Search Results - "Denoising Autoencoder"

Explainable Deep Learning Framework for SERS Bioquantification

Authors: Jihan K. Zaki Jakub Tomasik Jade A. McCune et al.

Contributors: Jihan K. Zaki Jakub Tomasik Jade A. McCune et al.

Source: ACS Sensors. 10:6597-6606

Subject Terms: FOS: Computer and information sciences, Serotonin, Computer Science - Machine Learning, urine analysis, SERS, deep learning, Spectrum Analysis, Raman, Other Quantitative Biology (q-bio.OT), CRIME, Quantitative Biology - Quantitative Methods, Quantitative Biology - Other Quantitative Biology, explainable AI, serotonin, Machine Learning (cs.LG), Deep Learning, denoising autoencoder, FOS: Biological sciences, Humans, biomarker quantification, Neural Networks, Computer, Biomarkers, Quantitative Methods (q-bio.QM)

File Description: application/pdf; application/zip; text/xml

Access URL: http://arxiv.org/abs/2411.08082

Implementation of a Multiple-Transmitter RS-OFDM-Based OCC System With Advanced ByteTrack for Mobility Environments

Authors: Herfandi Herfandi Ones Sanjerico Sitanggang Muhammad Rangga Aziz Nasution et al.

Source: IEEE Access, Vol 13, Pp 119411-119426 (2025)

Subject Terms: optical camera communication (OCC), denoising autoencoder, multi-transmitter decoding, Advanced ByteTrack, mobility environments, Electrical engineering. Electronics. Nuclear engineering, rolling shutter OFDM (RS-OFDM), TK1-9971

Access URL: https://doaj.org/article/13905263050a4558b7296edbf6ea8439

A Novel PAPR Reduction Scheme Based on Deep Autoencoder Network for FBMC Systems

Authors: Xing Cheng Deli Chen Shuaishuai Li et al.

Source: IEEE Access, Vol 13, Pp 68948-68958 (2025)

Subject Terms: peak to average power ratio, denoising autoencoder network, deep learning, Filter bank multicarrier, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/678c4448230241fc87fe49f3e61edf69

Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis

Authors: Jaesung Shim Kyuri Jo

Source: IEEE Access, Vol 13, Pp 86333-86343 (2025)

Subject Terms: Adversarial examples, denoising autoencoder, deep learning, Siamese network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/d1728c8ede5b409d99fd3fd94f3d6b9b

A novel 3D indoor localization method integrating deep spatial feature augmentation and attention-based denoising

Authors: Chunyuan Liu Xiaomin Yu Peilong Wu et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Subject Terms: 3D indoor positioning, Stacked variational autoencoder, Wasserstein generative adversarial network, Attention mechanism, Denoising autoencoder, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/7d6129d2fbb942f7bfc1b0de39fd8270

CNN-LSTM-AM approach for outdoor wireless optical communication systems

Authors: Montaser Abdelsattar Eman S. Amer Hamdy A. Ziedan et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-26 (2025)

Subject Terms: Deep learning, Bit error rate (BER), Deep denoising autoencoder (DDAE), Convolutional neural network (CNNs), Generative adversarial network (GAN), Gated recurrent unit (GRU), Medicine, Science

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Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/f045be58ddd14bdb94eb9e7e5d773a1a

Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering

Authors: Mohammad Abboush Christoph Knieke Andreas Rausch

Source: Results in Engineering, Vol 27, Iss, Pp 105900-(2025)

Subject Terms: Fault detection and clustering, Technology, Back-to-back testing, Deep denoising autoencoder, HIL testing, Real-time validation, Automotive software systems

Access URL: https://doaj.org/article/34c5052e622c4cb18ed2d1499b7ffeea

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Authors: Timothy K. Johnsen Xiangyu Bi Chunwei Chou et al.

Source: Water Resources Research, Vol 61, Iss 10, Pp n/a-n/a (2025)

Subject Terms: machine learning, denoising autoencoder, sensor noise reduction, uncertainty quantification, evapotranspiration, Environmental sciences, GE1-350

File Description: electronic resource

Relation: https://doaj.org/toc/0043-1397; https://doaj.org/toc/1944-7973

Access URL: https://doaj.org/article/86fb1f47d0ef444590d1c051eb05f97e

Data Quality Improvement Method for Power Equipment Condition Based on Stacked Denoising Autoencoders Improved by Particle Swarm Optimization

Authors: JI Rong, HOU Huijuan, SHENG Gehao, ZHANG Lijing, SHU Bo, JIANG Xiuchen

Source: Shanghai Jiaotong Daxue xuebao, Vol 59, Iss 6, Pp 780-788 (2025)

Subject Terms: power equipment, status data, stacked denoising autoencoder, data cleaning, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989

File Description: electronic resource

Relation: https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-6-780.shtml; https://doaj.org/toc/1006-2467

Access URL: https://doaj.org/article/a9b9c95033014d53b01068f2d1ca54df

Research on Reconstruction Method of Occluded Micro-Expressions Based on Quantized Denoising Autoencoder

Authors: LIU Hui, GUO Te, LIU Dong, LI Yingying

Source: Jisuanji gongcheng, Vol 51, Iss 5, Pp 288-304 (2025)

Subject Terms: occluded micro-expression recognition, feature reconstruction, optical flow, dynamic image, denoising autoencoder, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

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Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20250529.pdf; https://doaj.org/toc/1000-3428

Access URL: https://doaj.org/article/6dca6ccf5e1149279b8fd4d79d81dcd6

Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment

Authors: Eunmok Yang Sooyong Jeong Changho Seo

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-15 (2025)

Subject Terms: Cyberattack detection, Min–max scalar, Pelican optimization algorithm, Sparse denoising autoencoder, Fish migration optimizer, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/79e7a8faf92c4bf9b569dbce69c191ff

A deep learning approach to prediction of blood group antigens from genomic data

Authors: Camous Moslemi Susanne Sækmose Rune Larsen et al.

Source: Moslemi, C, Sækmose, S, Larsen, R, Brodersen, T, Bay, J T, Didriksen, M, Nielsen, K R, Bruun, M T, Dowsett, J, Dinh, K M, Mikkelsen, C, Hyvärinen, K, Ritari, J, Partanen, J, Ullum, H, Erikstrup, C, Ostrowski, S R, Olsson, M L & Pedersen, O B 2024, 'A deep learning approach to prediction of blood group antigens from genomic data', Transfusion, vol. 64, no. 11, pp. 2179-2195. https://doi.org/10.1111/trf.18013
Moslemi, C, Sækmose, S, Larsen, R, Brodersen, T, Bay, J T, Didriksen, M, Nielsen, K R, Bruun, M T, Dowsett, J, Dinh, K M, Mikkelsen, C, Hyvärinen, K, Ritari, J, Partanen, J, Ullum, H, Erikstrup, C, Ostrowski, S R, Olsson, M L & Pedersen, O B 2024, ' A deep learning approach to prediction of blood group antigens from genomic data ', Transfusion, vol. 64, no. 11, pp. 2179-2195 . https://doi.org/10.1111/trf.18013

Subject Terms: Male, Neural Networks, Genotype, Denmark, convolutional neural network, deep learning, Blood Donors, Genomics, Blood Group Antigens/genetics, Genomics/methods, Computer, genetic prediction, Deep Learning, AI, denoising autoencoder, Blood Group Antigens, blood types, Humans, Female, Neural Networks, Computer, Illumina GSA, Finland, blood antigen

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Access URL: https://pubmed.ncbi.nlm.nih.gov/39268576
https://vbn.aau.dk/da/publications/4f9f608b-0303-44a0-81ff-d5b29ee88a03
http://www.scopus.com/inward/record.url?scp=85203695657&partnerID=8YFLogxK
https://doi.org/10.1111/trf.18013
https://vbn.aau.dk/ws/files/754336085/Moslemi_et_al._2024_._A_deep_learning_approach_to_prediction_of_blood_group_antigens_from_genomic_data.pdf
https://curis.ku.dk/ws/files/413547537/Transfusion_2024_Moslemi_A_deep_learning_approach_to_prediction_of_blood_group_antigens_from_genomic_data.pdf
https://portal.findresearcher.sdu.dk/da/publications/dfe236f4-64ee-47aa-9884-5bb074600aec
https://doi.org/10.1111/trf.18013
https://pure.au.dk/portal/en/publications/50522a1c-a772-43cf-ba37-273fe15e2bf0
http://www.scopus.com/inward/record.url?scp=85203695657&partnerID=8YFLogxK
https://doi.org/10.1111/trf.18013

Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder

Authors: Asri Rizki Yuliani Hilman Ferdinandus Pardede Ade Ramdan et al.

