Výsledky vyhledávání - "deep stacked denoising autoencoder"

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Autoři: Sanjay Roka Manoj Diwakar

Zdroj: Journal of Electronic Imaging. 32

Deep learning framework designed for high-performance lithium-ion batteries state monitoring

Autoři: Paul Takyi-Aninakwa Shunli Wang Guangchen Liu a další

Zdroj: Renewable and Sustainable Energy Reviews. 218:115803

Témata: Lithium-ion batteries, State of charge estimation, Secondary scale feature extraction, Long short-term memory, Deep-stacked denoising autoencoder

Přístupová URL adresa: https://rgu-repository.worktribe.com/output/2830428

Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments

Autoři: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) a další

Přispěvatelé: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) a další

Zdroj: EUSIPCO ; https://hal.science/hal-02882342 ; EUSIPCO, Jan 2021, Amsterdam, Netherlands

Témata: speaker recognition, x-vector, data augmentation, noise compensation, denoising autoencoder, deep stacked denoising autoencoder, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Geografické téma: Amsterdam, Netherlands

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2006.15903; ARXIV: 2006.15903

Dostupnost: https://hal.science/hal-02882342
https://hal.science/hal-02882342v1/document
https://hal.science/hal-02882342v1/file/Data%20augmentation%20versus%20noise%20compensation%20for%20xvector%20speaker%20recognition%20systems%20in%20noisy.pdf

Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments

Autoři: Mohammadamini, Mohammad Matrouf, Driss

Přispěvatelé: Mohammadamini, Mohammad Matrouf, Driss

Témata: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Computation and Language, deep stacked denoising autoencoder, speaker recognition, 02 engineering and technology, Computer Science - Sound, x-vector, noise compensation, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Audio and Speech Processing (eess.AS), denoising autoencoder, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), data augmentation, Electrical Engineering and Systems Science - Audio and Speech Processing

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2006.15903
https://hal.science/hal-02882342v1
https://hal.science/hal-02882342v1/document

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance.

Autoři: Roka, Sanjay Diwakar, Manoj

Zdroj: Journal of Electronic Imaging; May/Jun2023, Vol. 32 Issue 3, p33015-033015-23, 1p

Témata: VIDEO surveillance, ANOMALY detection (Computer security), IMAGE denoising, OPTICAL flow, CONVOLUTIONAL neural networks, DEEP learning

Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning.

Autoři: Yamin, Mohammad Gedeon, Tom Bajaba, Saleh a další

Zdroj: Computers, Materials & Continua; 2023, Vol. 75 Issue 3, p5659-5674, 16p

Témata: DEEP learning, HUMAN fingerprints, MACHINE learning, EVOLUTIONARY algorithms, NATURAL language processing, PATTERN recognition systems, COMPUTER vision

Role of Deep Learning in Neurodevelopmental Disorders: A Narrative Review.

Autoři: Chacko, Nilah Ans Veedu, Nimmi Puthan Panayan, Abhinav a další

Zdroj: Journal of Clinical & Diagnostic Research; Nov2025, Vol. 19 Issue 11, p1-8, 8p

Témata: DEEP learning, AUTISM spectrum disorders, PEDIATRIC neurology, ATTENTION-deficit hyperactivity disorder, DIAGNOSTIC examinations, NEURAL development

Enhancing Surveillance Video Abnormal Behavior Detection Using Deep Convolutional Generative Adversarial Network With GRU Model.

Autoři: Kasegn, Setegn Asnakew Mwangi, Ronald Waweru Kimwele, Michael a další

Zdroj: Engineering Reports; May2025, Vol. 7 Issue 5, p1-21, 21p

Témata: GENERATIVE adversarial networks, VIDEO surveillance, FEATURE extraction, VIDEOS

The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI:A systematic review

Autoři: Li, Dana Vilmun, Bolette Mikela Carlsen, Jonathan Frederik a další

Zdroj: Li , D , Vilmun , B M , Carlsen , J F , Albrecht-Beste , E , Lauridsen , C A , Nielsen , M B & Hansen , K L 2019 , ' The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI : A systematic review ' , Diagnostics , vol. 9 , no. 4 , 9040207 .

Témata: Artificial intelligence, Deep learning, Nodule classification, Nodule detection, article

URL: https://researchprofiles.ku.dk/da/publications/the-performance-of-deep-learning-algorithms-on-automatic-pulmonary-nodule-detection-and-classification-tested-on-different-datasets-that-are-not-derived-from-lidcidri(586507b5-0cbe-4f7a-a3af-ecd3f740a034).html
https://doi.org/10.3390/diagnostics9040207
https://curis.ku.dk/ws/files/237755575/The_Performance_of_Deep_Learning_Algorithms_on_Automatic_Pulmonary_Nodule_Detection_and_Classification_Tested_on_Different_Datasets_That_Are_Not_Derived_from_LIDC_IDRI.pdf

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision.

