Suchergebnisse - residual conventional autoencoder

Artificial Neural Networks in the Wastewater Industry. From Conventional to Data-based Industrial Control

Autoren: Pisa Dacosta, Ivan

Thesis Advisors: Morell Pérez, Antoni, López Vicario, José, Vilanova i Arbós, Ramon

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Xarxes neuronals artificials, Redes neuronales artificiales, Artificial neural networks, Control automàtic, Control automático, Automatic control, Estacions depuradores d'aigües residuals, Estaciones depuradoras de aguas residuales, Wastewater treatment plants, Tecnologies

Time: 621.3

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/675219

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

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

Autoren: Yeonjin Park Jungpyo Hong

Quelle: Applied Sciences, Vol 15, Iss 1, p 92 (2024)

Schlagwörter: DIFAR sonobuoy, autoencoder, air channel modeling, signal reconstruction, 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/2076-3417/15/1/92; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/8302d3467a3241c8bcd55e61b003e71b

Multi-scale Residual Segmentation Network for Histopathological Image.

Autoren: BOZDAĞ KARAKEÇİ, Zehra TALU, Muhammed Fatih

Quelle: Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi; eyl2024, Vol. 15 Issue 3, p623-632, 10p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, IMAGE segmentation, OBJECT recognition (Computer vision), AUTOENCODERS, PATHOLOGY, ARTIFICIAL neural networks, IMAGE processing, ARTIFICIAL intelligence, MARKOV random fields, TRAINING needs

A Novel Risk-based Portfolio Optimization with Return Prediction Using Enhanced Heap Optimizer Aided Serial Cascaded Deep Residual Networks.

Autoren: Mehala, V. Sundar, D.

Quelle: International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. Sep2025, Vol. 33 Issue 6, p687-715. 29p.

Schlagwörter: *PORTFOLIO management (Investments), *EARNINGS forecasting, *RISK assessment, *STOCKS (Finance), MACHINE learning, DEEP learning

Efficient multiscale weighted features based residual ConvLSTM method to detect Parkinson's disease using electroencephalogram data.

Autoren: Rajesh Kumar, K. Ganesh Babu, T.R.

Quelle: Biomedical Signal Processing & Control. Nov2025, Vol. 109, pN.PAG-N.PAG. 1p.

Schlagwörter: Long short-term memory, Parkinson's disease, Optimization algorithms, Feature extraction, Autoencoder, Deep learning

Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning

Autoren: Tolooshams, Bahareh Dey, Sourav Ba, Demba

Index Begriffe: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1904.08827

Residual Sparse Autoencoders for Unsupervised Feature Learning and Its Application to HEp-2 Cell Staining Pattern Recognition

Autoren: Yen-Wei Chen Xian-Hua Han

Quelle: Intelligent Systems Reference Library ISBN: 9783030326050

Zugangs-URL: https://link.springer.com/chapter/10.1007%2F978-3-030-32606-7_11
https://rd.springer.com/chapter/10.1007/978-3-030-32606-7_11

Self-taught Learning with Residual Sparse Autoencoders for HEp-2 Cell Staining Pattern Recognition

Autoren: JiandDe Sun Yen-Wei Chen Xian-Hua Han et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030009182

Zugangs-URL: https://link.springer.com/chapter/10.1007/978-3-030-00919-9_16
https://dblp.uni-trier.de/db/conf/miccai/mlmi2018.html#HanSLC18
https://rd.springer.com/chapter/10.1007/978-3-030-00919-9_16

Multidisciplinary, Clinical Assessment of Accelerated Deep-Learning MRI Protocols at 1.5 T and 3 T After Intracranial Tumor Surgery and Their Influence on Residual Tumor Perception.

Autoren: Ruff, Christer Hauser, Till-Karsten Roder, Constantin et al.

Quelle: Diagnostics (2075-4418); Aug2025, Vol. 15 Issue 15, p1982, 27p

Schlagwörter: MAGNETIC resonance imaging, DEEP learning, TUMOR surgery, TUMORS, IMAGE quality analysis, INTERDISCIPLINARY research, HEALTH outcome assessment, BRAIN tumors

A residual autoencoder-based transformer for fault detection of multivariate processes.

