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Towards the Analysis of Regularized Denoising Autoencoder for Biosignal Processing: Lasso Versus Ridge Norms
The use of Internet of Things (IoT) that integrate smart bio sensor devices to the internet and shows individual health in real time. Healthcare...
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A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance
Autoencoder and conventional machine learning classifiers are widely used to design an intrusion detection system (IDS). However, noise and...
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Convolutional Autoencoder-Based Models for Image Denoising: A Comparative Study
Images are often infected with noise for a variety of reasons, including camera sensor defects, transmission via noisy channels, and incorrect memory...
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Patch-Based Autoencoder for Document Image Denoising with Smoothening
Documents can be stored in digital formats permanently thanks to the ease of digitization. While scanning or capturing documents, noises such as low...
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Speech Dereverberation Based on Self-supervised Residual Denoising Autoencoder with Linear Decoder
A recently proposed self-supervised denoising autoencoder with linear decoder (DAELD) speech enhancement system demonstrated promising potential in...
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Explainable Anomaly Detection of 12-Lead ECG Signals Using Denoising Autoencoder
Anomaly detection is an important task in the field of medical diagnostics, especially when it comes to ECG signals. Anomalies in ECG signals can be...
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U-Net-Based Denoising Autoencoder Network for De-Speckling in Fetal Ultrasound Images
Nowadays medical imaging plays an important role in the detection of defects before proceeding with any medical procedures. During pregnancy, medical...
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Automatic Signal Denoising and Multi-Component Fault Classification Based on Deep Learning Using Integrated Condition Monitoring in a Wind Turbine Gearbox
Abstract ObjectiveThis study aims to develop an automated method for signal denoising and fault classification in wind turbine gearboxes, known for...
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Trust-Based Context-Aware Collaborative Filtering Using Denoising Autoencoder
In recent times, extensive studies have been initiated to leverage deep learning strategies to enhance context-aware recommendation. Classical...
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Denoising transthoracic echocardiographic images in regional wall motion abnormality using deep learning techniques
Image analysis and classification perform well in pre-processed noise-free images than in corrupted images. Synthetic aperture radar (SAR) images,...
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Image Denoising Framework Employing Auto Encoders for Image Reconstruction
Auto Encoder (AE) can be used in denoising of images. It is a type of neural network that can reconstruct the input. An auto-encoder is to represent...
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An Experimental Study on Denoising the Images with Autoencoders
Noisy data is still one of the most common issues in modern data transmission. We can solve this by using deep learning with an autoencoder, a...
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Autoencoder: An Unsupervised Deep Learning Approach
With the advent of science and technology, it has been observed that autoencoder plays a vital role in unsupervised learning and in deep...
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Image dehazing using autoencoder convolutional neural network
In hazy weather, the image in the scene suffers from noise which makes them less visible and to detect an object in hazy weather becomes a...
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Simultaneous denoising and super resolution of document images
In this paper, we propose a unified approach for denoising and super-resolution of document images. The approach is a one shot unpaired technique...
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Dual-AutoEncoder & Bipartite Graph Embedding for article recommendation
Existing methods in article recommendation fail to fully use the article information, or pay less attention to the correlations among articles and...
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Effective Ensemble Dimensionality Reduction Approach Using Denoising Autoencoder for Intrusion Detection System
Intrusion detection system monitors network traffic and issues alert when it spots suspicious activities. Dimensionality reduction is the initial...
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On the Similarities Between Denoising Diffusion Models and Autoencoders
Denoising diffusion probabilistic models (DDPMs) have recently emerged as a powerful paradigm for generative modelling, outperforming adversarial...
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Multi-task learning with self-learning weight for image denoising
BackgroundImage denoising technology removes noise from the corrupted image by utilizing different features between image and noise. Convolutional...
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Active label-denoising algorithm based on broad learning for annotation of machine health status
Deep learning has led to tremendous success in machine maintenance and fault diagnosis. However, this success is predicated on the correctly...