Scientific Research Publishing

A Hybrid CNN-LSTM Variational Autoencoder for Treatment Response Prediction in Synthetic Psychiatric Data ()

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

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM

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 ...