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检索条件"主题词=Denoising autoencoder"

共 343 条记录，以下是71-80 订阅

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ScDA: A denoising autoencoder Based Dimensionality Reduction for Single-cell RNA-seq Data 17th

ScDA: A Denoising AutoEncoder Based Dimensionality Reduction...

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17th International Symposium on Bioinformatics Research and Applications (ISBRA)

作者： Zhu, Xiaoshu Lin, Yongchang Li, Jian Wang, Jianxin Peng, Xiaoqing Yulin Normal Univ Sch Comp Sci & Engn Yulin Guangxi Peoples R China Cent South Univ Hunan Prov Key Lab Bioinformat Sch Comp Sci & Engn Changsha 400083 Hunan Peoples R China Cent South Univ Ctr Med Genet Sch Life Sci Changsha 400083 Hunan Peoples R China

ISBN: (纸本)9783030914141;9783030914158

Single-cell RNA-seq (scRNA-seq) data has provided a higher resolution of cellular heterogeneity. However, scRNA-seq data also brings some computational challenges for its high-dimension, high-noise, and high-sparseness. The dimension reduction is a crucial way to denoise and greatly reduce the computational complexity by representing the original data in a low-dimensional space. In this study, to achieve an accurate low-dimension representation, we proposed a denoising autoencoder based dimensionality reduction method for scRNA-seq data (ScDA), combining the denoising function with the autoencoder. ScDA is a deep unsupervised generative model, which models the dropout events and denoises the scRNA-seq data. Meanwhile, ScDA can reveal the nonlinear feature extraction of the original data through maximum distribution similarity before and after dimensionality reduction. Tested on 16 scRNA-seq datasets, ScDA provides superior average performances, and especially superior performances in large-scale datasets compared with 3 clustering methods.

关键词： Dimensional reduction denoising autoencoder Single-cell RNA-seq data Kullback-Leibler divergence Hierarchical clustering

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MOOD DETECTION FROM DAILY CONVERSATIONAL SPEECH USING denoising autoencoder AND LSTM

MOOD DETECTION FROM DAILY CONVERSATIONAL SPEECH USING DENOIS...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Kun-Yi Wu, Chung-Hsien Su, Ming-Hsiang Fu, Hsiang-Chi Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781509041176

In current studies, an extended subjective self-report method is generally used for measuring emotions. Even though it is commonly accepted that speech emotion perceived by the listener is close to the intended emotion conveyed by the speaker, research has indicated that there still remains a mismatch between them. In addition, the individuals with different personalities generally have different emotion expressions. Based on the investigation, in this study, a support vector machine (SVM)-based emotion model is first developed to detect perceived emotion from daily conversational speech. Then, a denoising autoencoder (DAE) is used to construct an emotion conversion model to characterize the relationship between the perceived emotion and the expressed emotion of the subject for a specific personality. Finally, a long short-term memory (LSTM)-based mood model is constructed to model the temporal fluctuation of speech emotions for mood detection. Experimental results show that the proposed method achieved a detection accuracy of 64.5%, improving by 5.0% compared to the HMM-based method.

关键词： Long-term emotion tracking mood detection denoising autoencoder long short-term memory

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A denoising autoencoder Approach for Credit Risk Analysis 2018

A Denoising Autoencoder Approach for Credit Risk Analysis

引用

International Conference on Computing and Artificial Intelligence (ICCAI)

作者： Fan, Qi Yang, Jiasheng Nankai Univ Dept Financial Dept 94 Weijin Rd Tianjin 300072 Peoples R China Univ Southampton Dept Human Soc & Math Southampton SO17 1BJ Hants England

ISBN: (纸本)9781450364195

Credit risk evaluation is a key consideration in financial activities. Financial institutions such as banks rely on credit risk analysis for determining the potential risk involved in financial activities and then decide the degree of involvement in such activities as well as the appropriate interest rate and the amount of capital that should be reserved. The recent development of machine learning has provided powerful tools for computer-aided credit risk analysis, and neural networks are one of the most promising approaches. However, conventional artificial neural networks involve multiple layers of neurons which then become a universal function that can approximate any function. Therefore, it will learn from not only the information in the training data set but also from the noise in it. It is critical to remove the noise in order to improve the accuracy and efficiency of such algorithms. In this paper, a denoising autoencoder approach is proposed for the training process for neural networks. The denoising-autoencoder-based neural network model is then applied to credit risk analysis, and the performance is evaluated.

