ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs
نویسندگان
چکیده
منابع مشابه
A Radon-based Convolutional Neural Network for Medical Image Retrieval
Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...
متن کاملGraph Based Convolutional Neural Network
In this paper we present a method for the application of Convolutional Neural Network (CNN) operators for use in domains which exhibit irregular spatial geometry by use of the spectral domain of a graph Laplacian, Figure 1. This allows learning of localized features in irregular domains by defining neighborhood relationships as edge weights between vertices in graph G. By formulating the domain...
متن کاملTensor graph convolutional neural network
In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs. Especially, we propose a graph preserving layer to memorize salient nodes of those factorized subgraphs, i.e. cross graph convolution and grap...
متن کاملSOM Improved Neural Network Approach for Next Page Prediction
The increasing usage of web results the heavy communication and slow returns from web. Because of this, there is the requirement of some approaches to optimize the web resources usage. One of such approach is caching that can be used within an organization to optimize the access of frequently used web pages. Caching is about to predict the requirement of next web access of a user and load it in...
متن کاملGraph Convolutional Neural Networks for ADME Prediction in Drug Discovery
ADME in-silico methods have grown increasingly powerful over the past twenty years, driven by advances in machine learning and the abundance of high-quality training data generated by laboratory automation. Meanwhile, in the technology industry, deep-learning has taken o↵, driven by advances in topology design, computation, and data. The key premise of these methods is that the model is able to...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2021
ISSN: 1424-8220
DOI: 10.3390/s21134321