Suchergebnisse - optimized temporal denoised convolutional autoencoder~

Optimized Temporal Denoised Convolutional Autoencoder for Enhanced ADHD Classification Using fMRI Data

Autoren: Zarina Begum Kareemulla Shaik

Quelle: IEEE Access, Vol 13, Pp 29031-29045 (2025)

Schlagwörter: Attention deficit hyperactivity disorder, optimized temporal denoised convolutional autoencoder, deep learning, adaptive osprey optimization, Electrical engineering. Electronics. Nuclear engineering, spatial features, fMRI images, TK1-9971

Zugangs-URL: https://doaj.org/article/a8ff8ca9acda4137b99f9fee50cebd36

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder.

Autoren: Kurmi, Yashwant Viswanathan, Malvika Zu, Zhongliang

Quelle: Magnetic Resonance in Medicine; Dec2024, Vol. 92 Issue 6, p2404-2419, 16p

Schlagwörter: OVERHAUSER effect (Nuclear physics), MAGNETIZATION transfer, DEEP learning, IMAGE denoising, PROTONS

Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism.

Autoren: Yan, Hao Si, Xiangfeng Liang, Jianqiang et al.

Quelle: Sensors (14248220); Dec2024, Vol. 24 Issue 24, p8053, 19p

Schlagwörter: FAULT diagnosis, AUTOENCODERS, INDUSTRIAL applications, SCREWS, MACHINERY, DEEP learning

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.

Autoren: Zeng, Peipei Kang, Shuimiao Fan, Fan et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-15, 15p

Schlagwörter: ANOMALY detection (Computer security), HEART murmurs, SUPPORT vector machines, CONGENITAL heart disease, AUTOENCODERS

Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks.

Autoren: Liu, Yu Liu, Yuanbo Yang, Yan

Quelle: Entropy; Sep2023, Vol. 25 Issue 9, p1316, 33p

Schlagwörter: CONVOLUTIONAL neural networks, ROLLING contact fatigue, GAUSSIAN mixture models, TIME-varying networks, PRINCIPAL components analysis, FORECASTING

Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals.

Autoren: Anderson, Mark R. Basu, Vasundhara Martin, Ryan D. et al.

Quelle: European Physical Journal C -- Particles & Fields; Dec2022, Vol. 82 Issue 12, p1-16, 16p

Schlagwörter: ELECTRONIC noise, GERMANIUM detectors, PARTICLE physics, PHYSICS experiments, NEUTRINOLESS double beta decay, DETECTORS, TIME management

Denoising-autoencoder-facilitated MEMS computational spectrometer with enhanced resolution on a silicon photonic chip.

Autoren: Zhou, Jing Zhang, Hui Qiao, Qifeng et al.

Quelle: Nature Communications; 11/26/2024, Vol. 15 Issue 1, p1-12, 12p

Schlagwörter: SIGNAL-to-noise ratio, SPECTROMETERS, PHOTONICS, SILICON, INTERFEROMETERS, IMAGE denoising

Fast Dual Deep Convolutional Autoencoder Network for Denoising Monte Carlo Rendering.

Autoren: Alzbier, Ahmed Mustafa Taha Chunyi Chen Zhongye Shen et al.

Quelle: Journal of Imaging Science & Technology; May/Jun2023, Vol. 67 Issue 3, p1-12, 12p

Schlagwörter: DEEP learning, CONVOLUTIONAL neural networks, PIXELS, IMAGE reconstruction, SIGNAL-to-noise ratio

A high comprehensive performance ECG noise reduction architecture based on conditional generative adversarial net.

Autoren: Zeng, Ming Tian, Lan Liu, Guoyang et al.

Quelle: Biomedical Signal Processing & Control. Jan2026, Vol. 111, pN.PAG-N.PAG. 1p.

Schlagwörter: Signal denoising, Electrocardiography, Noise control, Heart disease diagnosis, Research evaluation, Arrhythmia

Ultrasonic Weld Quality Inspection Involving Strength Prediction and Defect Detection in Data-Constrained Training Environments.

