Suchergebnisse - Softmax and Sparse Autoencoder~

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

Autoren: Xiaobin Xu Jialin Wu Jiali Wang et al.

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

Schlagwörter: 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

Autoren: Wasfy B. Mikhael Sarmad Ibrahim Ahmed Aldhahab

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

Schlagwörter: 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

Autoren: Luefeng Chen Wanjuan Su Mengtian Zhou et al.

Quelle: Information Sciences. 428:49-61

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

Zugangs-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

Autoren: Bin Xin Yide Wang Pingping Wen et al.

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

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

Zugangs-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

Autoren: Debesh Jha Goo-Rak Kwon

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

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

Zugangs-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

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

Quelle: Electronics
Volume 9
Issue 11

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-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

Autoren: Yilai Zheng Tianzhen Wang Bin Xin et al.

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

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/fd86bb24300a4ee9844c13c5fdaa96e4

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

Autoren: Bin Xin Tianzhen Wang Yilai Zheng et al.

Schlagwörter: energy_fuel_technology

Dateibeschreibung: application/pdf

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

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

Autoren: M. Mohana P. Subashini George Ghinea

Quelle: Signal, Image and Video Processing. 19

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

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

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

Autoren: Ibomoiye Domor Mienye Yanxia Sun

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

Schlagwörter: 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

Verfügbarkeit: 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.

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

Quelle: 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.

Autoren: 王　雪 陈　炼 肖志勇

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

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals.

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

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

Schlagwörter: 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.

Autoren: Jo, Mi Young Kim, Hyun Jung

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

Schlagwörter: 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.

Autoren: Rui Xu Tie Yan Shihui Sun et al.

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

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

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

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

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

Schlagwörter: 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

Autoren: Mienye, Ibomoiye Domor Sun, Yanxia

Schlagwörter: 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.

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

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

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

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

Schlagwörter: 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

Autoren: Huseyin Polat Muammer Turkoglu Onur Polat

Quelle: IET Communications. 14:4089-4100

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

Zugangs-URL: 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.

Autoren: Jadhav, Pratik Sairam, Vuppala Adithya Bhojane, Niranjan et al.

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

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