Search Results - Softmax and Sparse Autoencoder*

Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier

Authors: Jinping Liu Longcheng Xu Yongfang Xie et al.

Source: IEEE Transactions on Cybernetics. 53:428-442

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://pubmed.ncbi.nlm.nih.gov/34550897
https://ieeexplore.ieee.org/document/9546667

Materials Identification of Polarized Pulse Laser Detection Based on Sparse Autoencoder and Softmax Classifier Framework

Authors: Xiaobin Xu Jialin Wu Jiali Wang et al.

Source: IEEE Transactions on Instrumentation and Measurement. 71:1-9

Subject Terms: 0103 physical sciences, 01 natural sciences, 0105 earth and related environmental sciences

Stacked Sparse Autoencoder and Softmax Classifier Framework to Classify MRI of Brain Tumor Images

Authors: Wasfy B. Mikhael Sarmad Ibrahim Ahmed Aldhahab

Source: International Journal of Intelligent Engineering and Systems. 13:268-279

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction

Authors: Luefeng Chen Wanjuan Su Mengtian Zhou et al.

Source: Information Sciences. 428:49-61

Subject Terms: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dl.acm.org/doi/10.1016/j.ins.2017.10.044
https://www.sciencedirect.com/science/article/pii/S0020025517310496
https://dblp.uni-trier.de/db/journals/isci/isci428.html#ChenZSWSH18
https://doi.org/10.1016/j.ins.2017.10.044

Blade imbalanced fault diagnosis for marine current turbine based on sparse autoencoder and softmax regression

Authors: Bin Xin Yide Wang Pingping Wen et al.

Source: 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). :246-251

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8406380
http://xplorestaging.ieee.org/ielx7/8400386/8405793/08406380.pdf?arnumber=8406380

Alzheimer's Disease Detection Using Sparse Autoencoder, Scale Conjugate Gradient and Softmax Output Layer with Fine Tuning

Authors: Debesh Jha Goo-Rak Kwon

Source: International Journal of Machine Learning and Computing. 7:13-17

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: http://www.ijmlc.org/vol7/612-IP018.pdf
http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=69&id=705

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

Authors: Sarah A. Ebiaredoh-Mienye Ebenezer Esenogho Theo G. Swart

Source: Electronics
Volume 9
Issue 11

Subject Terms: Softmax regression, medical diagnosis, machine learning, sparse autoencoder, 0202 electrical engineering, electronic engineering, information engineering, e-health, 02 engineering and technology, unsupervised learning, 10. No inequality, Unsupervised learning, artificial neural network, Sparse autoencoder, 3. Good health

File Description: application/pdf

Access URL: https://www.mdpi.com/2079-9292/9/11/1963/pdf
https://www.mdpi.com/2079-9292/9/11/1963/pdf
https://www.mdpi.com/2079-9292/9/11/1963

A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

Authors: Yilai Zheng Tianzhen Wang Bin Xin et al.

Source: Sensors, Vol 19, Iss 4, p 826 (2019)

Subject Terms: marine current turbine, blade attachment, sparse autoencoder, softmax regression, Chemical technology, TP1-1185

File Description: electronic resource

Relation: https://www.mdpi.com/1424-8220/19/4/826; https://doaj.org/toc/1424-8220

Access URL: https://doaj.org/article/fd86bb24300a4ee9844c13c5fdaa96e4

Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression

Authors: Vinod Jagannath Kadam Shivajirao Manikrao Jadhav K. Vijayakumar

Source: Journal of Medical Systems. 43

Subject Terms: Machine Learning, Biopsy, Fine-Needle, 0202 electrical engineering, electronic engineering, information engineering, Humans, Regression Analysis, Breast Neoplasms, Female, Diagnosis, Computer-Assisted, 02 engineering and technology, Early Detection of Cancer, 3. Good health

Access URL: https://pubmed.ncbi.nlm.nih.gov/31270634
https://www.infona.pl/resource/bwmeta1.element.springer-doi-10_1007-S10916-019-1397-Z
https://pubmed.ncbi.nlm.nih.gov/31270634/
https://www.ncbi.nlm.nih.gov/pubmed/31270634
https://link.springer.com/10.1007/s10916-019-1397-z
https://dl.acm.org/doi/10.1007/s10916-019-1397-z
https://dblp.uni-trier.de/db/journals/jms/jms43.html#KadamJV19

A Sparse Autoencoder and Softmax Regression based Diagnosis Method for the Attachment on the Blade of Marine Current Turbine

Authors: Bin Xin Tianzhen Wang Yilai Zheng et al.

