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Image colorization using deep convolutional auto-encoder with multi-skip connections
The colorization of grayscale images is a challenging task in image processing. Recently, deep learning has shown remarkable performance in image...
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Bearing condition monitoring via an unsupervised and enhanced stacked auto-encoder
Supervised deep learning models have been widely used in the construction of bearing health indicators (HIs) for performance degradation. Such models...
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Utilizing variable auto encoder-based TDO optimization algorithm for predicting loneliness from electrocardiogram signals
Several seniors and a substantial part of the general population are living in social isolation. This frequently occurs in vulnerability, isolation,...
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Two-Stream Auto-Encoder Network for Unsupervised Skeleton-Based Action Recognition
Representation learning from unlabeled skeleton data is a challenging task. Prior unsupervised learning algorithms mainly rely on the modeling...
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Enhancing Biometrics with Auto Encoder: Accurate Finger Detection from Fingerprint Images
The manuscript introduced a novel method for the precise identification of individual fingers from fingerprint images. Our approach leverages a...
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A Deep Learning Approach to Network Intrusion Detection Using a Proposed Supervised Sparse Auto-encoder and SVM
Due to the increasing use of communication technologies for data transmission, security threats have increased over the past decade. One of the...
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Comparison of Auto-Encoder Training Algorithms
Training of deep neural networks is difficult due to vanishing gradients. Therefore, a pre-training procedure based on restricted Boltzmann machines...
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A Fault Diagnosis Method of Rolling Bearing Based on GRU Convolution Denoising Auto-Encoder
Time series data of rolling bearing vibration is an important resource in the field of industrial systems, yet it is difficult to be exploited...
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An Intelligent Non-cooperative Spectrum Sensing Method Based on Convolutional Auto-encoder (CAE)
As an opportunistic spectrum utilization technology, cognitive radio can greatly improve the spectrum utilization efficiency and alleviate the...
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Deep clustering based on embedded auto-encoder
Deep clustering is a new research direction that combines deep learning and clustering. It performs feature representation and cluster assignments...
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Chaotic Biogeography Based Optimization Using Deep Stacked Auto Encoder for Big Data Classification
Big data's expansion has increased the need for efficient data classification methods. An optimization method inspired by nature called Chaotic...
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A Statistical WavLM Embedding Features with Auto-Encoder for Speech Emotion Recognition
Speech Emotion Recognition (SER) is an emerging field that encompasses various disciplines such as Human-Computer Interaction (HCI), Natural Language...
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Advance continuous monitoring of blood pressure and respiration rate using denoising auto encoder and LSTM
The importance of monitoring vital signs is increasing with the increase in the number of elderly people and deaths from chronic diseases worldwide....
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Fault Diagnosis of Rolling Element Bearings Based on a Second Order Cyclic Autocorrelation and a Deep Auto-encoder
A rolling element bearing fault diagnosis technique based on a second-order cyclic autocorrelation and a deep auto-encoder is proposed in this study...
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Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems
Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The...
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An iterative stacked weighted auto-encoder
The training of stacked auto-encoders (SAEs) consists of an unsupervised layer-wise pre-training and a supervised fine-tuning training. The...
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Intelligent recognition of milling tool wear status based on variational auto-encoder and extreme learning machine
In milling processing, the wear state of the tool has an essential influence on the processing quality. The machining process is not continuous in...
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Audio signal quality enhancement using multi-layered convolutional neural network based auto encoder–decoder
In this research article, a multi-layered convolutional neural network (MLCNN) based auto-CODEC for audio signal enhancement which is utilizing the...
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Hybrid Contractive Auto-encoder with Restricted Boltzmann Machine For Multiclass Classification
Contractive auto-encoder (CAE) is a type of auto-encoders and a deep learning algorithm that is based on multilayer training approach. It is...
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Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images
Conversion of grayscaled images to color images without human intervention is the subject of various researches within communities of machine...