Efficient quality enhancement of gastrointestinal endoscopic video by a novel method of color salient bilateral filtering
Sign Up to like & getrecommendations! Published in 2021 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-09951-x
Abstract: The recent advancements in bio-photonics enabled physicians to combine techniques such as narrow-band imaging, fluorescence spectroscopy, optical coherence tomography, with visible spectrum endoscopy video to provide in vivo microscopic tissue characterization in online optical biopsy… read more here.
Keywords: color; video; quality; bilateral filtering ... See more keywords
Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-018-0803-6
Abstract: Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural… read more here.
Keywords: android malware; model; deep autoencoder; cnn ... See more keywords
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Sign Up to like & getrecommendations! Published in 2017 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2017.03.034
Abstract: Abstract The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a… read more here.
Keywords: deep autoencoder; rotating machinery; machinery fault;
Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder
Sign Up to like & getrecommendations! Published in 2020 at "Nature Communications"
DOI: 10.1038/s41467-020-14666-6
Abstract: Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules… read more here.
Keywords: modules compressed; disease; disease modules; deep autoencoder ... See more keywords
A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2964294
Abstract: Lane-changing (LC) is a critical task for autonomous driving, especially in complex dynamic environments. Numerous automatic LC algorithms have been proposed. This topic, however, has not been sufficiently addressed in existing on-road manoeuvre decision methods.… read more here.
Keywords: autonomous vehicles; model; network xgboost; deep autoencoder ... See more keywords
Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Systems Journal"
DOI: 10.1109/jsyst.2021.3136683
Abstract: Designing an electricity theft cyberattack detector for the advanced metering infrastructures (AMIs) is challenging due to the limited availability of electricity theft datasets (i.e., malicious datasets). Anomaly detectors, which are trained solely on honest customers’… read more here.
Keywords: deep autoencoder; detection; electricity theft; electricity ... See more keywords
3-D Poststack Seismic Data Compression With a Deep Autoencoder
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3028023
Abstract: We approach the problem of 3-D poststack seismic data compression by training a model based on a deep autoencoder. Our network architecture is trained to consider the similarity between 3-D seismic sections drawn from one… read more here.
Keywords: compression; seismic data; deep autoencoder; data compression ... See more keywords
Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2021.3127716
Abstract: Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the… read more here.
Keywords: augmented deep; visual anomaly; self supervision; anomaly detection ... See more keywords
Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3172902
Abstract: Infrared thermography is an economical nondestructive testing technique for structural health monitoring of composite materials. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous backgrounds prevent it from… read more here.
Keywords: autoencoder thermography; thermography defect; defect detection; deep autoencoder ... See more keywords
A Deep Autoencoder With Novel Adaptive Resolution Reconstruction Loss for Disentanglement of Concepts in Face Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3165261
Abstract: Among the different categories of natural images, face images are very important because of their broad range of applications. One challenging topic of face processing by computers is extracting information related to only specific concepts… read more here.
Keywords: deep autoencoder; face images; autoencoder; reconstruction loss ... See more keywords
A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing
Sign Up to like & getrecommendations! Published in 2018 at "Mathematical Problems in Engineering"
DOI: 10.1155/2018/3145947
Abstract: Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the… read more here.
Keywords: classification; neural networks; method; autoencoder ... See more keywords