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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Zhai, Jia Zhu, Sha Chen, Feng Xie, Xiaodan Zhu, Yong Yin, Hongcheng Sci & Technol Electromagnet Scattering Lab Beijing Peoples R China Beijing Inst Remote Sensing Informat Beijing Peoples R China Sci & Technol Opt Radiat Lab Beijing Peoples R China

ISBN: (纸本)9781728117089

Aiming at the problem of Synthetic Aperture Radar (SAR) target recognition, a new deep learning method is proposed. The stacked Auto Encoder (SAE) network and the convolutional Neural Network (CNN) have remarkable performance. Through the implementation and comparison of these methods, it is shown that the proposed deep learning recognition method has strong adaptability to different situations and has robustness to attitude angle, background and noise.

关键词： Synthetic Aperture Radar (SAR) target recognition principle component analysis stacked autoencoder convolutional neural network

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Combating Threat-Alert Fatigue with Online Anomaly Detection Using Isolation Forest 26th

Combating Threat-Alert Fatigue with Online Anomaly Detection...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Aminanto, Muhamad Erza Zhu, Lei Ban, Tao Isawa, Ryoichi Takahashi, Takeshi Inoue, Daisuke Natl Inst Informat & Commun Technol Tokyo Japan Lingnan Normal Univ Zhanjiang Peoples R China

ISBN: (纸本)9783030367084;9783030367077

The threat-alert fatigue problem, which is the inability of security operators to genuinely investigate each alert coming from network-based intrusion detection systems, causes many unexplored alerts and hence a deterioration of the quality of service. Motivated by this pressing need to reduce the number of threat-alerts presented to security operators for manual investigation, we propose a scheme that can triage alerts of significance from massive threat-alert logs. Thanks to the fully unsupervised nature of the adopted isolation forest method, the proposed scheme does not require any prior labeling information and thus is readily adaptable for most enterprise environments. Moreover, by taking advantage of the temporal information in the alerts, it can be used in an online mode that takes in the most recent information from past alerts and predicts the incoming ones. We evaluated the performance of our scheme using a 10-month dataset consisting of more than half a million alerts collected in a real-world enterprise environment and found that it could screen out 87.41% of the alerts without missing any single significant ones. This study demonstrates the efficacy of unsupervised learning in screening minor threat-alerts and is expected to shed light on the threat-alert fatigue problem.

关键词： Threat-alert fatigue Intrusion detection system stacked autoencoder Isolation forest

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HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON stacked MARGINAL DISCRIMINATIVE autoencoder 37

HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON STACKED MARGINAL...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Feng, Jie Liu, Liguo Zhang, Xiangrong Wang, Rongfang Liu, Hongying Xidian Univ Key Lab Intelligent Percept & Image Undersatnding Minist Educ Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781509049516

In this paper, a novel stacked marginal discriminative autoencoder (SMDAE) method is proposed for hyperspectral image classification. It uses a deep neural network to learn discriminative features from hyperspectral images automatically. In hyperspectral images, the collection of training samples is difficult. When the number of training samples is not enough, these training samples are difficult to estimate the statistical distribution of hyperspectral images accurately. In order to solve the small sample problem and improve the classification performance of the autoencoder, the marginal samples are selected through the distribution characteristics of samples. The marginal samples are searched based on k nearest neighbors between different classes. These samples are used to fine-tune the SMDAE network. The experimental results show that the proposed SMDAE method can achieve satisfying performance under small training set.

关键词： stacked autoencoder hyperspectral classification deep learning discriminative features marginal samples

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SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第17期77卷 22809-22820页

作者： Xiao, Zhiyong Wang, Canhua Jia, Nan Wu, Jianhua Nanchang Univ Sch Mechatron Engn Nanchang 330031 Jiangxi Peoples R China Jiangxi Agr Univ Sch Software Nanchang 330045 Jiangxi Peoples R China Jiangxi Univ Tradit Chinese Med Sch Comp Nanchang 330004 Jiangxi Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China

This paper employs a novel-deep learning method and brain frequencies to discriminate school-aged children with autism spectrum disorders (ASD) from typically developing (TD) school-aged children with functional magnetic resonance imaging (fMRI) data of 84 subjects from the ABIDE (Autism Brain Imaging Data Exchange) database. Firstly, the fMRI data were preprocessed, and then each subject's dataset was decomposed into 30 independent components (IC). Secondly, some key ICs were selected and inputted into a stacked autoencoder (SAE). The SAE was adopted for features subtraction and dimensionality reduction. Finally, a softmax classifier was used to discriminate the school-aged children with ASD from TD school-aged children. The average accuracy of the work was as high as 87.21% (average sensitivity = 92.86%, average specificity = 84.32%). The results of classification demonstrated that the proposed method may have the potential to automatically discriminate school-aged children with ASD from TD school-aged children. Attempts to use deep learning-based algorithms and brain frequencies to discriminate school-aged children with ASD from TD school-aged children should likely be a key step forward in auxiliary clinical utility.

