Search Results - Hybrid robust convolutional autoencoder*

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises by Yan, Shen, Shao, Haidong, Xiao, Yiming, Liu, Bin, Wan, Jiafu

“…•A new FDD loss function to suppress the noises is designed.•Construct the PCDF module to enhance the robustness of the network.•The unsupervised anomaly…”
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DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing by Hadi, Fazal, Yang, Jingxiang, Ullah, Matee, Ahmad, Irfan, Farooque, Ghulam, Xiao, Liang

“… In this paper, we present a new method for robust supervised HSU based on a deep hybrid (3D and 2D) convolutional autoencoder…”
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Hybrid Graph Convolutional Network With Online Masked Autoencoder for Robust Multimodal Cancer Survival Prediction by Hou, Wentai, Lin, Chengxuan, Yu, Lequan, Qin, Jing, Yu, Rongshan, Wang, Liansheng

“… This manuscript proposes a novel hybrid graph convolutional network, entitled HGCN, which is equipped with an online masked autoencoder paradigm for robust multimodal cancer survival prediction…”
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Hybrid Attention-Enhanced Regularized Convolutional Autoencoder for Robust EEG Feature Extraction in Seizure Prediction by Chen, Yu-Hao, Chen, Hsiang-Yu, Chen, Yu-Chia, Chen, Hsuan-Fu, Chen, Hsiang-Han

“… Consequently, this study proposes a novel hybrid attention-enhanced regularized convolutional autoencoder (HA-RCAE…”
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Robust Sensor Fault Detection in Wireless Sensor Networks Using a Hybrid Conditional Generative Adversarial Networks and Convolutional Autoencoder by Khan, Rehan, Saeed, Umer, Koo, Insoo

“… Addressing the challenge of sensor fault detection, we propose a novel hybrid technique to enhance the classification of sensor fault data in WSNs…”
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A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network by Kosana, Vishalteja, Madasthu, Santhosh, Teeparthi, Kiran

“…) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components…”
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Hybrid data-driven feature extraction-enabled surface modeling for metal additive manufacturing by Shi, Zhangyue, Mandal, Soumya, Harimkar, Sandip, Liu, Chenang

“…Metal additive manufacturing (AM) has become popular in a large variety of applications due to its excellent capabilities of handling complex geometries and…”
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Driver distracted detection using a new hybrid deep convolutional neural network based on Autoencoder by Hassam, Dhulfekar Dhurgham, Mohamme, Ayas Talib, Sadiq, Arcelan S, Salman, Baraa Yousif

“… To counter these problems, we propose a novel deep convolutional network with the introduction of an Autoencoder-based feature dimensionality reduction, an Attention mechanism towards better feature…”
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RACMF: robust attention convolutional matrix factorization for rating prediction by Zeng, Biqing, Shang, Qi, Han, Xuli, Zeng, Feng, Zhang, Min

“… To overcome the data sparsity problem, we present a hybrid model named robust attention convolutional matrix factorization (RACMF…”
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Noise robust damage detection of laminated composites using multichannel wavelet-enhanced deep learning model by Azad, Muhammad Muzammil, Kim, Heung Soo

“… Therefore, this study proposes a hybrid deep learning framework called a multi-channel convolutional autoencoder-support vector machine (MC-CAE-SVM…”
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Unsupervised Convolutional Transformer Autoencoder for Robust Health Indicator Construction and RUL Prediction in Rotating Machinery by Dahal, Amrit, Huang, Hong-Zhong, Huang, Cheng-Geng, Huang, Tudi, Khanal, Smaran, Niazi, Sajawal Gul

“… Specifically, a sequential autoencoder integrating a convolutional neural network (CNN) and vision Transformer (Vi…”
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3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance by Shin, Wonsup, Bu, Seok-Jun, Cho, Sung-Bae

“… We propose a hybrid deep learning model composed of a video feature extractor trained by generative adversarial network with deficient anomaly data and an anomaly detector boosted by transferring the extractor…”
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Enhanced Intelligent Video Monitoring using Hybrid Integration of Spatiotemporal Autoencoders and Convolutional LSTMs by Umale-Nagmote, Ankita, Goel, Charu, Lal, Nidhi

“… This paper proposes a hybrid deep learning framework that combines spatial-temporal autoencoders with convolutional LSTMs for automated anomaly detection in surveillance videos…”
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Anomaly detection for multivariate times series through the multi-scale convolutional recurrent variational autoencoder by Xie, Tianming, Xu, Qifa, Jiang, Cuixia

“… It is a hybrid of convolutional autoencoder and convolutional long short-term memory with variational autoencoder (ConvLSTM-VAE…”
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Adaptive hybrid convolutional neural network-autoencoder framework for backdoor detection in GenAI-driven semantic communication systems by El Alami, Hassan, Rawat, Danda B.

“… To address these challenges, we propose a hybrid framework, CNN-AAE, which combines a Convolutional Neural Network (CNN…”
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Automatic Facial Expression Recognition Using Modified LPQ and HOG Features with Stacked Deep Convolutional Autoencoders by Naveen Kumar, H. N., Patil, Chandrashekar M., Nagaraja, B. G., Jain, Amith K., Sudheesh, K. V., Mahadevaswamy, S

“… and histogram of oriented gradients feature; multi-stage stacked deep convolutional autoencoder (SDCA…”
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Massive Ultrasonic Data Compression Using Wavelet Packet Transformation Optimized by Convolutional Autoencoders by Wang, Boyang, Saniie, Jafar

“…Ultrasonic signal acquisition platforms generate considerable amounts of data to be stored and processed, especially when multichannel scanning or beamforming…”
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Revolutionary hybrid ensembled deep learning model for accurate and robust side-channel attack detection in cloud computing by Reddy, C. Lakshminatha, Malathi, K.

“… scalability and accuracy. We propose a hybrid ensembled deep learning (HEDL) model that integrates convolutional neural networks (CNN…”
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IMDAC: A robust intelligent software defect prediction model via multi‐objective optimization and end‐to‐end hybrid deep learning networks by Zhu, Kun, Zhang, Nana, Jiang, Changjun, Zhu, Dandan

“… In this article, we propose a robust intelligent SDP model called IMDAC based on deep learning and soft computing techniques…”
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Spindle Autoencoder-CNN hybrid model for cardiac arrhythmia classification by Akkuş, Merve, Karabatak, Murat, Tekin, Ramazan

“…) with a Convolutional Neural Network (CNN). Unlike traditional autoencoders, the Spindle Autoencoder utilizes deeper and symmetric hidden layers to extract complex and meaningful representations from ECG signals…”
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