Search Results - optimized temporal denoised convolutional autoencoder*

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

Authors: Zarina Begum Kareemulla Shaik

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

Subject Terms: 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

Access URL: https://doaj.org/article/a8ff8ca9acda4137b99f9fee50cebd36

Enhanced Noise Reduction in Photoplethysmography Signals Using a Denoising Autoencoder.

Authors: Shin-Chi Lai Yao-Feng Liang Yi-Chang Zhu et al.

Source: Sensors & Materials; 2025, Vol. 37 Issue 11, Part 3, p5123-5139, 17p

Subject Terms: PHOTOPLETHYSMOGRAPHY, CONVOLUTIONAL neural networks, NOISE control, SIGNAL processing, SIGNAL denoising, REAL-time computing, MEDICAL technology, WEARABLE technology

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets.

Authors: Salehiyan, Ahmad Moghaddam, Pardis Sadatian Kaveh, Masoud

Source: Future Internet; Jul2025, Vol. 17 Issue 7, p279, 34p

Subject Terms: INTRUSION detection systems (Computer security), EDGE computing, AUTOENCODERS, INTERNET security, CYBER physical systems, DEEP learning, GENERATIVE adversarial networks

Hybrid deep learning and machine learning approach for detecting spatial and temporal forgeries in videos.

Authors: Singh, Upasana Rathor, Sandeep Kumar, Manoj

Source: Neural Computing & Applications; Oct2025, Vol. 37 Issue 29, p23723-23737, 15p

Subject Terms: DEEP learning, MACHINE learning, BOOSTING algorithms, CONVOLUTIONAL neural networks, FEATURE extraction, CLASSIFICATION algorithms

A Review of Neural Network-Based Image Noise Processing Methods.

Authors: Volkov, Anton A. Kozlov, Alexander V. Cheremkhin, Pavel A. et al.

Source: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6088, 37p

Subject Terms: CONVOLUTIONAL neural networks, DIGITAL image processing, SIGNAL filtering, AUTOENCODERS, ARTIFICIAL neural networks, GENERATIVE adversarial networks, IMAGE quality analysis

A Hybrid Approach Based on a Windowed-EMD Temporal Convolution–Reallocation Network and Physical Kalman Filtering for Bearing Remaining Useful Life Estimation.

Authors: Wei, Zhe Lang, Lang Chen, Mo et al.

Source: Machines; Sep2025, Vol. 13 Issue 9, p802, 15p

Subject Terms: REMAINING useful life, KALMAN filtering, BEARING steel, DEEP learning, FEATURE extraction, HILBERT-Huang transform, CONVOLUTIONAL neural networks

Speech enhancement using complex depthwise convolutional recurrent network with self-attention mechanism.

Authors: Iqbal, Yasir Zhang, Tao Iqbal, Anjum et al.

Source: Signal, Image & Video Processing; Dec2025, Vol. 19 Issue 12, p1-9, 9p

Feature-Shuffle and Multi-Head Attention-Based Autoencoder for Eliminating Electrode Motion Noise in ECG Applications.

Authors: Wang, Szu-Ting Hsu, Wen-Yen Lai, Shin-Chi et al.

Source: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6322, 21p

Subject Terms: ELECTROCARDIOGRAPHY, AUTOENCODERS, NOISE, DEEP learning, SIGNAL-to-noise ratio, REAL-time computing, CARDIOVASCULAR disease diagnosis

scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling.

Authors: Wang, Shuo Liu, Yuanning Zhang, Hao et al.

Source: Biology Direct; 5/27/2025, Vol. 20 Issue 1, p1-25, 25p

Subject Terms: GRAPH neural networks, AUTOENCODERS, DEEP learning, K-nearest neighbor classification, SYSTEMS theory

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

Authors: Kurmi, Yashwant Viswanathan, Malvika Zu, Zhongliang

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

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

End-to-end deep learning for smart maritime threat detection: an AE–CNN–LSTM-based approach.

Authors: Anuja, R. Annrose, J.

Source: Scientific Reports; 10/17/2025, Vol. 15 Issue 1, p1-26, 26p

Subject Terms: INTRUSION detection systems (Computer security), INTERNET of things, REAL-time computing, DEEP learning, INTERNET security, MARITIME management

Remaining Useful Life Prediction of Cutting Tools Based on a Depthwise Separable TCN-BiLSTM Model with Temporal Attention.

