Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network
Sign Up to like & getrecommendations! Published in 2020 at "Applied Energy"
DOI: 10.1016/j.apenergy.2019.114159
Abstract: Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace… read more here.
Keywords: stacked sparse; combustion stability; combustion; sparse autoencoder ... See more keywords
A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network
Sign Up to like & getrecommendations! Published in 2021 at "Computers in Biology and Medicine"
DOI: 10.1016/j.compbiomed.2021.105134
Abstract: Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome… read more here.
Keywords: sparse autoencoder; computer aided; covid; chest ray ... See more keywords
Feature learning and change feature classification based on deep learning for ternary change detection in SAR images
Sign Up to like & getrecommendations! Published in 2017 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2017.05.001
Abstract: Abstract Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this… read more here.
Keywords: feature; change; sparse autoencoder; ternary change ... See more keywords
Dynamic Summarization of Videos Based on Descriptors in Space-Time Video Volumes and Sparse Autoencoder
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2872685
Abstract: This paper addresses the problem of generating meaningful summaries from unedited user videos. A framework based on spatiotemporal and high-level features is proposed in this paper to detect the key-shots after segmenting the videos into… read more here.
Keywords: video; time; sparse autoencoder; videos based ... See more keywords
Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3244795
Abstract: The diagnostic study on single-fault with distinguishing features based on monitoring data analysis is mature and fruitful in recent years. However, the early fault signals collected by practical monitoring systems often possess the following characteristics:… read more here.
Keywords: fault diagnosis; denoising integrated; diagnosis; sparse autoencoder ... See more keywords
An Explainable AI Framework Integrating Variational Sparse Autoencoder and Random Forest for EEG-Based Epilepsy Detection
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3620762
Abstract: Epilepsy is a medical condition characterized by sudden and frequent sensory disruptions which is commonly detected by electroencephalogram (EEG) analysis. However, analyzing these signals is challenging for traditional classifiers due to their non-stationary nature and… read more here.
Keywords: autoencoder; variational sparse; sparse autoencoder; random forest ... See more keywords
A Fuzzy Deep Neural Network With Sparse Autoencoder for Emotional Intention Understanding in Human–Robot Interaction
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2020.2966167
Abstract: A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e., age, gender, and region), in which the fuzzy C-means (FCM) is used to… read more here.
Keywords: intention; dnnsa; emotional intention; network ... See more keywords
Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder
Sign Up to like & getrecommendations! Published in 2018 at "Mathematical Problems in Engineering"
DOI: 10.1155/2018/9837359
Abstract: This paper presents a lane departure detection approach that utilizes a stacked sparse autoencoder (SSAE) for vehicles driving on motorways or similar roads. Image preprocessing techniques are successfully executed in the initialization procedure to obtain… read more here.
Keywords: stacked sparse; lane departure; detection; departure detection ... See more keywords
Domain-adaptive gearbox fault diagnosis using deep residual sparse autoencoder with symmetric dot pattern encoding
Sign Up to like & getrecommendations! Published in 2025 at "Structural Health Monitoring"
DOI: 10.1177/14759217251378649
Abstract: The intelligent operation and maintenance of gear systems face challenges like diverse and dynamic working conditions, which may cause distribution discrepancies in the feature space, thereby significantly reducing the effectiveness of traditional diagnostic models. In… read more here.
Keywords: deep residual; sparse autoencoder; residual sparse; fault diagnosis ... See more keywords
A new deep sparse autoencoder for community detection in complex networks
Sign Up to like & getrecommendations! Published in 2020 at "EURASIP Journal on Wireless Communications and Networking"
DOI: 10.1186/s13638-020-01706-4
Abstract: Feature dimension reduction in the community detection is an important research topic in complex networks and has attracted many research efforts in recent years. However, most of existing algorithms developed for this purpose take advantage… read more here.
Keywords: sparse autoencoder; deep sparse; community detection; complex networks ... See more keywords
Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder
Sign Up to like & getrecommendations! Published in 2019 at "PLoS ONE"
DOI: 10.1371/journal.pone.0214712
Abstract: Based on electrohysterogram, this paper designed a new method using wavelet-based nonlinear features and stacked sparse autoencoder for preterm birth detection. For each sample, three level wavelet decomposition of a time series was performed. Approximation… read more here.
Keywords: wavelet; stacked sparse; preterm birth; sparse autoencoder ... See more keywords