Autoren: Shang, Jilin Yu, Jianbo

Quelle: Applied Soft Computing. Sep2024, Vol. 163, pN.PAG-N.PAG. 1p.

Schlagwörter: Artificial neural networks, Signal processing, Manufacturing processes, Attentional bias, Acquisition of data

AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model.

Autoren: Sirati-Amsheh, Mojtaba Shabaninia, Elham Chaparian, Ali

Quelle: Journal of Biomedical Physics & Engineering; Jun2025, Vol. 15 Issue 3, p271-280, 10p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, AUTOENCODERS, X-ray imaging, BONE measurement

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

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

Memory Residual Regression Autoencoder for Bearing Fault Detection.

Autoren: Huang, Xin Wen, Guangrui Dong, Shuzhi et al.

Quelle: IEEE Transactions on Instrumentation & Measurement. 2021, Vol. 70, p1-12. 12p.

Schlagwörter: *ARTIFICIAL neural networks, *KURTOSIS, INTRUSION detection systems (Computer security), ROLLER bearings, MEMORY, HILBERT-Huang transform

An Artificial Intelligence-Based Automatic Classifier for the Presence of False Lumen Thrombosis After Frozen Elephant Trunk Operation.

Autoren: Osswald, Anja Tsagakis, Konstantinos Thielmann, Matthias et al.

Quelle: Diagnostics (2075-4418); Dec2024, Vol. 14 Issue 24, p2853, 9p

Schlagwörter: ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, AORTIC dissection, COMPUTED tomography, AUTOENCODERS

Prediction of Recurrence using a Stacked Denoising Autoencoder and Multifaceted Feature Analysis of Pretreatment MRI in Patients with Nasopharyngeal Carcinoma.

Autoren: Liu Y Wang X Li J et al.

Quelle: Current radiopharmaceuticals [Curr Radiopharm] 2025; Vol. 18 (3), pp. 224-243.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Bentham Science Publishers Country of Publication: United Arab Emirates NLM ID: 101468718 Publication Model: Print Cited Medium: Internet ISSN: 1874-4729 (Electronic) Linking ISSN: 18744710 NLM ISO Abbreviation: Curr Radiopharm Subsets: MEDLINE

MeSH-Schlagworte: Nasopharyngeal Carcinoma*/diagnostic imaging , Nasopharyngeal Carcinoma*/radiotherapy , Nasopharyngeal Carcinoma*/pathology , Magnetic Resonance Imaging*/methods , Neoplasm Recurrence, Local*/diagnostic imaging , Nasopharyngeal Neoplasms*/diagnostic imaging , Nasopharyngeal Neoplasms*/radiotherapy , Nasopharyngeal Neoplasms*/pathology, Humans ; Female ; Male ; Middle Aged ; Adult ; Retrospective Studies ; Radiotherapy, Intensity-Modulated ; Aged ; Deep Learning ; Autoencoder

Intelligent Differentiation Framework for Lewy Body Dementia and Alzheimer's Disease Using Adaptive Multi-Cascaded ResNet–Autoencoder–LSTM Network.

Autoren: Sravani, K. RaviSankar, V.

Quelle: International Journal of Image & Graphics; Nov2025, Vol. 25 Issue 6, p1-30, 31p

Deep reinforcement learning autoencoder with RA-GAN and GAN

Autoren: Nguyen, Hoang-Sy Huynh, Cong-Danh

Quelle: International Journal of Advances in Intelligent Informatics; Vol 8, No 3 (2022): November 2022; 313-323; 2548-3161; 2442-6571

Index Begriffe: Artificial neural networks; Communication systems; Reinforcement; learning-based training; Channel models, info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: http://ijain.org/index.php/IJAIN/article/view/896
http://ijain.org/index.php/IJAIN/article/view/896/ijain_v8i3_p313-323
http://ijain.org/index.php/IJAIN/article/view/896/ijain_v8i3_p313-323

The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization.

Autoren: Xu, Feng Li, Zhiyong Wen, Bo et al.

Quelle: Geofluids; 3/22/2021, p1-11, 11p, 2 Color Photographs, 13 Diagrams, 1 Graph

Schlagwörter: PATTERN recognition systems, SEISMIC testing, CLUSTER analysis (Statistics), SURFACE fault ruptures, STATISTICS, SIGNAL convolution, VIDEO coding