检索结果-内蒙古大学图书馆

International Symposium on Electrical and Electronics Engineering (ISEE)

作者： Dat Ngo Lam Pham Anh Nguyen Ben Phan Khoa Tran Truong Nguyen VNU HCM HCMUT Dept Elect & Elect Engn Ho Chi Minh City Vietnam UT DUT Dept Elect & Elect Engn Da Nang City Vietnam

ISBN: (纸本)9781665414876

This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C-DNN and autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a result, we achieve competitive performances with ICBHI average score of 0.49, ICBHI harmonic score of 0.42.

关键词： Respiratory disease wheeze crackle ensemble C-DNN autoencoder network

来源：评论

学校读者我要写书评

暂无评论

Codebook Design for Noncoherent Communications Using autoencoders

Codebook Design for Noncoherent Communications Using Autoenc...

引用

IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Sergienko, Alexander B. Apalina, Polina, V St Petersburg Electrotech Univ LETI Dept Theoret Fundamentals Radio Engn St Petersburg Russia

ISBN: (纸本)9781665404761

The problem of joint optimization of coding and modulation for noncoherent communications is considered. To minimize block error rate, an autoencoder network is used. The network architecture is designed to mimic the end-to-end operation of a communication system with noncoherent correlation receiver. Signal codebooks with various spectral efficiencies are obtained for several small block lengths. Achieved pairwise correlations between signals of codebooks are compared with some known results. Also, minimum distances and power efficiencies of the obtained codebooks are analyzed. The obtained results show that autoencoder networks can be successfully used to design signal codebooks tailored to specific operating conditions.

关键词： noncoherent reception autoencoder network codebook optimization joint coding and modulation

来源：评论

学校读者我要写书评

暂无评论

Efficient Fundus Image Gradeability Approach Based on Deep Reconstruction-Classification network 23

Efficient Fundus Image Gradeability Approach Based on Deep R...

引用

23rd International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Khalid, Saif Abdulwahab, Saddam Rashwan, Hatem A. Cristiano, Julian Abdel-Nasser, Mohamed Puig, Domenec Univ Rovira & Virgili DEIM Tarragona Spain Univ Al Qadisiyah Al Diwaniyah Iraq Aswan Univ Dept Elect Engn Aswan 81528 Egypt

ISBN: (纸本)9781643682112;9781643682105

Quality of retinal image is vital for screening of ailments pertaining to eye such as glaucoma, diabetic retinopathy (DR) and age related macular degeneration. Therefore, assessing quality of retinal image prior to any kind of diagnosis has assumed significance in Computer Aided Desgin (CAD) applications. The rationale behind this is that reliability of retinal image is to be guaranteed to have dependable diagnosis. In this paper, we propose a novel retinal fundus image quality assessment (RIQA) method based on autoencoder network to assess retinal images if the image is acceptable for screening or not. The autoencoder network architecture is well suited to precisely to properly represent the key features of the image quality, especially when the network can correctly reconstruct the input image. The proposed model consists of encoder and decoder successive networks. The encoder will be used for representing the features of the input image. In turn, the decoder will be used for reconstruct the input image. The features get from encoder network will then be fed to a classifier in order to classify the quality of retinal image to two classes: gradable or ungradable. The experimental results revealed more useful assessment and the proposed deep model provides a superior performance for RIQA. Thus, our model can serve real-world Clinical Decision Support Systems in the healthcare domain.

关键词： Retinal Image Quality Assessment autoencoder network Ocular Diseases Deep Learning

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Unmixing of Hyperspectral Data via Deep autoencoder networks

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第9期16卷 1467-1471页

作者： Wang, Mou Zhao, Min Chen, Jie Rahardja, Susanto Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Minist Ind & Informat Technol Key Lab Ocean Acoust & Sensing Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Dev Inst Shenzhen 518057 Peoples R China

Nonlinear spectral unmixing is an important and challenging problem in hyperspectral image processing. Classical nonlinear algorithms are usually derived based on specific assumptions on the nonlinearity. In recent years, deep learning shows its advantage in addressing general nonlinear problems. However, existing ways of using deep neural networks for unmixing are limited and restrictive. In this letter, we develop a novel blind hyperspectral unmixing scheme based on a deep autoencoder network. Both encoder and decoder of the network are carefully designed so that we can conveniently extract estimated endmembers and abundances simultaneously from the nonlinearly mixed data. Because an autoencoder is essentially an unsupervised algorithm, this scheme only relies on the current data and, therefore, does not require additional training. Experimental results validate the proposed scheme and show its superior performance over several existing algorithms.

