IEEE

Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder

A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power ...
Frontiers

Automated detection of pinworm parasite eggs using YOLO convolutional block attention module for enhanced microscopic image analysis

Introduction: Parasitic infections remain a major public health concern, particularly in healthcare and community settings where rapid and accurate diagnosis is essential for effective treatment and ...
GitHub

Fusing Sequence Motifs and Pan-Genomic Features: Antimicrobial Resistance Prediction using an Explainable Lightweight 1D CNN - XGBoost Ensemble

A Sequence-Aware Custom 1D CNN: A deep, lightweight 1D Convolutional Neural Network (CNN) processes the ordered sequence of integer-encoded SNPs. Its hierarchical convolutional layers are designed to ...
C&EN

DCGCN: Dual-Channel Graph Convolutional Network-Based Drug–Target Interaction Prediction Method with 3D Molecular Structure

College of Artificial Intelligence, Tianjin Normal University, Tianjin 300387, China Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin ...
Frontiers

Fault diagnosis algorithm based on multi-channel neighbor feature convolutional network

Introduction: The health status of bearings is an essential prerequisite to ensure the safe and stable operation of vehicles. However, the negative impact of covariate shifts among data channels on ...
Scientific Research Publishing

Li, Z., Yang, W., Peng, S., et al. (2020) A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects.

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...
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 ...