This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...

ABSTRACT: Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex ...

The evolving regulatory landscape has increasingly recognized the value of real-world data (RWD) in enhancing drug safety surveillance across the clinical development lifecycle. Enabled by frameworks ...

The region, known as the South Atlantic Anomaly, has grown by an area nearly half the size of continental Europe, sprouting a lobe in the direction of Africa where the field is weakening the fastest.

ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...

We showcase a novel unsupervised learning method with a Convolutional Variational Autoencoder (CVAE) model that can automatically classify and cluster different types ...

WASHINGTON — True Anomaly, a defense-focused space technology startup based in Colorado, hired satellite industry executive Sarah Walter as chief operating officer, the company announced Sept. 2. The ...

To say that neutrinos aren’t the easiest particles to study would be a bit of an understatement. Outside of dark matter, there’s not much in particle physics that is as slippery as the elusive “ghost ...

A comprehensive Python-based machine learning solution for detecting anomalies in multivariate time series data from industrial IoT sensors. This solution identifies abnormal behavior patterns and ...

Introduction: Recent advances in artificial intelligence have created opportunities for medical anomaly detection through multimodal learning frameworks. However, traditional systems struggle to ...