Výsledky vyhľadávania - 1D conventional autoencoder~

Design of Identification System Based on Machine Tools’ Sounds Using Neural Networks

Autori: Fusaomi Nagata Tomoaki Morimoto Keigo Watanabe a ďalší

Zdroj: Designs, Vol 9, Iss 5, p 121 (2025)

Predmety: 1D CNN, autoencoder, sound block (SB), SB data-based FCDD, identification, anomaly detection, Technology, Engineering design, TA174

Popis súboru: electronic resource

Relation: https://www.mdpi.com/2411-9660/9/5/121; https://doaj.org/toc/2411-9660

Prístupová URL adresa: https://doaj.org/article/51b1c73e5d2543088a10d969241b2a5f

Multi-model deep learning approach for collaborative filtering recommendation system

Autori: Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah

Zdroj: CAAI Transactions on Intelligence Technology (2020)

Predmety: recommender systems, collaborative filtering, learning (artificial intelligence), neural nets, matrix decomposition, conventional collaborative filtering techniques, user preferences, item latent features, deep neural networks, maps user, item features, multimodel deep learning approach, item functions, mmdl approach, deep autoencoder, one-dimensional convolution neural network model, collaborative filtering recommendation system, implicit feedback, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765

Popis súboru: electronic resource

Relation: https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0031; https://doaj.org/toc/2468-2322

Prístupová URL adresa: https://doaj.org/article/9a214fc8df424f39bf7d1d5f04caeb34

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

Autori: Park, Yeonjin Hong, Jungpyo

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

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

A hybrid 1DCNN-GRU deep learning framework for classifying caprine granulosa cell fertility potential using single-cell transcriptomics.

Autori: Sananmuang, Thanida Puthier, Denis Chokeshaiusaha, Kaj

Zdroj: Veterinary World. Jul2025, Vol. 18 Issue 7, p1922-1935. 14p.

Predmet: Granulosa cells, Fertility, Convolutional neural networks, Transcriptomes, Deep learning, Recurrent neural networks, Goats, Biomarkers

Autoencoder with Constraints for Subsurface Defect Detection in Quadratic Frequency Modulated Thermal Wave Imaging.

Autori: Bandari, Enosh Oleti, Siva Subrahmanyam Prasanthi, Y. Naga a ďalší

Zdroj: Sensing & Imaging; 8/18/2025, Vol. 26 Issue 1, p1-17, 17p

Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder.

Autori: Wang, Haoran Han, Zhongze Xiong, Xiaoshuang a ďalší

Zdroj: Machines; May2024, Vol. 12 Issue 5, p309, 17p

Predmety: ARTIFICIAL neural networks, YARN, PROBABILITY density function, FAULT diagnosis, PLANT maintenance, SPECTROGRAMS

Autoencoders and their applications in machine learning: a survey.

Autori: Berahmand, Kamal Daneshfar, Fatemeh Salehi, Elaheh Sadat a ďalší

Zdroj: Artificial Intelligence Review; Feb2024, Vol. 57 Issue 2, p1-52, 52p

Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.

Autori: Burgess, James Nirschl, Jeffrey J. Zanellati, Maria-Clara a ďalší

Zdroj: Nature Communications; 2/3/2024, Vol. 15 Issue 1, p1-14, 14p

Predmety: CELL morphology, ORGANELLES, CYTOLOGY, IMAGE analysis, OUTLIER detection, VISUAL cryptography

EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism.

Autori: Bansal, Jayoti Gangwar, Gaurav Aljaidi, Mohammad a ďalší

Zdroj: Brain Sciences (2076-3425); Jan2025, Vol. 15 Issue 1, p95, 22p

Predmety: CHILD patients, FEATURE selection, DEEP learning, AUTOENCODERS, FEATURE extraction

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

Autori: Xu, Feng Li, Zhiyong Wen, Bo a ďalší

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

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

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

Autori: Tao R Li H Lu J a ďalší

Zdroj: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Oct; Vol. 62 (10), pp. 3089-3106. Date of Electronic Publication: 2024 May 22.

