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 ...

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 ...

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 ...

Abstract: The use of heart rate monitoring devices has increased significantly, leading to the emergence of a new class of consumer-grade wearable devices designed for continuous heart rate monitoring ...

Abstract: Internet of Vehicles (IoV) systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly ...