Search Results - "Deep-stacked denoising autoencoder"

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Authors: Sanjay Roka Manoj Diwakar

Source: Journal of Electronic Imaging. 32

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

Authors: Paul Takyi-Aninakwa Shunli Wang Guangchen Liu et al.

Source: Renewable and Sustainable Energy Reviews. 218:115803

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

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

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

Authors: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) et al.

Contributors: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) et al.

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

Subject Terms: 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]

Subject Geographic: Amsterdam, Netherlands

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

Availability: 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

Authors: Mohammadamini, Mohammad Matrouf, Driss

Contributors: Mohammadamini, Mohammad Matrouf, Driss

Subject Terms: [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

File Description: application/pdf

Access URL: 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.

Authors: Roka, Sanjay Diwakar, Manoj

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

Subject Terms: 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.

Authors: Yamin, Mohammad Gedeon, Tom Bajaba, Saleh et al.

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

Subject Terms: 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.

Authors: Chacko, Nilah Ans Veedu, Nimmi Puthan Panayan, Abhinav et al.

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

Subject Terms: 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.

Authors: Kasegn, Setegn Asnakew Mwangi, Ronald Waweru Kimwele, Michael et al.

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

Subject Terms: 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

Authors: Li, Dana Vilmun, Bolette Mikela Carlsen, Jonathan Frederik et al.

Source: 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 .

Index Terms: 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.

Authors: Zhao, Rongyong Hua, Feng Wei, Bingyu et al.

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

Subject Terms: 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.

Authors: Borah, Kasmika Das, Himanish Shekhar Seth, Soumita et al.

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

Subject Terms: 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.

Authors: Li, Dana Mikela Vilmun, Bolette Frederik Carlsen, Jonathan et al.

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

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

Company/Entity: INSTITUTE of Electrical & Electronics Engineers

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

Authors: An, Jiyao Zhao, Jin Liu, Qingqin et al.

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

Subject Terms: 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.

Authors: Zheng, Zhanpeng Yin, Zhong Wang, Yongxiong et al.

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

Subject Terms: *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

Authors: Keying Liu Wentao Mao Huadong Shi et al.

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/72ec87c5b0f040bb89ded97470ad752f

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

Authors: Chamundeeswari, G. Srinivasan, S. Bharathi, S. Prasanna et al.

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

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

Company/Entity: UNIVERSITA di Pavia

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

Authors: Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua

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

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

File Description: 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

Availability: 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.

Authors: Kumar, Santosh Kumar, Sunil Shafi, Mehak et al.

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

Subject Terms: ENDANGERED species, FEATURE extraction, FACE, EXTRACTION techniques

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

Authors: Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua

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

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

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

Authors: Yu, Jianbo Zhang, Chengyi Wang, Shijin

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

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