Spôsob vydávania: Journal Article

Informácie o časopise: Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE

Výrazy zo slovníka MeSH: Diabetic Retinopathy*/diagnosis , Blood Glucose*/analysis , Neural Networks, Computer*, Humans ; Diabetes Mellitus, Type 2/diagnosis ; Diabetes Mellitus, Type 2/blood ; Blood Glucose Self-Monitoring/methods ; Blood Glucose Self-Monitoring/instrumentation ; Wearable Electronic Devices ; Deep Learning ; Male ; Female ; Algorithms

基于时空自编码网络的风电齿轮箱状态监测. (Chinese)

Alternate Title: Condition Monitoring of Wind Turbine Gearbox Based on Spatial-temporal Autoencoder Network. (English)

Autori: 刘长良 徐健 王梓齐

Zdroj: Information & Control; 2023, Vol. 52 Issue 1, p84-92, 9p

Automatic Baseline Correction of 1D Signals Using a Parameter-Free Deep Convolutional Autoencoder Algorithm.

Autori: Górski, Łukasz Jakubowska, Małgorzata

Zdroj: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p12069, 18p

Predmety: AUTOENCODERS, ANALYTICAL chemistry, DEEP learning, ALGORITHMS, SIGNAL processing

A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data.

Autori: Xuan, Wenbo Chen, Pengchao Li, Rui a ďalší

Zdroj: Scientific Reports; 4/3/2025, Vol. 15 Issue 1, p1-13, 13p

Interplay of Power-Law correlated Disorder and Long-Range Hopping in One Dimension: Mobility Edges, Criticality, and ML-Based Phase Identification

Autori: Pouranvari, Mohammad

Predmety: Strongly Correlated Electrons

Relation: http://arxiv.org/abs/2511.18341

Dostupnosť: http://arxiv.org/abs/2511.18341

Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction.

Autori: Liu, Yaojie Fan, Haoyu Jin, Yan a ďalší

Zdroj: Remote Sensing; Nov2025, Vol. 17 Issue 22, p3729, 32p

Predmety: SOIL moisture, REMOTE sensing, CLIMATE research, FEATURE extraction, AUTOENCODERS, HYDROLOGICAL research, ELECTRONIC data processing, ENVIRONMENTAL research

Geografický termín: CHINA

Convolutional Variational Autoencoders and Resampling Techniques with Generative Adversarial Network for Enhancing Internet of Thing Security.

Autori: Dong, Huiyao Kotenko, I. V.

Zdroj: Pattern Recognition & Image Analysis; Sep2024, Vol. 34 Issue 3, p562-569, 8p

Design of Identification System Based on Machine Tools' Sounds Using Neural Networks.

Autori: Nagata, Fusaomi Morimoto, Tomoaki Watanabe, Keigo a ďalší

Zdroj: Designs; Oct2025, Vol. 9 Issue 5, p121, 18p

Predmety: MACHINE tools, ARTIFICIAL neural networks, SOUND waves, TIME series analysis, NUMERICAL control of machine tools, DEEP learning, OUTLIER detection, VISUALIZATION

基于1DResAE网络模型的车轮多边形检测研究. (Chinese)

Alternate Title: Research on Wheel Polygon Detection Based on 1DResAE Network Model. (English)

Autori: 林凤涛 倪鹏辉 杜磊 a ďalší

Zdroj: Journal of East China Jiaotong University; Jun2025, Vol. 42 Issue 3, p96-107, 12p

Predmety: ARTIFICIAL neural networks, PATTERN recognition systems, SUPERVISED learning, DEEP learning, AUTOENCODERS

Classification of Imbalanced Data Using SMOTE and AutoEncoder Based Deep Convolutional Neural Network.

Autori: Alex, Suja A. Nayahi, J. Jesu Vedha

Zdroj: International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. Jun2023, Vol. 31 Issue 3, p437-469. 33p.

Predmety: *DATABASES, CONVOLUTIONAL neural networks, DEEP learning, MACHINE learning, DATA augmentation, NOISE control