Source: Journal of Mechatronics, Electrical Power, and Vehicular Technology, Vol 15, Iss 1, Pp 93-104 (2024)

Subject Terms: neural network, remaining useful life (rul), denoising autoencoder (dae), lithium-ion (li-ion) battery, TJ1-1570, system robustness, Electrical engineering. Electronics. Nuclear engineering, Mechanical engineering and machinery, 7. Clean energy, TK1-9971

Access URL: https://doaj.org/article/045065a2412848b69e82bedb7c7174af

A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium‐ion batteries

Authors: Wei Xia Jinli Xu Baolei Liu et al.

Source: Energy Science & Engineering, Vol 12, Iss 8, Pp 3390-3400 (2024)

Subject Terms: Technology, Science, remaining useful life, 02 engineering and technology, 7. Clean energy, 01 natural sciences, 0104 chemical sciences, denoising autoencoder, lithium‐ion batteries, reconstruction loss, 0210 nano-technology, CNN‐BiGRU

Access URL: https://doaj.org/article/16be299cf6cd47afbe649edb9f74b513

GENERATION: An Efficient Denoising Autoencoders-Based Approach for Amputated Image Reconstruction: An Efficient Denoising Autoencoders-Based Approach foAmputated Image Reconstruction

Authors: Othman, Leila Ben Niloofar, Parisa Yahia, Sadok Ben

Source: Proceedings of the 16th International Conference on Agents and Artificial Intelligence. :1237-1244

Subject Terms: Denoising Autoencoder, Missing Data Mechanism, 4. Education, Image Reconstruction, Amputation, Data Quality, Imputation

Machine Learning‐Based Denoising of Surface Solar Irradiance Simulated With Monte Carlo Ray Tracing

Authors: M. Reeze M. A. Veerman C. C. vanHeerwaarden

Source: Journal of Geophysical Research: Machine Learning and Computation, Vol 2, Iss 3, Pp n/a-n/a (2025)

Subject Terms: Monte Carlo ray tracing, shallow cumulus clouds, machine learning, denoising autoencoder, solar irradiance, Geophysics. Cosmic physics, QC801-809, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://doaj.org/toc/2993-5210

Access URL: https://doaj.org/article/83e5a0b30ecd4c7999545bf2d473e791

Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model

Authors: Can Qin Bo Liang Jia'an Niu et al.

Source: Array, Vol 27, Iss , Pp 100475- (2025)

Subject Terms: Lighting environment of road tunnel, Stacked denoising autoencoder, Particle swarm optimization, Information representation, Psychophysiological perception, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S259000562500102X; https://doaj.org/toc/2590-0056

Access URL: https://doaj.org/article/42d2b7245a534844bd1edcf6bbf34507

A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems

Authors: Chae Yoon Jung Jae-Mo Kang Dong In Kim

Source: ICT Express, Vol 10, Iss 2, Pp 400-405 (2024)

Subject Terms: Backscatter communication, 0508 media and communications, Beamforming, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, Channel estimation, Deep learning, Denoising autoencoder, Information technology, 02 engineering and technology, T58.5-58.64

Access URL: https://doaj.org/article/60e6f8807c1e43dc88d5cd7e99775171

Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information

Authors: Senyu Zhou Jian Luo Mei Tang et al.

Source: Front Pharmacol
Frontiers in Pharmacology, Vol 16 (2025)

Subject Terms: Pharmacology, CatBoost, cardiovascular disease, evolutionary information, protein–protein interactions, Therapeutics. Pharmacology, RM1-950, deep denoising autoencoder

Access URL: https://pubmed.ncbi.nlm.nih.gov/40135232
https://doaj.org/article/dfbaf48dbdb74080bd34cd4b29f4c845

Noise Removal from the Image Using Convolutional Neural Networks-Based Denoising Auto Encoder

Authors: Younus FAROOQ Serkan SAVAŞ

Source: Volume: 3, Issue: 121-28
Journal of Emerging Computer Technologies

Subject Terms: image noise, denoising autoencoder, convolutional neural network, image denoising, Görüntü İşleme, Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

File Description: application/pdf

Access URL: https://dergipark.org.tr/tr/pub/ject/issue/77437/1390428