关键词： Neural Network denoising autoencoder Machine Learning Credit Risk Risk Management

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RADIATION DOSE REDUCTION IN COMPUTED TOMOGRAPHY PERFUSION OF ACUTE ISCHEMIC STROKE PATIENTS USING A denoising autoencoder 21

RADIATION DOSE REDUCTION IN COMPUTED TOMOGRAPHY PERFUSION OF...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Charatpangoon, Pattarawut Delannoy, Pauline McDougall, Connor Vega, Fernando Mohite, Aashka Barber, Philip Menon, Bijoy K. Ganesh, Aravind MacDonald, M. Ethan Univ Calgary Dept Biomed Engn Calgary AB Canada Univ Calgary Dept Elect & Software Engn Calgary AB Canada Univ Calgary Dept Radiol Calgary AB Canada Univ Calgary Dept Clin Neurosci Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Paris Est Creteil Dept Engn Specialized Biomed & Hlth Ecole Publ Sante & Numer Creteil France

ISBN: (纸本)9798350313345;9798350313338

Computed Tomography Perfusion (CTP) is a widely used imaging technique for evaluating acute stroke. Compared to other imaging modalities, such as non-contrast computed tomography and X-ray, CTP involves a higher radiation exposure because of repeated volume imaging. Thus, reducing the radiation dose is an important task. However, reducing the radiation dose increases noise, which hinders the image quality. This work proposes a machine learning method using a denoising autoencoder (DAE) to remove the noise resulting from dose reduction. The study was conducted using CTP images from the PRove-IT dataset with 46 acute ischemic stroke patients. Low-dose images were simulated by introducing two types of noise, Gaussian noise and Poisson noise, for different reduction levels. These simulated noise images, along with the original images, were used to train the DAE. Additionally, the original perfusion parameter maps were compared with one from different levels of dose reduction. The findings indicate that by reducing the dose to 80%, the original and denoised images were almost identical, with an average Structural Similarity Index (SSIM) of 0.959 and 0.925 for the Gaussian and Poisson noise, respectively. Although the CTP maps had a slightly lower SSIM, they can still determine the core infarction and penumbra, with average SSIM values of four maps of 0.942 and 0.910 for the Gaussian and Poisson groups, respectively. This study demonstrates the potential to decrease the radiation dose of CTP scans by up to 80% with limited compromise on the quality of the resulting images.

关键词： computed tomography perfusion dose reduction denoising autoencoder acute ischemic stroke

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Single-channel dereverberation for distant-talking speech recognition by combining denoising autoencoder and temporal structure normalization 9

Single-channel dereverberation for distant-talking speech re...

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9th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Ueda, Yuma Wang, Longbiao Kai, Atsuhiko Xiao, Xiong Chng, Eng Siong Li, Haizhou Shizuoka Univ Grad Sch Engn Hamamatsu Shizuoka 4328561 Japan Nagaoka Univ Technol Nagaoka Niigata 9402188 Japan Nanyang Technol Univ Sch Comp Engn Singapore Singapore Inst Infocomm Res Human Language Technol Singapore Singapore

ISBN: (纸本)9781479942190

In this paper, we propose a robust distant-talking speech recognition by combining cepstral domain denoising autoencoder (DAE) and temporal structure normalization (TSN) filter. For the proposed method, after applying a DAE in the cepstral domain of speech to suppress reverberation, we apply a postprocessing technology based on temporal structure normalization (TSN) filter to reduce the noise and reverberation effects by normalizing the modulation spectra to reference spectra of clean speech. The proposed method was evaluated using speech in simulated and real reverberant environments. By combining a cepstral-domain DAE and TSN, the average Word Error Rate (WER) was reduced from 25.2% of the baseline system to 21.2% in simulated environments and from 47.5% to 41.3% in real environments, respectively.