关键词： stacked autoencoder Classification School-aged children Autism spectrum disorder Brain frequency

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Performance improvement of deep neural network classifiers by a simple training strategy

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 67卷 14-23页

作者： Caliskan, Abdullah Yuksel, Mehmet Emin Badem, Hasan Basturk, Alper Erciyes Univ Dept Biomed Engn Kayseri Turkey Erciyes Univ Dept Comp Engn Kayseri Turkey Kahramanmaras Sutcu Imam Univ Dept Comp Engn Kahramanmaras Turkey

Improving the classification performance of Deep Neural Networks (DNN) is of primary interest in many different areas of science and technology involving the use of DNN classifiers. In this study, we present a simple training strategy to improve the classification performance of a DNN. In order to attain our goal, we propose to divide the internal parameter space of the DNN into partitions and optimize these partitions individually. We apply our proposed strategy with the popular L-BFGS optimization algorithm even though it can be applied with any optimization algorithm. We evaluate the performance improvement obtained by using our proposed method by testing it on a number of well-known classification benchmark data sets and by performing statistical analysis procedures on classification results. The DNN classifier trained with the proposed strategy is also compared with the state-of-the-art classifiers to demonstrate its effectiveness. Our classification experiments show that the proposed method significantly enhances the training process of the DNN classifier and yields considerable improvements in the accuracy of the classification results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： autoencoder Deep neural network Deep learning Limited memory BFGS Softmax classifier stacked autoencoder

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Deep multi-view representation learning for social images

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APPLIED SOFT COMPUTING 2018年 73卷 106-118页

作者： Huang, Feiran Zhang, Xiaoming Zhao, Zhonghua Li, Zhoujun He, Yueying Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100029 Peoples R China

Multi-view representation learning for social images has recently made remarkable achievements in many tasks, such as cross-view classification and cross-modal retrieval. Since social images usually contain link information besides the multi-modal contents (e.g., text description, and visual content), simply employing the data content may result in sub-optimal multi-view representation of the social images. In this paper, we propose a Deep Multi-View Embedding Model (DMVEM) to learn joint embeddings for the three views including the visual content, the associated text descriptions, and their relations. To effectively encode the link information, a weighted relation network is built based on the linkages between social images, which is then embedded into a low dimensional vector space using the Skip-Gram model. The learned vector is regarded as the third view besides the visual content and text description. To learn a joint representation from the three views, a deep learning model with three-branch nonlinear neural network is proposed. A three-view bi-directional loss function is used to capture the correlation between the three views. The stacked autoencoder is adopted to preserve the self-structure and reconstructability of the learned representation for each view. Comprehensive experiments are conducted in the tasks of image-to-text, text-to-image, and image-to-image searches. Compared to the state-of-the-art multi-view embedding methods, our approach achieves significant improvement of performance. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Multi-view learning Image embedding Representation learning stacked autoencoder

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Deep networks in identifying CT brain hemorrhage

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第2期35卷 2215-2228页

作者： Helwan, Abdulkader El-Fakhri, Georges Sasani, Hadi Ozsahin, Dilber Uzun Near East Univ Dept Biomed Engn Near East Blvd TR-99138 Trnc Nicosia Turkey Harvard Med Sch Massachusetts Gen Hosp Gordon Ctr Med Imaging Radiol Boston MA USA

Deep learning algorithms have recently been applied to solving challenging problems in medicine such as medical image classification and analysis. In some areas, those algorithms have outperformed the human medical experts experience in diagnosis. Thus, in this paper we apply three different deep networks to solve the problem of brain hemorrhage identification in CT images. The motivation behind this work is the difficulty that radiologists encounter when diagnosing a hemorrhagic brain CT image, in particularly in the early stages of the brain bleeding. autoencoder (AE), stacked autoencoder (SAE), and convolutional neural network (CNN) are employed and trained to classify the CT images into hemorrhagic or non-hemorrhagic. Experimentally, it was found that all employed networks performed differently in terms of accuracy, error reached, and training time. However, stacked autoencoder has achieved a higher accuracy and lesser error compared to other used networks.

