Machine learning empowered computer networks
Comput. Networks, 2023 This special issue explores how emerging machine learning (ML) and artificial intelligence (AI) algorithms can help computer networks become smarter.
Michela Meo +9 more
core +2 more sources
Learning temporal information for brain-computer interface using convolutional neural networks
IEEE Transactions on Neural Networks and Learning Systems, 2018 Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems.
Sakhavi, Siavash +5 more
core +2 more sources
Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks [PDF]
IEEE Access, 2022 Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as the thriving underground cyber-crime economy to support it. Over the past several years,
Ayesha S. Dina +2 more
semanticscholar +1 more source
Learning to Configure Computer Networks with Neural Algorithmic Reasoning [PDF]
Neural Information Processing Systems, 2022 We present a new method for scaling automatic configuration of computer networks. The key idea is to relax the computationally hard search problem of finding a configuration that satisfies a given specification into an approximate objective amenable to ...
Luca Beurer-Kellner +3 more
semanticscholar +1 more source
Demystifying the Transferability of Adversarial Attacks in Computer Networks [PDF]
IEEE Transactions on Network and Service Management, 2021 Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry.
Ehsan Nowroozi +4 more
semanticscholar +1 more source
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much
Zewen Li +4 more
semanticscholar +1 more source
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning [PDF]
IEEE Transactions on Medical Imaging, 2016 Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs).
Hoo-Chang Shin +8 more
semanticscholar +1 more source
Analytical Model of a Single Link of Elastic Optical Networks
IEEE Access, 2022 This article discusses and evaluates a model of resources to which multiservice traffic is offered. The initial assumption for the model is that calls of particular traffic classes are always serviced in neighboring allocation units of the resources ...
Mariusz Glabowski +2 more
doaj +1 more source
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [PDF]
International Conference on 3D Vision, 2016 Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used ...
Fausto Milletarì +2 more
semanticscholar +1 more source
Non-local Neural Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies.
X. Wang +3 more
semanticscholar +1 more source