关键词： speech recognition dereverberation denoising autoencoder environment adaptation distant-talking speech

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Ameliorated Deep Learning based on Improved denoising autoencoder and GACNN 37

Ameliorated Deep Learning based on Improved Denoising Autoen...

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37th Chinese Control Conference (CCC)

作者： Wang, Da Zhou, Xuefeng Xu, Zhihao Cheng, Taobo Wang, Xiaoxv Miao, Haoqing Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China Guangdong Inst Intelligent Mfg Guangdong Key Lab Modern Control Technol Guangzhou 510070 Guangdong Peoples R China

ISBN: (纸本)9789881563958

Improving the performance of deep learning and making it more in line with real life have always been the research direction of artificial intelligence. In this paper, a denoising autoencoder genetic algorithm convolution neural network (DGCNN) model based on deep learning is proposed. Two parts of the research work are combined to improve its performance. Firstly, the traditional autoencoder is replaced by the denoising autoencoder and improved the way of adding noise. Secondly, genetic algorithm is utilized to combine CNN at the stage of image classification. This allows DGCNN to cope with complex and volatile situations while enhancing image processing capabilities. Simulation results show that the method can enhance the ability of image processing than traditional methods. The performance of the proposed model is better than traditional method when the images of different loss levels are processed by this method. The results are verifying the feasibility and effectiveness of the model and algorithm. DGCNN shows better capacity in improving the performance of image processing and dealing with complex situations effectively.

关键词： deep learning denoising autoencoder genetic algorithm convolution neural network

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NOISE REDUCTION IN LOW-DOSE CT USING A 3D MULTISCALE SPARSE denoising autoencoder

NOISE REDUCTION IN LOW-DOSE CT USING A 3D MULTISCALE SPARSE ...

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27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Mentl, Katrin Mailhe, Boris Ghesu, Florin C. Schebesch, Frank Haderlein, Tino Maier, Andreas Nadar, Mariappan S. Friedrich Alexander Univ Pattern Recognit Lab Erlangen Germany Siemens Healthineers Med Imaging Technol Princeton NJ 08540 USA

ISBN: (纸本)9781509063413

This article presents a novel neural network-based approach for enhancement of 3D medical image data. The proposed networks learn a sparse representation basis by mapping the corrupted input data to corresponding optimal targets. To reinforce the adjustment of the network to the given data, the threshold values are also adaptively learned. In order to capture important image features on various scales and be able to process large computed tomography (CT) volumes in a reasonable time, a multiscale approach is applied. Recursively downsampled versions of the input are used and denoising operator of constant size are learnt at each scale. The networks are trained end-to-end from a database of real high-dose acquisitions with synthetic additional noise to simulate the corresponding low-dose scans. Both 2D and 3D networks are evaluated on CT volumes and compared to the block-matching and 3D filtering (BM3D) algorithm. The presented methods achieve an increase of 4% to 11% in the SSIM and of 2.4 to 2.8 dB in the PSNR with respect to the ground truth, outperform BM3D in quantitative comparisions and present no visible texture artifacts. By exploiting volumetric information, 3D networks achieve superior results over 2D networks.

关键词： CT 3D neural networks denoising autoencoder

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Learning via denoising autoencoder on 5G NR Phase Noise Estimation

Learning via Denoising Autoencoder on 5G NR Phase Noise Esti...

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IEEE Global Communications Conference (GLOBECOM)

作者： Lin, Mu-sheng Kwon, Hyukjoon Samsung Semicond Inc San Diego CA 92121 USA

ISBN: (纸本)9781728181042

In this paper, on phase noise of 5G NR mmWave systems, we propose a learning-based common phase error (CPE) estimation algorithm based on the denoising autoencoder serving as a nonlinear filter on the existing CPE estimators. The proposed algorithm learns the low dimension manifold of phase noise distributions with high probability. Traditional CPE methods have limitation when the time domain pilots are few, which causes degraded system performance with CPE interpolation. Besides accurate CPE estimation, the proposed method is more robust to various FR2 channels, Doppler effects, and the numerologies. Simulation results show that block error rate (BLER) could be improved up to 1.43 dB on SNR.

