenuation. The target noisy data y …

… networks (CNNs) aims to perform image restoration by learning from training examples of
noisy-… However, the top-performing networks are generally composed of many convolutional …

… In recent years, deep learning achieves remarkable results in the field of artificial
intelligence. However, the training process of deep neural networks may cause the leakage of …

… the network predictions are fed back as input for the following steps. Extending the analysis
to artificial noisy … , as shown in Sangiorgio and Dercole [27] on noise-free artificial data. …