Výsledky vyhľadávania - Softmax and Sparse Autoencoder

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

Autori: Xiaobin Xu Jialin Wu Jiali Wang a ďalší

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

Predmety: 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

Autori: Wasfy B. Mikhael Sarmad Ibrahim Ahmed Aldhahab

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

Predmety: 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

Autori: Luefeng Chen Wanjuan Su Mengtian Zhou a ďalší

Zdroj: Information Sciences. 428:49-61

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

Prístupová URL adresa: 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

Autori: Bin Xin Yide Wang Pingping Wen a ďalší

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

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: 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

Autori: Debesh Jha Goo-Rak Kwon

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

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

Prístupová URL adresa: 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

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

Zdroj: Electronics
Volume 9
Issue 11

Predmety: 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

Popis súboru: application/pdf

Prístupová URL adresa: 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

Autori: Yilai Zheng Tianzhen Wang Bin Xin a ďalší

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

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

Popis súboru: electronic resource

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

Prístupová URL adresa: https://doaj.org/article/fd86bb24300a4ee9844c13c5fdaa96e4

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

Autori: Bin Xin Tianzhen Wang Yilai Zheng a ďalší

Predmety: energy_fuel_technology

Popis súboru: application/pdf

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

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

Autori: M. Mohana P. Subashini George Ghinea

Zdroj: Signal, Image and Video Processing. 19

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

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

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

Autori: Ibomoiye Domor Mienye Yanxia Sun

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

Predmety: 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

Dostupnosť: 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.

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

Zdroj: 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.

Autori: 王　雪 陈　炼 肖志勇

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

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals.

Autori: Dutta, Ashit Kumar Albagory, Yasser Al Faraj, Manal a ďalší

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

Predmety: 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.

Autori: Jo, Mi Young Kim, Hyun Jung

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

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

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

Autori: Rui Xu Tie Yan Shihui Sun a ďalší

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

Predmety: SHALE oils, SUPERVISED learning, PETROLEUM prospecting, AUTOENCODERS, MACHINE learning

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

Autori: Abdussamad Daud, Hanita Sokkalingam, Rajalingam a ďalší

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

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

Improved heart disease prediction using stacked sparse autoencoder and softmax regression

Autori: Mienye, Ibomoiye Domor Sun, Yanxia

Predmety: Deep learning, Heart disease, Softmax regression

Relation: http://hdl.handle.net/10210/487565; uj:44388; Citation: Mienye, I.D. & Sun, Y. 2021. Improved heart disease prediction using stacked sparse autoencoder and softmax regression.

Dostupnosť: http://hdl.handle.net/10210/487565

Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders.

Autori: Hafeezallah, Adel Al-Dhamari, Ahlam Abu-Bakar, Syed Abd Rahman

Zdroj: Computer Systems Science & Engineering. 2023, Vol. 47 Issue 1, p593-611. 19p.

Predmety: Collective behavior, Image segmentation, Deep learning, Data analysis, Accuracy

Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET

Autori: Huseyin Polat Muammer Turkoglu Onur Polat

Zdroj: IET Communications. 14:4089-4100

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: http://digital-library.hnpme.com/content/journals/10.1049/iet-com.2020.0477
https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2020.0477
https://doi.org/10.1049/iet-com.2020.0477
https://dblp.uni-trier.de/db/journals/iet-com/iet-com14.html#PolatTP20
https://digital-library.theiet.org/content/journals/10.1049/iet-com.2020.0477
https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-com.2020.0477
http://digital-library.hnpme.com/content/journals/10.1049/iet-com.2020.0477;fmt=download

Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder.

Autori: Jadhav, Pratik Sairam, Vuppala Adithya Bhojane, Niranjan a ďalší

Zdroj: Computers, Materials & Continua; 2025, Vol. 83 Issue 2, p3493-3517, 25p

Predmety: CONVOLUTIONAL neural networks, GENERATIVE adversarial networks, THERMOGRAPHY, STANDARD deviations, AUTOENCODERS, ELECTRONIC noses