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Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis
Deep clustering has been widely applicated in various fields, including natural image and language processing. However, when it is applied to...
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Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network
Medical diagnosis can be severely hindered by distorted medical images, especially in the analysis of Magnetic Resonance Imaging (MRI) and Computed...
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Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder
The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online....
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Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder
Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a...
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TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition
Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to...
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ECG heartbeats classification with dilated convolutional autoencoder
Electrocardiography is essential for the early diagnosis and treatment of heart diseases, as undiagnosed heart diseases can lead to unfortunate...
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Epileptic EEG signal classification using an improved VMD-based convolutional stacked autoencoder
Numerous techniques have been explored so far for epileptic electroencephalograph (EEG) signal detection and classification. Deep learning-based...
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Diesel Engine Fault Diagnosis Based on Convolutional Autoencoder Using Vibration Signals
AbstractThe diesel engine is the power source and core equipment of large mechanical systems such as ships. Thus, the engine must be maintained in...
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Design and implementation of an Automatic Deep Stacked Sparsely Connected Convolutional Autoencoder (ADSSCCA) neural network for remote sensing lithological map** using calculated dropout
The accurate map** of lithological units in tropical environments characterised by dense forest, persistent cloud cover, and limited bedrock...
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Vector Quantized Convolutional Autoencoder Network for LDCT Image Reconstruction with Hybrid Loss
Medical image reconstruction is the process of creating high-quality and accurate images. During acquisition, these devices capture raw measurements...
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FPGA-Based Convolutional Autoencoder Implementation
Field-programmable gate array (FPGA) is widely regarded as a promising platform for accelerating Convolutional Autocoders (CAEs). This paper...
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CESCAL: A joint compression-encryption scheme based on convolutional autoencoder and logistic map
In the modern digital world, it is getting harder to share voluminous data with high dimensions, over the internet with adequate security. Images...
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A multi-information fusion anomaly detection model based on convolutional neural networks and AutoEncoder
Network traffic anomaly detection, as an effective analysis method for network security, can identify differentiated traffic information and provide...
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A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images
The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and...
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Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder
SARS-CoV-2’s population structure might have a substantial impact on public health management and diagnostics if it can be identified. It is critical...
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Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification
Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the...
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Deep Embedding Clustering Based on Residual Autoencoder
Clustering algorithm is one of the most widely used and influential analysis techniques. With the advent of deep learning, deep embedding...
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Remove and recover: two stage convolutional autoencoder based sonar image enhancement algorithm
High-quality forward-looking sonar images are the basic guarantee for underwater object detection and classification of autonomous underwater vehicle...
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Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection
Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation....
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Development of Autoencoder and Variational Autoencoder for Image Recognition Using Convolutional Neural Network
In this work, we present the technology of develo** the architecture of a standard and variational autoencoder based on a convolutional neural...