Authors: Wang, Shaoyan Jia, Haili Wang, Aimin et al.

Source: Lubricants (2075-4442); Nov2025, Vol. 13 Issue 11, p507, 18p

Subject Terms: REMAINING useful life, CUTTING tools, FEATURE extraction, DEEP learning, INDUSTRIAL efficiency

Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials.

Authors: Moustakidis, Serafeim Stergiou, Konstantinos Gee, Matthew et al.

Source: Infrastructures; Mar2025, Vol. 10 Issue 3, p51, 27p

Subject Terms: STRUCTURAL health monitoring, FAST Fourier transforms, MARKOV processes, FIBER-reinforced plastics, AUTOENCODERS

A Novel Autoencoder-Based Design for Channel Estimation in Maritime OFDM Systems.

Authors: Yang, Yongjie Chao, Wenming Ma, Li et al.

Source: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3454, 15p

Subject Terms: CHANNEL estimation, ORTHOGONAL frequency division multiplexing, LONG short-term memory, ARTIFICIAL neural networks, MACHINE learning, AUTOENCODERS, RANDOM noise theory, MARINE communication

AI-Driven Anomaly Detection for Securing IoT Devices in 5G-Enabled Smart Cities.

Authors: Reis, Manuel J. C. S.

Source: Electronics (2079-9292); Jun2025, Vol. 14 Issue 12, p2492, 35p

Subject Terms: LONG short-term memory, FEDERATED learning, ANOMALY detection (Computer security), SMART cities, CONVOLUTIONAL neural networks, DEEP learning

A Real-Time Deep Learning Approach for Electrocardiogram-Based Cardiovascular Disease Prediction with Adaptive Drift Detection and Generative Feature Replay.

Authors: Zertal, Soumia Saighi, Asma Kouah, Sofia et al.

Source: Computer Modeling in Engineering & Sciences (CMES); 2025, Vol. 144 Issue 3, p3737-3782, 46p

Subject Terms: CARDIOVASCULAR diseases, DEEP learning, ELECTROCARDIOGRAPHY, PROGNOSIS, CONVOLUTIONAL neural networks, RECURRENT neural networks, MACHINE learning

Fault Diagnosis of Rolling Bearings Using Denoising Multi-Channel Mixture of CNN and Mamba-Enhanced Adaptive Self-Attention LSTM.

Authors: Lai, Songjiang Cheung, Tsun-Hin Fung, Ka-Chun et al.

Source: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6652, 32p

Subject Terms: FAULT diagnosis, DEEP learning, SIGNAL processing, FEATURE extraction, LONG short-term memory, ROLLER bearings

TIC-FusionNet: A multimodal deep learning framework with temporal decomposition and attention-based fusion for time series forecasting.

Authors: Chen, Liyu Fan, Xiangwei

Source: PLoS ONE; 10/9/2025, Vol. 20 Issue 10, p1-35, 35p

Subject Terms: DEEP learning, MACHINE learning, PATTERN perception, MULTISENSOR data fusion, BUSINESS forecasting, FORECASTING

A proposed deep learning model for multichannel ECG noise reduction.

Authors: Maurya, Jay Prakash Manoria, Manish Joshi, Sunil

Source: Discover Artificial Intelligence; 5/22/2025, Vol. 5 Issue 1, p1-16, 16p

Subject Terms: ELECTROCARDIOGRAPHY, NOISE control, HEART diseases, DEEP learning, SIGNAL processing, CONVOLUTIONAL neural networks, MATHEMATICAL regularization

Deep learning driven electrocardiogram classification with optimized convolutional neural network for accurate arrhythmia detection and explainable clinical decision support.

Authors: Priya, Anthani Kamala Madireddy, Bhavani

Source: International Journal of Basic & Applied Science; 2025, Vol. 14 Issue 1, p251-263, 13p

Subject Terms: CONVOLUTIONAL neural networks, CLINICAL decision support systems, DEEP learning, ARTIFICIAL intelligence, FEATURE extraction, ARRHYTHMIA