Scientific Research Publishing

Justice, A.C., Covinsky, K.E. and Berlin, J.A. (1999) Assessing the Generalizability of Prognostic Information. Annals of Internal Medicine, 130, 515-524.

ABSTRACT: Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of ...
Frontiers

Convolutional neural networks and mixture of experts for intrusion detection in 5G networks and beyond

Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece The advent of 6G/NextG networks offers numerous benefits, ...
IEEE

Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Scientific Research Publishing

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications ()

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...
Frontiers

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly ...
GitHub

A quantised temporal convolutional autoencoder deployed on the PYNQ-Z2 FPGA using the FINN framework.

The model architecture, training, and validation can be found in the QuantisedAutoEncoder.ipynb Python Jupyter Notebook This part of the project was done using the Google Drive and Google Colab, and ...