In information-centric networking, accurately predicting content popularity can improve the
performance of caching. Therefore, based on software defined network (SDN), this paper …

Distributed training of deep nets is an important technique to address some of the present
day computing challenges like memory consumption and computational demands. Classical …

Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that play
important roles in many biological processes and major cancer diagnosis and treatment …

In recent years, a constant and fast information growing has characterized digital
applications in the majority of real-life scenarios. Thus, a new information asset, namely Big …

TensorFlow is an open-source software library designed for Deep Learning using dataflow
graph computation. Thanks to the flexible architecture of TensorFlow, users can deploy …

This article describes how machine learning (ML) algorithms are very useful for analysis of
data and finding some meaningful information out of them, which could be used in various …

Real-time applications such as autonomous and connected cars, surveillance, and online
learning applications have to train on streaming data. They require low-latency, high …

Deep neural networks (DNNs) achieve higher accuracy as the amount of training data
increases. However, training data such as personal medical data are often privacy sensitive …

With the recent success of deep learning, the amount of data and computation continues to
grow daily. Hence a distributed deep learning system that shares the training workload has …

Deep Learning (DL) and Big Data (BD) have converged to a hybrid computing paradigm that
merges the dynamic processing in DL models with the computational power of the …