23,207 research outputs found
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Self-Modeling Based Diagnosis of Software-Defined Networks
Networks built using SDN (Software-Defined Networks) and NFV (Network
Functions Virtualization) approaches are expected to face several challenges
such as scalability, robustness and resiliency. In this paper, we propose a
self-modeling based diagnosis to enable resilient networks in the context of
SDN and NFV. We focus on solving two major problems: On the one hand, we lack
today of a model or template that describes the managed elements in the context
of SDN and NFV. On the other hand, the highly dynamic networks enabled by the
softwarisation require the generation at runtime of a diagnosis model from
which the root causes can be identified. In this paper, we propose finer
granular templates that do not only model network nodes but also their
sub-components for a more detailed diagnosis suitable in the SDN and NFV
context. In addition, we specify and validate a self-modeling based diagnosis
using Bayesian Networks. This approach differs from the state of the art in the
discovery of network and service dependencies at run-time and the building of
the diagnosis model of any SDN infrastructure using our templates
Feature Detection in Medical Images Using Deep Learning
This project explores the use of deep learning to predict age based on pediatric hand X-Rays. Data from the Radiological Society of North America’s pediatric bone age challenge were used to train and evaluate a convolutional neural network. The project used InceptionV3, a CNN developed by Google, that was pre-trained on ImageNet, a popular online image dataset. Our fine-tuned version of InceptionV3 yielded an average error of less than 10 months between predicted and actual age. This project shows the effectiveness of deep learning in analyzing medical images and the potential for even greater improvements in the future. In addition to the technological and potential clinical benefits of these methods, this project will serve as a useful pedagogical tool for introducing the challenges and applications of deep learning to the Bryant community
- …