autoencoderdecision support systemsElectrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisition and evaluation (e.g., 24-hour holter recording...
lung cancer pathological image analyzer based on convolutional autoencoder - GitHub - HuichuanLiu/cnncancer: lung cancer pathological image analyzer based on convolutional autoencoder
Convolutional VariationalAutoencoder Implementation of CNN-VAE for MNIST reconstrunction. Training Loss for reconstrunction shows below: The next images show original images from test set: And it's respective output from the NN: machine-learningdeep-learningcnnmnistconvolutional-neural-networksvariational-...
Convolutional Autoencoder Deep Learning Etching Feature Extraction Industry 4.0 Neural Network Optical Emission Spectroscopy Semiconductor Manufacturing View PDFReferences 1 Bruschetta M., Maran F., Beghi A. A fast implementation of mpc-based motion cueing algorithms for mid-size road vehicle motion simulat...
One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable for multimodal data, which relies on two convolutional autoencoders jointly trained to reconstruct the positive input data...
The coasts of the Northeastern United States experience wind and flood damage as a result of extratropical cyclones (such as Nor’easters). However, r
In this paper, it is tried to increase the encryption complexity and unpredictability of the encryption scheme using different phases of chaos game representation (GCR), logistic map diffusion, and convolutional auto-encoder-based image representation. In the proposed scheme, the original image's ...
An enhanced framework for peak-to-average power ratio (PAPR) reduction and waveform design for Multiple-Input-Multiple-Output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems, based on a convolutional-autoencoder (CAE) architecture, is presented. The end-to-end learning-based auto...
You can also find the code in my Github. nageshsinghc4/Recreating-Fingerprints-using-Convolutional-Autoencoders Build a Neural network that is capable of recreating or reconstructing fingerprint images. The dataset that I'm using… Conclusion ...
In this repository we make use of various convetional machine learning models as well as a few convolutional autoencoders for this purpose. Data Download dataset Prior to conducting any experiments, you will need to acquire the FRGADB dataset from: https://zenodo.org/records/13773680 and ...