关键词： autoencoder network deep learning hyperspectral imaging nonlinear spectral unmixing

来源：评论

学校读者我要写书评

暂无评论

ToxMVA: An end-to-end multi-view deep autoencoder method for protein toxicity prediction

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2022年第PartB期151卷 106322-106322页

作者： Shi, Hua Li, Yan Chen, Yi Qin, Yuming Tang, Yifan Zhou, Xun Zhang, Ying Wu, Yun Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen 361024 Fujian Peoples R China Southwest Med Univ Affiliated Hosp Anesthesiol Dept Luzhou 646000 Sichuan Peoples R China Beidahuang Ind Grp Gen Hosp Harbin Peoples R China Xiamen Univ Technol Coll Comp & Informat Engn Xiamen 361024 Fujian Peoples R China

Effectively predicting protein toxicity plays an essential step in the early stage of protein-based drug discovery, which is of great help to speed up novel drug screening and reduce costs. Recently, several relevant datasets have been designed, and then machine learning-based methods have been proposed to predict the toxicity of the protein and have shown satisfactory performance. However, previous studies generally directly concatenate different protein features, which may introduce irrelevant information and decrease model performance. In this study, we present a novel end-to-end deep learning-based method called ToxMVA, to predict protein toxicity. To be specific, we first build comprehensive feature profiles of proteins based on primary sequences, including sequential, physicochemical, and contextual semantic information. Next, an autoencoder network is introduced to integrate the multi-view information for obtaining a more concise and accurate feature representation. Extensive experimental results on three datasets demonstrate that ToxMVA has superior performance for protein toxicity prediction and shows better robustness among three different datasets.

关键词： Protein toxicity prediction autoencoder network Multi -view information

来源：评论

学校读者我要写书评

暂无评论

Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing autoencoders for One-Class Change Detection in Multispectral Imagery

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Mou, Lichao Hua, Yuansheng Saha, Sudipan Bovolo, Francesca Bruzzone, Lorenzo Zhu, Xiao Xiang Tech Univ Munich TUM Data Sci Earth Observat D-80333 Munich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Wessling Germany Tech Univ Munich TUM Chair Data Sci Earth Observat D-80333 Munich Germany Fdn Bruno Kessler I-38100 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38050 Trento Italy

Change detection is a long-standing and challenging problem in remote sensing. Very often, features about changes are difficult to model beforehand, thus making the collection of changed samples a challenging task. In comparison, it is much easier to collect numerous no-change samples. It is possible to define a change detection approach using only easily available annotated no-change samples, which we henceforth call one-class change detection. autoencoder networks being trained on no-change data are natural candidates for addressing this task due to their superior performance when compared with other one-class classification models. However, the autoencoders usually suffer from the problem of overgeneralization, i.e., they tend to generalize too well, thus risking properly reconstructing changed samples. In this article, we propose a novel data-enclosing-ball minimizing autoencoder (DebM-AE) that is trained with dual objectives-a reconstruction error criterion and a minimum volume criterion. The network learns a compact latent space, where encodings of no-change samples have low intraclass variance, which as counterpart has the identification of changed instances. We conducted extensive experiments on three real-world datasets. Results demonstrate advantages of the proposed method over other competitors. We make our data and code publicly available (https://***/ai4eo/reasoning/DebM-AE;https://***/lcmou/DebM-AE).

关键词： autoencoder network change detection oneclass classification remote sensing

来源：评论

学校读者我要写书评

暂无评论

Sequential three-way decision based on multi-granular autoencoder features

引用

INFORMATION SCIENCES 2020年 507卷 630-643页

作者： Zhang, Libo Li, Huaxiong Zhou, Xianzhong Huang, Bing Nanjing Univ Sch Management & Engn Dept Control & Syst Engn Nanjing 210093 Jiangsu Peoples R China Nanjing Audit Univ Sch Technol Nanjing 211815 Jiangsu Peoples R China

autoencoder network is an efficient representation learning method. In general, a finer feature set obtained from autoencoder leads to a lower error rate and lower total misclassification cost. However, the network is usually trained for a long time to obtain a finer feature set, leading to a high time cost and total cost. To address this issue, a Sequential Three-Way Decision (S3WD) model is developed to balance the misclassification cost and the time cost in autoencoder based classifications and decisions. To implement the tradeoff strategy, it is necessary to extract a multi-granular feature set. In the network, the associated discriminative information in the extracted features increases with training epochs, which constructs a multi-granular feature structure. An autoencoder-based multi-granular feature description definition is presented. Based on the definition, an autoencoder composed of restricted Boltzmann machines is adopted to extract the multi-granular features. Then, a new cost-sensitive S3WD model is proposed, which aims to find the optimal granule level with the lowest total cost. Finally, the experiments demonstrate the effectiveness of the proposed approaches. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Three-way decision Granular computing autoencoder network Restricted Boltzmann machine Cost-sensitive learning

