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DSSAE: Deep Stacked Sparse autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy

ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022年第1期13卷 2-2页

作者： Wang, Shui-Hua Zhang, Xin Zhang, Yu-Dong Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China Univ Leicester Sch Math & Actuarial Sci Leicester LE1 7RH Leics England Fourth Peoples Hosp Huaian Dept Med Imaging Huaian 223002 Jiangsu Peoples R China Univ Leicester Sch Informat Leicester LE1 7RH Leics England

(Aim) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. (Methods) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom deep stacked sparse autoencoder (DSSAE) model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. (Results) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). (Conclusion) Our method outperforms 10 state-of-the-art approaches.

关键词： Deep learning COVID-19 autoencoder fractional Fourier entropy

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Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 125卷 104016-104016页

作者： Takahashi, Hirokazu Emami, Ali Shinozaki, Takashi Kunii, Naoto Matsuo, Takeshi Kawai, Kensuke Univ Tokyo Grad Sch Informat Sci & Technol Dept Mechanoinformat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Univ Tokyo Res Ctr Adv Sci & Technol Meguro Ku 4-6-1 Komaba Tokyo 1538904 Japan Natl Inst Informat & Commun Technol CiNet Meguro Ku 1-4 Yamadaoka Suita Osaka 5650871 Japan Univ Tokyo Grad Sch Med Dept Neurosurg Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Tokyo Metropolitan Neurol Hosp 2-6-1 Musadhidai Fuchu Tokyo 1830042 Japan Jichi Med Univ Dept Neurosurg 3311-1 Yakushiji Shimotsuke Tochigi 3290498 Japan

Objective: In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still has room for reducing the false-positive alarm rate. Methods: We combined a CNN that processed images of EEC plots with patient-specific autocncoders (AE) of EEC signals to reduce the false alarms during seizure detection. The AE automatically logged abnormalities, i.e., both seizures and artifacts. Based on seizure logs compiled by expert epileptologists and errors made by AE, we constructed a CNN with 3 output classes: seizure, non-seizure-but-abnormal, and non-seizure. The accumulative measure of number of consecutive seizure labels was used to issue a seizure alarm. Results: The second-by-second classification performance of AE-CNN was comparable to that of the original CNN. False-positive seizure labels in AF-CNN were more likely interleaved with "non-seizure-but-abnormal" labels than with true-positive seizure labels. Consequently, "non-seizure-but-abnormal" labels interrupted runs of false-positive seizure labels before triggering an alarm. The median false alarm rate with the AE-CNN was reduced to 0.034 h(-1), which was one-fifth of that of the original CNN (0.17 h(-1)). Conclusions: A label of "non-seizure-but-abnormal" offers practical benefits for seizure detection. The modification of a CNN with an AE is worth considering because Alis can automatically assign "non-seizure-but-abnormal" labels in an unsupervised manner with no additional demands on the time of the epileptologist.

关键词： autoencoder Convolutional neural network Electroencephalography Epilepsy Seizure Video-EEG monitoring

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A Transceiver Design Based on an autoencoder Network for Multi-Color VLC Systems

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2020年第3期12卷 1-16页

作者： Zhang, Dong-Fang Yu, Hong-Yi Zhu, Yi-Jun Zhu, Zhao-Rui Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450000 Peoples R China

We propose a transceiver design method based on an autoencoder (AE) network for multi-color visible light communication (VLC) systems. Taking into account the chromaticity constraint described by MacAdam ellipses and the peak-value constraint of transmitted signals, the proposed AE network utilizes a peak-value constraint layer and an integrated loss function which different from previous AE designs. The new structure of AE network can be suitable for VLC systems with different numbers of colors. Additionally, noisy channel state information (CSI) is employed during the training of the AE in order to achieve a better performance for the system with imperfect CSI. After training, a transceiver design with the target of minimizing block error rate (BLER) can be obtained, which simultaneously meets the requirements of lighting. The results of numerical simulation experiments demonstrate that our proposed transceiver design outperforms conventional color shift keying (CSK) constellation design in imperfect CSI channel.