Autoren: Mohandas, Reenu Mongan, Patrick Hayes, Martin

Quelle: Sensors (14248220); Oct2024, Vol. 24 Issue 20, p6553, 26p

Schlagwörter: ULTRASONIC welding, WELDING defects, MANUFACTURED products, WELDING inspection, X-ray imaging

RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and Autoencoder Modeling.

Autoren: Guan, Peng Zhang, Tianrui Zhou, Lianhong

Quelle: Processes; Sep2024, Vol. 12 Issue 9, p1831, 23p

Schlagwörter: REMAINING useful life, ROLLER bearings, INDUSTRIAL equipment, SIGNAL processing, PREDICTION models

Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure.

Autoren: Mir, Waseem Ahmad Anjum, Mohd Izharuddin et al.

Quelle: Diagnostics (2075-4418); Feb2023, Vol. 13 Issue 4, p773, 17p

Schlagwörter: EPILEPSY, DEEP learning, DIAGNOSIS, DIAGNOSIS of epilepsy, NEUROLOGICAL disorders, SEIZURES (Medicine)

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

Autoren: Johnsen, Timothy K. Bi, Xiangyu Chou, Chunwei et al.

Quelle: Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-23, 23p

Schlagwörter: EVAPOTRANSPIRATION, AUTOENCODERS, STOCHASTIC analysis, MACHINE learning, HYDROLOGICAL forecasting, NOISE control, TELEMETRY

DuSAFNet: A Multi-Path Feature Fusion and Spectral–Temporal Attention-Based Model for Bird Audio Classification.

Autoren: Lu, Zhengyang Li, Huan Liu, Min et al.

Quelle: Animals (2076-2615); Aug2025, Vol. 15 Issue 15, p2228, 40p

Schlagwörter: BIODIVERSITY monitoring, BIODIVERSITY conservation, DEEP learning, TIME-frequency analysis, BIRD vocalizations, ACOUSTIC signal detection

Classification of COVID-19 Using Temporal and Spectral Features of Cough Sounds.

Autoren: Tessema, Biruk Abera

Quelle: International Journal Bioautomation; 2025, Vol. 29 Issue 1, p5-18, 14p

EEG-based cross-subject passive music pitch perception using deep learning models.

Autoren: Meng, Qiang Tian, Lan Liu, Guoyang et al.

Quelle: Cognitive Neurodynamics; 2025, Vol. 19 Issue 1, p1-19, 19p

Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps.

Autoren: Akbari, Hesam Korani, Wael Muhammad, Sadiq et al.

Quelle: Brain Sciences (2076-3425); Sep2025, Vol. 15 Issue 9, p977, 31p

Schlagwörter: MENTAL depression, TREATMENT effectiveness, SEROTONIN uptake inhibitors, TRANSCRANIAL magnetic stimulation, ARTIFICIAL neural networks, MENTAL health, ELECTROENCEPHALOGRAPHY, COMPUTER-aided diagnosis

Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach.

Autoren: Dasan, Evangelin Gnanaraj, Rajakumar

Quelle: Circuits, Systems & Signal Processing; Nov2022, Vol. 41 Issue 11, p6152-6181, 30p

Schlagwörter: SIGNAL reconstruction, IMAGE compression, MACHINE learning, BIOMEDICAL signal processing, VITAL signs, DATA compression, WEARABLE technology, VIDEO compression

EPILEPTIC EEG SIGNALS RHYTHMS ANALYSIS IN THE DETECTION OF FOCAL AND NON-FOCAL SEIZURES BASED ON OPTIMISED MACHINE LEARNING AND DEEP NEURAL NETWORK ARCHITECTURE.

Autoren: SAMINU, SANI XU, GUIZHI SHUAI, ZHANG et al.

Quelle: Journal of Mechanics in Medicine & Biology; Feb2024, Vol. 24 Issue 1, p1-33, 33p

Schlagwörter: DEEP learning, SEIZURES (Medicine), MACHINE learning, ELECTROENCEPHALOGRAPHY, EPILEPSY, PRINCIPAL components analysis, DEEP brain stimulation, CIRCADIAN rhythms

Dual Channel Residual Learning for Denoising Path Tracing.

Autoren: Liu, Ping Ji, Hangyu

Quelle: International Journal of Image & Graphics; Jan2025, Vol. 25 Issue 1, p1-20, 20p