Subject Terms: energy_fuel_technology

File Description: application/pdf

Access URL: https://www.preprints.org/manuscript/201811.0394/v1/download

Fast and quantitative noncontact laser ultrasound tapping detection of debonding in aerospace honeycomb sandwich panel based on autoencoder-softmax.

Authors: Wu, Qiang Xie, Weichen Xiong, Yi et al.

Source: Nondestructive Testing & Evaluation. Apr2025, p1-23. 23p. 16 Illustrations.

Subject Terms: *LASER ultrasonics, *SANDWICH construction (Materials), *MANUFACTURING defects, *AUTOENCODERS, *DEBONDING

XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition

Authors: M. Mohana P. Subashini George Ghinea

Source: Signal, Image and Video Processing. 19

Subject Terms: autoencoder, semi-supervised learning, convolutional sparse autoencoder, facial expression recognition, feature representation, unsupervised learning

Access URL: https://bura.brunel.ac.uk/handle/2438/31141

Improved Heart Disease Prediction Using Particle Swarm Optimization Based Stacked Sparse Autoencoder

Authors: Ibomoiye Domor Mienye Yanxia Sun

Source: Electronics, Vol 10, Iss 2347, p 2347 (2021)

Subject Terms: deep learning, heart disease, particle swarm optimization, softmax regression, stacked sparse autoencoder, Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/10/19/2347; https://doaj.org/toc/2079-9292; https://doaj.org/article/d151bef155304f508d8020b791b53a27

Availability: https://doi.org/10.3390/electronics10192347
https://doaj.org/article/d151bef155304f508d8020b791b53a27

XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition.

Authors: Mohana, M. Subashini, P. Ghinea, George

Source: Signal, Image & Video Processing; May2025, Vol. 19 Issue 5, p1-18, 18p

基于深度稀疏自编码网络的植物叶片分类.

Alternate Title: Plant leaf classification based on Softmax regression and K* deep sparse autoencoder network.

Authors: 王　雪 陈　炼 肖志勇

Source: Journal of Nanchang University (Natural Science). Dec2019, Vol. 43 Issue 6, p606-610. 5p.

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals.

Authors: Dutta, Ashit Kumar Albagory, Yasser Al Faraj, Manal et al.

Source: Computer Systems Science & Engineering. 2023, Vol. 44 Issue 2, p1517-1529. 13p.

Subject Terms: Machine learning, Electroencephalography, Mathematical regularization, Feature extraction, Time-domain analysis, Heterogeneity

A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.

Authors: Jo, Mi Young Kim, Hyun Jung

Source: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6439, 22p

Subject Terms: EDGE computing, AUTOENCODERS, RESEARCH evaluation, ANOMALY detection (Computer security), FEEDFORWARD neural networks, ARTIFICIAL neural networks

Advances in probabilistic modelling : sparse Gaussian processes, autoencoders, and few-shot learning

Authors: Bauer, Matthias Rasmussen, Carl Edward Schölkopf, Bernhard

Contributors: Bauer, Matthias Rasmussen, Carl Edward Schölkopf, Bernhard

Subject Terms: 519.2, Machine Learning, Probabilistic Modelling, Gaussian Processes, Variational Autoencoders, Few-shot Learning, Meta-Learning, Transfer Learning, Approximate Bayesian Inference, Variational Inference, Inductive Biases, Amortised Inference, Sparse Gaussian Processes, Invariance

Access URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.801859

Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder.

Authors: Rui Xu Tie Yan Shihui Sun et al.

Source: Oil Shale; 2025, Vol. 42 Issue 1, p79-114, 36p

Subject Terms: SHALE oils, SUPERVISED learning, PETROLEUM prospecting, AUTOENCODERS, MACHINE learning

A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction.

Authors: Abdussamad Daud, Hanita Sokkalingam, Rajalingam et al.

Source: Scientific Reports; 10/21/2025, Vol. 15 Issue 1, p1-22, 22p

Subject Terms: TYPE 2 diabetes, FEATURE selection, FORECASTING, COMPUTER performance, ARTIFICIAL neural networks, MEDICAL care, DEEP learning