关键词： Transceiver design autoencoder visible light communication channel state information color shift keying

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Comparative Study on Three autoencoder-Based Deep Learning Algorithms for Geochemical Anomaly Identification

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EARTH AND SPACE SCIENCE 2022年第11期9卷

作者： Feng, Bin Chen, Lirong Xu, Yongyang Zhang, Yu China Univ Geosci Sch Geog & Informat Engn Wuhan Peoples R China China Acad Geol Sci Inst Geophys & Geochem Explorat Langfang Peoples R China China Geol Survey Dev & Res Ctr Beijing Peoples R China China Univ Geosci Beijing Peoples R China

Deep autoencoder (AE) networks show a powerful ability for geochemical anomaly identification. Because of little contribution to the AE network, small probability samples (again, please check this) having comparatively high reconstructed errors can be recognized by the trained model as anomalous samples. However, different autoencoder networks have different abilities for anomaly identification. To test these methods for geochemical anomaly identification, we based our study on stream sediment data of the Cu-Zn-Ag metallogenic area in southwest Fujian province as samples. Three unsupervised deep learning models: the autoencoder (AE), multi-convolutional autoencoder (MCAE), and fusion convolutional autoencoder (FCAE), were used to extract the combined structural, spatial distribution, and mixed features of multiple-elements. The results showed that the anomalous area delineated by the FCAE model had the best consistency with the known copper mineral occurrences, followed by the MCAE and AE models, with area under the curve values (AUC) of 0.80, 0.78, and 0.61, respectively. FCAE and AE were insensitive to changes in convolution window size, while MCAE extracted more spatial distribution or mixed features. Overall, FCAE focused more on structural distribution or mixed features, combining the advantages of both MCAE and AE. Therefore, FCAE performed best among the three deep learning methods. This study provides a practical basis for selecting and constructing geochemical anomaly recognition models based on deep learning algorithms.

关键词： geochemical anomaly identification autoencoder feature combination unsupervised learning spatial feature extraction

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Hyperspectral Unmixing Using Orthogonal Sparse Prior-Based autoencoder With Hyper-Laplacian Loss and Data-Driven Outlier Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第9期58卷 6550-6564页

作者： Dou, Zeyang Gao, Kun Zhang, Xiaodian Wang, Hong Wang, Junwei Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China

Hyperspectral unmixing, which estimates end-members and their corresponding abundance fractions simultaneously, is an important task for hyperspectral applications. In this article, we propose a new autoencoder-based hyperspectral unmixing model with three novel components. First, we propose a new sparse prior to abundance maps. The proposed prior, called orthogonal sparse prior (OSP), is based on the observations that different abundance maps are close to orthogonal because, generally, no more than two end-members are mixed within one pixel. As opposed to the conventional norm-based sparse prior that assumes the abundance maps are independent, the proposed OSP explores the orthogonality between the abundance maps. Second, we propose the hyper-Laplacian loss to model the reconstruction error. The key observation is that the reconstruction error distribution usually has a heavy-tailed shape, which is better modeled by the hyper-Laplacian distribution rather than the commonly used Gaussian distribution. Third, to ease the side effect of outliers for end-member initializations, we develop a data-driven approach to detect outliers from the raw hyperspectral images. Extensive experiments on both synthetic and real-world data sets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods, with up to more than 50% improvements.

关键词： Hyperspectral imaging Decoding Image reconstruction Gaussian distribution Spatial resolution Anomaly detection autoencoder hyper-Laplacian distribution outlier detection sparse prior spectral unmixing

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autoencoder based Friendly Jamming

Autoencoder based Friendly Jamming

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Bui Minh Tuan Ta Duc Tuyen Nguyen Linh Trung Nguyen Viet Ha Vietnam Natl Univ VNU Univ Engn & Technol AVITECH Hanoi Vietnam Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Vietnam Acad Mil Sci & Technol Hanoi Vietnam

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131061

Physical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information (CSI) of their channel. Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to-end learning in AE-based FJ.

关键词： Physical layer security autoencoder friendly jamming wiretap channel mutual information neural estimation

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Against Insider Threats with Hybrid Anomaly Detection with Local-Feature autoencoder and Global Statistics (LAGS)

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2020年第4期E103D卷 888-891页

作者： Jang, Minhae Ryu, Yeonseung Kim, Jik-Soo Cho, Minkyoung KEPCO Res Inst Digital Solut Lab Daejeon South Korea Myongji Univ Dept Comp Engn Seoul South Korea

Internal user threats such as information leakage or system destruction can cause significant damage to the organization, however it is very difficult to prevent or detect this attack in advance. In this paper, we propose an anomaly-based insider threat detection method with local features and global statistics over the assumption that a user shows different patterns from regular behaviors during harmful actions. We experimentally show that our detection mechanism can achieve superior performance compared to the state of the art approaches for CMU CERT dataset.

