Suchergebnisse - sparse conventional autoencoder (same)

Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.

Autoren: Gwak, Seunghyun Yang, Sooyoung Jeong, Heawon et al.

Quelle: Biomedical Engineering Letters; Jul2025, Vol. 15 Issue 4, p785-795, 11p

Stacked Sparse Autoencoder for Audio Object Coding

Autoren: Xiaochen Wang Gang Li Yulin Wu et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030678319

Zugangs-URL: https://dblp.uni-trier.de/db/conf/mmm/mmm2021-1.html#WuHWHL21
https://link.springer.com/content/pdf/10.1007%2F978-3-030-67832-6_5.pdf
https://rd.springer.com/chapter/10.1007/978-3-030-67832-6_5
https://link.springer.com/chapter/10.1007/978-3-030-67832-6_5

Comparing Functional and Effective Connectivity Features in Diagnosis of Autism Spectrum Disorder Using Stacked Autoencoder by Resting-State fMRI Data.

Autoren: Navaei, Pegah Khadem, Ali Zaferani, Effat Jalaeian

Quelle: Frontiers in Biomedical Technologies; Autumn2025, Vol. 12 Issue 4, p830-843, 14p

Schlagwörter: AUTISM spectrum disorders, FUNCTIONAL connectivity, AUTOENCODERS, ARTIFICIAL neural networks, FUNCTIONAL magnetic resonance imaging, GRANGER causality test

Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling.

Autoren: Park, Yeonjin Hong, Jungpyo

Quelle: Applied Sciences (2076-3417); Jan2025, Vol. 15 Issue 1, p92, 21p

Schlagwörter: VECTOR quantization, SIGNAL reconstruction, AUTOENCODERS, SIGNALS & signaling

A new Sparse Auto-encoder based Framework using Grey Wolf Optimizer for Data Classification Problem

Autoren: Karim, Ahmad Mozaffer

Index Begriffe: Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2201.12493

Simulation of Spring Discharge Using Deep Learning, Considering the Spatiotemporal Variability of Precipitation.

Autoren: Ma, Chunmei Jiao, Haoyu Hao, Yonghong et al.

Quelle: Water Resources Research; Apr2025, Vol. 61 Issue 4, p1-24, 24p

Schlagwörter: STANDARD deviations, SPRING, PRECIPITATION variability, DATA augmentation, AUTOENCODERS, DEEP learning

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays.

Autoren: Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed et al.

Quelle: Machines; Dec2024, Vol. 12 Issue 12, p894, 15p

Schlagwörter: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network.

Autoren: Medvedev, A.V. Lehmann, B.

Quelle: Frontiers in Neuroinformatics; 2025, p1-11, 11p

Schlagwörter: BRAIN waves, AUTOENCODERS, ARTIFICIAL intelligence, DEEP learning, DATABASES, UNIVERSITY hospitals

hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction.

Autoren: Cisotto, Giulia Zancanaro, Alberto Zoppis, Italo F. et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-22, 22p

Schlagwörter: AUTOENCODERS, BRAIN-computer interfaces, MOTOR imagery (Cognition), SIGNAL reconstruction, RESEARCH questions, DEEP learning

Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments.

Autoren: Nalinipriya, G. Rama Sree, S. Radhika, K. et al.

Quelle: Scientific Reports. 7/9/2025, Vol. 15 Issue 1, p1-24. 24p.

Schlagwörter: *MACHINE learning, *ARTIFICIAL intelligence, *EMPIRICAL research, *FEATURE selection, *INTERNET security, *CYBERTERRORISM

Harnessing Generative Deep Learning for Enhanced Ensemble Data Assimilation.

Autoren: Foroumandi, Ehsan Moradkhani, Hamid

Quelle: Water Resources Research; Jul2025, Vol. 61 Issue 7, p1-25, 25p

Schlagwörter: DATA assimilation, HYDROLOGIC models, GENERATIVE artificial intelligence, AUTOENCODERS

A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization.

Autoren: Duceac, Mădălina Buzea, Călin Gheorghe Pleșea-Condratovici, Alina et al.

Quelle: Diagnostics (2075-4418); Jul2025, Vol. 15 Issue 13, p1628, 40p

Schlagwörter: ARTIFICIAL neural networks, ENSEMBLE learning, CLINICAL decision support systems, AUTOENCODERS, BLENDED learning

Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.

Autoren: Di Gao Guanghao Yang Jiarun Shen et al.

Quelle: Frontiers in Computational Neuroscience; 2024, p01-12, 12p

Schlagwörter: HEALTH risk assessment, FUNCTIONAL magnetic resonance imaging, VALUE engineering, EXTRACTION techniques, DATA mining

AACNet: attention aided CNN-autoencoder network for precise categorization of respiratory conditions from HRCT scans.

Autoren: Kakati, Anchita Bhattacharjya, Upasana Medhi, Jyoti Prakash et al.

Quelle: Network Modeling & Analysis in Health Informatics & Bioinformatics; 8/20/2025, Vol. 14 Issue 1, p1-29, 29p

On the Relation Between Linear Autoencoders and Non-Negative Matrix Factorization for Mutational Signature Extraction.

Autoren: Egendal, Ida Brøndum, Rasmus Froberg Pelizzola, Marta et al.

Quelle: Journal of Computational Biology; May2025, Vol. 32 Issue 5, p461-472, 12p

An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.

Autoren: Islam, Monira Lee, Tan

Quelle: Brain Informatics; 8/2/2025, Vol. 12 Issue 1, p1-19, 19p

Schlagwörter: EMOTION recognition, ELECTROENCEPHALOGRAPHY, MULTIVARIATE analysis, SIGNAL processing, SPECTRUM analysis, DEEP learning

Application of the robust autoencoder to reduce reverberation and facilitate underwater target tracking.

Autoren: Xiang, Wenjie Song, Zhongchang Gao, Zhanyuan et al.

Quelle: Applied Acoustics. Jan2025, Vol. 228, pN.PAG-N.PAG. 1p.

Schlagwörter: *NONLINEAR estimation, *MATCHED filters, *FIELD research, *SONAR, *ALGORITHMS

A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders.

Autoren: Chuyi Zhang Zhen Zhang Feng Zhang et al.

Quelle: Frontiers in Microbiology; 2024, p1-15, 15p

Schlagwörter: IRRITABLE colon, TYPE 2 diabetes, HUMAN body, PREDICTION models, ASTHMA

Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning.

Autoren: Tolooshams, Bahareh Dey, Sourav Ba, Demba

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Jun2021, Vol. 32 Issue 6, p2415-2429, 15p

Schlagwörter: IMAGE denoising, RECURRENT neural networks, EXPECTATION-maximization algorithms, REGULARIZATION parameter

ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.

Autoren: Almuqhim, Fahad Saeed, Fahad

Quelle: Frontiers in Computational Neuroscience; 4/8/2021, Vol. 15, pN.PAG-N.PAG, 10p

Schlagwörter: AUTISM spectrum disorders, FUNCTIONAL magnetic resonance imaging, BRAIN imaging, NEURAL development, FEATURE extraction