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: The integration of Network Functions Virtualization (NFV) systems into mobile edge and core networks has heightened the need for effective anomaly detection and localization methods. The ...

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 ...

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...

CAR-T cell therapy has demonstrated remarkable success in treating hematologic malignancies and is now expanding into solid tumors. On June 1, 2025, The Lancet published positive results from the ...

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

TEL AVIV, Israel & BOSTON--(BUSINESS WIRE)--Transmit Security's Blinded by the Agent research reveals a coming crisis: consumer AI agents are defeating traditional fraud detection. Enterprises are ...

A complete workflow for building, training, and deploying a lightweight LSTM Autoencoder anomaly detector for temperature data on the ESP32 microcontroller—without TensorFlow or TFLite. This project ...

Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect ...