The primary goal of this project was to explore the use of deep learning, specifically an autoencoder, for generating pseudo-random data that exhibits similar characteristics to a given training ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

This project uses a Keras/TensorFlow Autoencoder to identify anomalous traffic patterns in an AWS Elastic Load Balancer (ELB) request count dataset. The goal is to build an unsupervised learning model ...

"IEEE.tv is an excellent step by IEEE. This will pave a new way in knowledge-sharing and spreading ideas across the globe." ...

A fairly common sub-problem in many machine learning and data science scenarios is the need to compute the similarity (or difference or distance) between two datasets. For example, if you select a ...

Abstract: Spectrum map completion is crucial for effective radio environment management. Currently most spectrum map completion methods only consider the available information of measurement data in ...