关键词： Deep learning deep networks hemorrhage autoencoder stacked autoencoder convolutional neural network

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Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018年第3期13卷 621-636页

作者： Aminanto, Muhamad Erza Choi, Rakyong Tanuwidjaja, Harry Chandra Yoo, Paul D. Kim, Kwangjo Korea Adv Inst Sci & Technol Sch Comp Daejeon 34141 South Korea Def Acad United Kingdom Cranfield Def & Secur Ctr Elect Warfare Informat & Cyber Shrivenham SN6 8LA Wilts England

The recent advances in mobile technologies have resulted in Internet of Things (IoT)-enabled devices becoming more pervasive and integrated into our daily lives. The security challenges that need to be overcome mainly stem from the open nature of a wireless medium, such as a Wi-Fi network. An impersonation attack is an attack in which an adversary is disguised as a legitimate party in a system or communications protocol. The connected devices are pervasive, generating high-dimensional data on a large scale, which complicates simultaneous detections. Feature learning, however, can circumvent the potential problems that could be caused by the large-volume nature of network data. This paper thus proposes a novel deep-feature extraction and selection (D-FES), which combines stacked feature extraction and weighted feature selection. The stacked autoencoding is capable of providing representations that are more meaningful by reconstructing the relevant information from its raw inputs. We then combine this with modified weighted feature selection inspired by an existing shallow-structured machine learner. We finally demonstrate the ability of the condensed set of features to reduce the bias of a machine learner model as well as the computational complexity. Our experimental results on a well-referenced Wi-Fi network benchmark data set, namely, the Aegean Wi-Fi Intrusion data set, prove the usefulness and the utility of the proposed D-FES by achieving a detection accuracy of 99.918% and a false alarm rate of 0.012%, which is the most accurate detection of impersonation attacks reported in the literature.

关键词： Intrusion detection system impersonation attack deep learning feature extraction stacked autoencoder large-scale Wi-Fi networks

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Deep learning for pixel-level image fusion: Recent advances and future prospects

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INFORMATION FUSION 2018年 42卷 158-173页

作者： Liu, Yu Chen, Xun Wang, Zengfu Wang, Z. Jane Ward, Rabab K. Wang, Xuesong Hefei Univ Technol Dept Biomed Engn Hefei 230009 Anhui Peoples R China Univ Sci & Technol China Dept Elect Sci & Technol Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

By integrating the information contained in multiple images of the same scene into one composite image, pixel-level image fusion is recognized as having high significance in a variety of fields including medical imaging, digital photography, remote sensing, video surveillance, etc. In recent years, deep learning (DL) has achieved great success in a number of computer vision and image processing problems. The application of DL techniques in the field of pixel-level image fusion has also emerged as an active topic in the last three years. This survey paper presents a systematic review of the DL-based pixel-level image fusion literature. Specifically, we first summarize the main difficulties that exist in conventional image fusion research and discuss the advantages that DL can offer to address each of these problems. Then, the recent achievements in DL-based image fusion are reviewed in detail. More than a dozen recently proposed image fusion methods based on DL techniques including convolutional neural networks (CNNs), convolutional sparse representation (CSR) and stacked autoencoders (SAEs) are introduced. At last, by summarizing the existing DL-based image fusion methods into several generic frameworks and presenting a potential DL-based framework for developing objective evaluation metrics, we put forward some prospects for the future study on this topic. The key issues and challenges that exist in each framework are discussed.

关键词： Image fusion Deep learning Convolutional neural network Convolutional sparse representation stacked autoencoder

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A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2019年第1期29卷 1850015-1850015页

作者： Hua, Chengcheng Wang, Hong Wang, Hong Lu, Shaowen Liu, Chong Khalid, Syed Madiha Northeastern Univ Dept Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China Univ Manchester Control Syst Ctr Manchester Lancs England Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110189 Liaoning Peoples R China

Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-datadriven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel EEG data into codes and then computes the dissimilarity between the codes from every pair of electrodes to build FBN. The highlight of this method is that the SAE has a multi-layered structure and is semi-supervised, which means it can dig deeper information and generate better features. Then an experiment was performed, the EEG of the operators were collected while they were operating and analyzed to detect their proficiency. The results show that the BCSAE method generated more number of separable features with less redundancy, and the average accuracy of classification (96.18%) is higher than that of the control methods: PLV (92.19%) and PLI (78.39%).

关键词： Functional brain connection EEG stacked autoencoder supervised fine-tuning proficiency detection

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