Suchergebnisse - Convolutional sparse autoencoder~

Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

Autoren: Qin, Yahang Li, Zhenni Xie, Shengli et al.

Quelle: Tsinghua Science and Technology. 30:68-86

Schlagwörter: Satellites, Multipath classification, BeiDou Navigation Satellite System (BDS), Solid modeling, Three-dimensional displays, Long short-term memory (LSTM), Convolutional sparse autoencoder, Interference, Urban areas, Convolution, Long short term memory

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

IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network

Autoren: M. Ramkumar S. Syed Jamaesha M. S. Gowtham et al.

Quelle: Signal, Image and Video Processing. 18:3509-3525

Aggregating Dual Attention Residual Network and Convolutional Sparse Autoencoder to Enhance the Diagnosis of Alzheimer’s Disease

Autoren: Shuiqing Jing Ruohan Li Shiguan Mu et al.

Quelle: 2023 International Conference on Human-Centered Cognitive Systems (HCCS). :1-6

Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest

Autoren: Na Yang Zhenkai Zhang Jianhua Yang et al.

Quelle: Natural Resources Research. 32:1-18

Schlagwörter: 0101 mathematics, 01 natural sciences, 0105 earth and related environmental sciences

Convolutional Sparse Autoencoder for Emotion Recognition

Autoren: M. Mohana P. Subashini

Quelle: Lecture Notes on Data Engineering and Communications Technologies ISBN: 9783031277610

Aggregating Dual Attention Residual Network and Convolutional Sparse Autoencoder to Enhance Alzheimer's Disease Diagnosis

Autoren: Shuiqing Jing Lanlan Li Jianlong Li et al.

Artists Identification Using Convolutional Sparse Autoencoder

Autoren: Vinnia Kemala Putri Felix Indra Kurniadi

Quelle: 2019 5th International Conference on Computing Engineering and Design (ICCED). :1-4

Schlagwörter: 4. Education, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9161109
http://xplorestaging.ieee.org/ielx7/9153549/9161075/09161109.pdf?arnumber=9161109

Domain Adaptation with Stacked Convolutional Sparse Autoencoder

Autoren: Yi Zhu Xinke Zhou Yun Li et al.

Quelle: Communications in Computer and Information Science ISBN: 9783030923068

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

Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

Autoren: Kashyap, Hirak J Fowlkes, Charless C Krichmar, Jeffrey L

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 32(6)

Schlagwörter: Information and Computing Sciences, Computer Vision and Multimedia Computation, Estimation, Dynamics, Optical imaging, Cameras, Training, Adaptive optics, Motion segmentation, Convolutional autoencoder, ego motion, object motion, overcomplete basis, sparse representation, cs.CV, cs.LG, Artificial Intelligence & Image Processing, Artificial intelligence

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/7p53p8zv
https://escholarship.org/content/qt7p53p8zv/qt7p53p8zv.pdf

Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder

Autoren: Yi Zhu Xinke Zhou Xindong Wu

Quelle: Applied Sciences, Vol 13, Iss 1, p 481 (2022)

Schlagwörter: domain adaptation, convolutional autoencoder, sparse autoencoder, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/1/481; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/491024e2261147178e7df1a59356e59a

A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features

Autoren: Fei Xiang Mingyue Liu Wenna Chen et al.

Quelle: Frontiers in Neuroscience, Vol 19 (2025)

Schlagwörter: seizure detection, convolutional neural network, gated recurrent unit, sparse autoencoder, borderline synthetic minority oversampling technique, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fnins.2025.1698960/full; https://doaj.org/toc/1662-453X

Zugangs-URL: https://doaj.org/article/2a3723ccee1d49029288ff3b2996bd8f

RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction.

Autoren: Baviskar, Vaishali Verma, Madhushi Chatterjee, Pradeep et al.

Quelle: Expert Systems. Dec2024, Vol. 41 Issue 12, p1-20. 20p.

Schlagwörter: CONVOLUTIONAL neural networks, MACHINE learning, OPTIMIZATION algorithms, NOSOLOGY, CLASSIFICATION algorithms

Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest.

Autoren: Yang, Na Zhang, Zhenkai Yang, Jianhua et al.

Quelle: Natural Resources Research; Feb2023, Vol. 32 Issue 1, p1-18, 18p

Schlagwörter: ADDITION (Mathematics), GENERALIZATION, MINERALIZATION, ORE deposits, PRIOR learning

Geografische Kategorien: SHAANXI Sheng (China), CHINA

IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network.

Autoren: Ramkumar, M. Jamaesha, S. Syed Gowtham, M. S. et al.

Quelle: Signal, Image & Video Processing; Jun2024, Vol. 18 Issue 4, p3509-3525, 17p

Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder

Autoren: Kunjin Chen Jun Hu Jinliang He

Quelle: 2017 IEEE Power & Energy Society General Meeting. :1-1

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: https://dblp.uni-trier.de/db/journals/tsg/tsg9.html#ChenHH18a
http://ieeexplore.ieee.org/document/7539352/
https://ieeexplore.ieee.org/document/7539352
https://ieeexplore.ieee.org/document/8273992/

Attention-enhanced residual autoencoder for NIR spectral feature extraction and classification of grain varieties.

Autoren: Kabiso, Abel Chernet Ko, Cheng-Hao

Quelle: Scientific Reports; 9/24/2025, Vol. 15 Issue 1, p1-18, 18p

Schlagwörter: NEAR infrared spectroscopy, DEEP learning, MACHINE learning, CROP yields, AUTOENCODERS, BIOLOGICAL classification, AGRICULTURE

An ECG Signal Encryption and Classification Using Sparse Autoencoder and Optimized Neural Network Techniques.

Autoren: Shanmugasundaram, Sumathi Chinnasamy, Murukesh Annaman, Lakshmi Sangeetha

Quelle: Traitement du Signal. Feb2025, Vol. 42 Issue 1, p557-568. 12p.

Schlagwörter: *DATA security, *ARTIFICIAL neural networks, *DATA protection, PATIENT monitoring, ELECTROCARDIOGRAPHY, CLASSIFICATION

Remove and recover: two stage convolutional autoencoder based sonar image enhancement algorithm.

Autoren: Liu, Ting Yan, Shun Wang, Guofeng

Quelle: Multimedia Tools & Applications; May2024, Vol. 83 Issue 18, p55963-55979, 17p

Schlagwörter: SONAR imaging, IMAGE intensifiers, SPECKLE interference, OBJECT recognition (Computer vision), SPECKLE interferometry, TRACKING radar, TRAFFIC noise