关键词： Common Phase Error learning denoising autoencoder neural network 5G NR FR2

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Intrusion Detection in Power Cyber-Physical Systems Using denoising autoencoder and EQL v2 Loss Function 20th

Intrusion Detection in Power Cyber-Physical Systems Using De...

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20th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Yiying Dong, Yanping Liu, Ke Zhang, Xiankun Sun, Xianfan Li, Siwei Tianjin Univ Sci & Technol Tianjin 300457 Peoples R China State Grid Tianjin Informat & Telecommun Co Tianjin 300000 Peoples R China Beijing Fibrlink Commun Co Beijing 100070 Peoples R China

ISBN: (纸本)9789819756056;9789819756063

The network connection of the power cyber-physical system (PCPS) makes it easy to become a potential attack target, leading to serious consequences such as paralysis of the power system. The existence of class imbalance and noise in the PCPS network traffic dataset limits the intrusion detection accuracy. In response to the above difficulties, this article combines the denoising autoencoder and the CNN-Attention network to define the AE-CNA NIDS architecture. The BatchSwapNoise method DAE is used to denoise, compress and reconstruct features of the data, and extract local spatiotemporal features in theCNAblock formed by CNN and Attention. Multiple CNA blocks are stacked together to comprehensively learn the multi-layer spatiotemporal characteristics of network attack data. In addition, for the network intrusion dataset imbalance problem, equalization loss v2 (EQL v2) is used to balance the weight attention of the minority class. Experimental results show that AE-CNA performs well in terms of accuracy, precision, and recall value, with an accuracy rate of 98.3%, effectively improving intrusion detection performance and minority class detection rate.

关键词： Power cyber-physical systems Intrusion detection denoising autoencoder Attention mechanism Abnormal traffic

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Revolutionizing Cancer Diagnosis Through Hybrid Self-supervised Deep Learning: EfficientNet with denoising autoencoder for Semantic Segmentation of Histopathological Images 27th

Revolutionizing Cancer Diagnosis Through Hybrid Self-supervi...

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27th Annual Conference on Medical Image Understanding and Analysis (MIUA)

作者： Hammouda, Mostafa A. Khaled, Marwan Ali, Hesham Selim, Sahar Elattar, Mustafa Nile Univ Sch Informat Technol & Comp Sci Giza 12677 Egypt Nile Univ Med Imaging & Image Proc Res Grp Ctr Informat Sci Giza 12677 Egypt

ISBN: (纸本)9783031485923;9783031485930

Machine Learning technologies are being developed day after day, especially in the medical field. New approaches, algorithms and architectures are implemented to increase the efficiency and accuracy of diagnosis and segmentation. Deep learning approaches have proven their efficiency;these approaches include architectures like EfficientNet and denoising autoencoder. Accurate segmentation of nuclei in histopathological images is essential for the diagnosis and prognosis of diseases like cancer. In this paper, we propose a novel method for semantic segmentation of nuclei using EfficientNet and denoising Auto-encoder on the PanNuke dataset. The denoising auto-encoder pre-processing step is used to enhance the feature representations of input images, and EfficientNet is the model that has been used as the semantic segmentation model. Our proposed method achieved state-of-the-art results, outperforming most of the previously proposed methods by a significant margin, as our proposed method achieved a higher Dice score of 83.33 compared to the previous related work methods, which achieved scores varying from 69.3 to 80.28. The efficiency of our proposed approach will be demonstrated in this paper by discussing, exploring, and comparing it with previously proposed methods and their results.

关键词： semantic segmentation histopathological images self-supervised deep learning EfficientNet denoising autoencoder

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