Autoři: Zhao, Rongyong Hua, Feng Wei, Bingyu a další

Zdroj: Applied Sciences (2076-3417); Nov2024, Vol. 14 Issue 21, p9758, 30p

Témata: COLLECTIVE behavior, COMPUTER vision, COMPUTER engineering, PUBLIC spaces, SOFTWARE development tools, DEEP learning

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Autoři: Borah, Kasmika Das, Himanish Shekhar Seth, Soumita a další

Zdroj: Functional & Integrative Genomics; Oct2024, Vol. 24 Issue 5, p1-31, 31p

Témata: MACHINE learning, NUCLEOTIDE sequencing, FEATURE selection, FEATURE extraction, EXTRACTION techniques, DEEP learning, STATISTICAL learning

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review.

Autoři: Li, Dana Mikela Vilmun, Bolette Frederik Carlsen, Jonathan a další

Zdroj: Diagnostics (2075-4418); Dec2019, Vol. 9 Issue 4, p207-207, 1p

Témata: DEEP learning, ARTIFICIAL neural networks, PULMONARY nodules, MACHINE learning, META-analysis, SIGNAL convolution

Korporace: INSTITUTE of Electrical & Electronics Engineers

Self-Constructed Deep Fuzzy Neural Network for Traffic Flow Prediction.

Autoři: An, Jiyao Zhao, Jin Liu, Qingqin a další

Zdroj: Electronics (2079-9292); Apr2023, Vol. 12 Issue 8, p1885, 23p

Témata: FUZZY neural networks, DEEP learning, TRAFFIC flow, INTELLIGENT transportation systems, TRAFFIC congestion, CITY traffic, CITIES & towns

People: MENDEL, Gregor, 1822-1884

Inter-subject cognitive workload estimation based on a cascade ensemble of multilayer autoencoders.

Autoři: Zheng, Zhanpeng Yin, Zhong Wang, Yongxiong a další

Zdroj: Expert Systems with Applications. Jan2023, Vol. 211, pN.PAG-N.PAG. 1p.

Témata: *DISTRIBUTION (Probability theory), *COMPUTATIONAL complexity, *ELECTROENCEPHALOGRAPHY, *INDIVIDUAL differences, *DATA recorders & recording

A New Unsupervised Online Early Fault Detection Framework of Rolling Bearings Based on Granular Feature Forecasting

Autoři: Keying Liu Wentao Mao Huadong Shi a další

Zdroj: IEEE Access, Vol 9, Pp 159684-159698 (2021)

Témata: Early fault detection, anomaly detection, streaming data, denoising autoencoder, alarm threshold, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/9634014/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/72ec87c5b0f040bb89ded97470ad752f

Optimal deep convolutional neural network based crop classification model on multispectral remote sensing images.

Autoři: Chamundeeswari, G. Srinivasan, S. Bharathi, S. Prasanna a další

Zdroj: Microprocessors & Microsystems. Oct2022, Vol. 94, pN.PAG-N.PAG. 1p.

Témata: *ARTIFICIAL neural networks, *CONVOLUTIONAL neural networks, *REMOTE sensing, *AGRICULTURAL productivity, *FOREST degradation, *SUSTAINABLE urban development

Korporace: UNIVERSITA di Pavia

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals

Autoři: Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua

Zdroj: Chinese Control Conference (CCC) ; 39 ; 2020 39th Chinese Control Conference (CCC) ; 6237-6242

Témata: Cognitive workloads, Electroencephalograms, Ensemble learning, Extreme learning machines, Stacked denoising autoencoders

Popis souboru: application/pdf

Relation: 2020 39th Chinese Control Conference (CCC); Chinese Control Conference (CCC);2020 39th Chinese Control Conference (CCC); https://ieeexplore.ieee.org/document/9188806; National Natural Science Foundation of China: 61703277; Shanghai Sailing Program: 17YF1427000; https://hdl.handle.net/11250/2772135; https://doi.org/10.23919/CCC50068.2020.9188806; cristin:1882942

Dostupnost: https://hdl.handle.net/11250/2772135
https://doi.org/10.23919/CCC50068.2020.9188806

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques.

Autoři: Kumar, Santosh Kumar, Sunil Shafi, Mehak a další

Zdroj: Multimedia Tools & Applications; Sep2022, Vol. 81 Issue 21, p31075-31106, 32p

Témata: ENDANGERED species, FEATURE extraction, FACE, EXTRACTION techniques

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals

Autoři: Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua

Relation: https://ieeexplore.ieee.org/document/9188806

Dostupnost: https://hdl.handle.net/11250/2772135

Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes.

Autoři: Yu, Jianbo Zhang, Chengyi Wang, Shijin

Zdroj: Neural Computing & Applications; Apr2021, Vol. 33 Issue 8, p3085-3104, 20p

Témata: FAULT diagnosis, MANUFACTURING processes, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, SIGNAL processing, WAVELET transforms