关键词： abnormal detection sequence-to-sequence learning autoencoder reconstruction error

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Identifying marsh dieback events from Landsat image series (1998–2018) with an autoencoder in the NIWB estuary, South Carolina

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International Journal of Digital Earth 2020年第12期13卷 1467-1483页

作者： Huixuan Li Cuizhen Wang Jean T.Ellis Yuxin Cui Gwen Miller James T.Morris Department of Geography University of South CarolinaColumbiaSCUSA Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA School of the Earth Oceanand EnvironmentUniversity of South CarolinaColumbiaSCUSA Belle Baruch Institute for Marine&Coastal Sciences University of South CarolinaColumbiaSCUSA

This study reports an inventory of marsh dieback events from spatial and temporal perspectives in the North Inlet-Winyah Bay(NIWB)estuary,South Carolina(SC).Past studies in the Gulf/Atlantic coast states have reported acute marsh dieback events in which marsh rapidly browned and thinned,leaving stubble of dead stems or mudflat with damaged ecosystem *** marsh dieback in SC,however,have been *** study identified all marsh dieback events in the estuary since *** 20 annually collected Landsat images,the Normalized Difference Vegetation Index(NDVI)series was extracted.A Stacked Denoising autoencoder neural network was developed to identify the NDVI anomalies on the *** marsh dieback patches were extracted,and their inter-annual changes were *** showed a continuous,spatially variable multi-year dieback event in 1998–2005,which aligned with the reported dieback in the early 2000s from other *** identified patches mostly returned to normal within one year while the phenomenon reoccurred in other areas of the estuary during the prolonged dieback *** study presents the first attempt to explore long-term dieback dynamics in an estuary using satellite time *** provides valuable information in documenting marsh healthiness and environmental resilience on SC coasts.

关键词： Salt marsh dieback NOAA NERR satellite time series autoencoder coastal remote sensing

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A novel statistical analysis and autoencoder driven intelligent intrusion detection approach

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NEUROCOMPUTING 2020年第0期387卷 51-62页

作者： Ieracitano, Cosimo Adeel, Ahsan Morabito, Francesco Carlo Hussain, Amir Univ Mediterranea Reggio Calabria DICEAM Via Graziella I-89060 Reggio Di Calabria Italy Univ Wolverhampton Sch Math & Comp Sci Wolverhampton WV1 1LY England Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Midlothian Scotland

In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dangerous malware attacks that make intrusion recognition a very difficult task. In this context, traditional analytical tools are facing severe challenges to detect and mitigate these threats. In this work, we introduce a novel statistical analysis and autoencoder (AE) driven intelligent intrusion detection system (IDS). Specifically, the proposed IDS combines data analytics and statistical techniques with recent advances in machine learning theory to extract more optimized, strongly correlated features. The proposed IDS is evaluated using the benchmark NSL-KDD database. Comparative experimental results show that the designed statistical analysis and AE based IDS achieves better classification performance compared to conventional deep and shallow machine learning and other recently proposed state-of-the-art techniques. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.

关键词： Anomaly detection Deep learning autoencoder Optimized features extraction NSL-KDD database

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Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2022年 177卷 121523-121523页

作者： Li, Tong Wang, Zhaohua Zhao, Wenhui Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Beijing Inst Technol Ctr Sustainable Dev & Intelligent Decis Beijing 100081 Peoples R China Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing 100081 Peoples R China Sustainable Dev Res Inst Econ & Soc Beijing Beijing 100081 Peoples R China

Studies of electricity consumption behavior patterns (ECBPs) are very important for demand-side management and emission reduction. Most of the existing ECBPs studies have limitations in data volume, algorithm perfor-mance and application potential in large-scale data scenario. Based on various autoencoders, this study proposes an ECBPs mining method with better performance and wider application potential. Two indices are constructed for model evaluations. The empirical research results based on the high-frequency power consumption data of residents show that the representation learning of ECBPs through autoencoders can not only effectively reduce the data dimension (reduced by 90%) for pattern mining but also achieve an effect no less than that of pattern mining from the original data, with the difference in pattern aggregation degree between them being only 0.003. Different autoencoders have characteristics in the representation mapping of the original data space. The results also show that the curves decoded by different autoencoders reflect different characteristics suitable for different scenarios. This study solves the dimension disaster problem of ECBPs mining in the context of large-scale data, and provides a better tool for more complex ECBPs based tasks such as multi-energy prosumers modeling and energy system optimization.

关键词： autoencoder Electricity consumption behavior pattern Demand side management Clustering Smart grid

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