来源：评论

学校读者我要写书评

暂无评论

Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer's Disease

引用

IEEE ACCESS 2020年 8卷 115383-115392页

作者： Guo, Haibing Zhang, Yongjin Jiangsu Ocean Univ Sch Sci Lianyungang 222005 Peoples R China First Peoples Hosp Lianyungang Neurol Dept Lianyungang 222005 Peoples R China

The development of computerized healthcare has been powered by diagnostic imaging and machine learning techniques. In particular, recent advances in deep learning have opened a new era in support of multimedia healthcare distribution. For earlier detection of Alzheimer's disease, the study suggested the Improved Deep Learning Algorithm (IDLA) and statistically significant text information. The specific information in clinical text includes the age, sex and genes of the person and apolipoprotein E;the brain function is established using resting-state functional data (MRI) for the measurement of connectivity in the brain regions. A specialized network of autoencoders is used in earlier diagnosis to distinguish between natural aging and disorder progression. The suggested approach incorporates effectively biased neural network functionality and allows a reliable Alzheimer's disease recognition. In comparison with conventional classifiers depends on time series R-fMRI results, the proposed deep learning algorithm has improved significantly and, in the best cases, the standard deviation reduced by 45%, indicating the forecast model is more reliable and efficient in relation to conventional methodologies. The work examines the benefits of improved deep learning algorithms from recognizing high-dimensional information in healthcare and can lead to the early diagnosis and prevention of Alzheimer's disease.

关键词： Functional magnetic resonance imaging Alzheimer's disease Deep learning Data models Brain modeling Alzheimer's disease autoencoder network improved deep learning algorithm (IDLA) R-fMRI data

来源：评论

学校读者我要写书评

暂无评论

Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第7期30卷 1789-1802页

作者： Han, Chaoyi Duan, Yiping Tao, Xiaoming Xu, Mai Lu, Jianhua Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China

Compressing large images with a generative model goes beyond typical image encoding standards under a notably low bitrate. In this paper, we step toward practical generative compression systems based on recent advances. Specifically, we show that the channel redundancy of the latent representation produced by an autoencoder network can be effectively compressed via mask compression. The mask compression performs quantization on the channel variance of latent representation instead of original values. Instead of training multiple models, changing the mask leads to a simple and efficient variable rate compression scheme. Then, we estimate the relative bitrate by measuring the L1 norm of the channel variance and hence obtain the rate-distortion formulation. The L1 regularizer assumes a Laplacian prior on the channel variance, through which model we develop corresponding methods to produce approximate images at a target bitrate. This eliminates the need for manually searching hyperparameters for our variable-rate compression. We conduct exhaustive experiments to demonstrate the advanced performance of the proposed method in preserving image quality and semantics.

关键词： Image coding Bit rate Quantization (signal) Training Image reconstruction Decoding Correlation Variable-Rate image compression generative model convolutional neural network autoencoder network channel redundancy

来源：评论

学校读者我要写书评

暂无评论

NeuralPot: An Industrial Honeypot Implementation Based On Deep Neural networks

NeuralPot: An Industrial Honeypot Implementation Based On De...

引用

25th IEEE Symposium on Computers and Communications (ISCC)

作者： Siniosoglou, Ilias Efstathopoulos, Georgios Pliatsios, Dimitrios Moscholios, Ioannis D. Sarigiannidis, Antonios Sakellari, Georgia Loukas, Georgios Sarigiannidis, Panagiotis Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece 0 INFIN Ltd Imperial Off London England Univ Peloponnese Tripoli Dept Informat & Telecommun Tripoli Greece Sidroco Holdings Ltd Limassol Cyprus Univ Greenwich Comp & Informat Syst London England

ISBN: (纸本)9781728180861

Honeypots are powerful security tools, developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network attracting malicious activity and securing the rest of the network entities. Since an increase in intrusions has been observed lately, more advanced security systems are necessary. In this paper a new method of adapting a honeypot system in a modern industrial network, employing the Modbus protocol, is introduced. In the presented NeuralPot honeypot, two distinct deep neural network implementations are utilized to adapt to network Modbus entities and clone them, actively confusing the intruders. The proposed deep neural networks and their generated data are then compared.

关键词： Industrial Control System SCADA Honeypots GAN network autoencoder network Data Generation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：