Our last model that uses unsupervised learning is again a general learning machine invented by Geoffrey Hinton and Terrance Sejnowski in the mid 1980 called Boltzmann machine. This machine is a general form of a recurrent neural network with visible nodes that receive input or provide output, and hidden notes that are not connected to the outside world directly. Such a stochastic dynamic networ...

the establishment as well as the survival of mobile ad-hoc networks relies on the cooperation of nodes for performing network operations such as routing and packet forwarding. in these networks, misbehaving nodes can severely degrade network’s performance by not cooperating in networking operations. in this paper, we study the issue of node misbehavior in packet forwarding. to counter this type...

This paper is the first analysis of caching architectures for Question Answering (QA). We introduce the novel concept of multi-layer collaborative caches, where: (a) each resource intensive QA component is allocated a distinct segment of the cache, and (b) the overall cache is transparently spread across all nodes of the distributed system. We empirically analyze the proposed architecture using...

nowadays, one of the main problems encountered in minimally invasive surgery and telesurgery is the detection of arteries in tissue. in this study, for the first time, tactile detection of an artery in tissue and distinguishing it from the tumor has been modeled by finite element method. in this modeling, three 2d models of tissue have been created: tissue, tissue including a tumor, and tissue ...

Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, paper, we propose a novel iterative structured pruning algorithm for CNNs based the recursive least squares (RLS) optimization. Our combines inver...

in this study, a three–layer artificial neural network (ann) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. a total of 455 data points were collected from 13 data sources to develop the ann model. superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of t...