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检索条件"主题词=stacked autoencoder"

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The Comparison of deep learning recognition methods based on SAR image

The Comparison of deep learning recognition methods based on...

引用

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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Enhancing Speech and Music Discrimination Through the Integration of Static and Dynamic Features 25

Enhancing Speech and Music Discrimination Through the Integr...

引用

25th Interspeech Conference

作者： Chen, Liangwei Zhou, Xiren Tut, Qiang Chen, Huanhuan Univ Sci & Technol China Hefei Peoples R China Anhui Prov Hosp Hefei Peoples R China

Audio is inherently temporal data, where features extracted from each segment evolve over time, yielding dynamic traits. These dynamics, relative to the acoustic characteristics inherent in raw audio features, primarily serve as complementary aids for audio classification. This paper employs the reservoir computing model to fit the audio feature sequences efficiently, capturing feature-sequence dynamics into the readout models, and without the need for offline iterative training. Additionally, stacked autoencoders further integrate the extracted static features (i.e., raw audio features) with the captured dynamics, resulting in more stable and effective classification performance. The entire framework is called Static-Dynamic Integration Network (SDIN). The conducted experiments demonstrate the effectiveness of SDIN in speech-music classification tasks.

关键词： reservoir computing model stacked autoencoder speech-music classification audio processing

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Optimization method of diffuse optical tomography reconstruction based on neural network 10

Optimization method of diffuse optical tomography reconstruc...

引用

Conference on Optics in Health Care and Biomedical Optics X held at held at SPIE/COS Photonics Asia Conference

作者： Cao, Bin Zhang, Limin Shi, Ke Zhao, Zhichao Sun, Zhilong Li, Jiao Zhou, Zhongxing Gao, Feng Tianjin Univ Coll Precis Instrument & Optoeletron Engn Tianjin 300072 Peoples R China Tianjin Key Lab Biomed Detecting Tech & Instrumen Tianjin 300072 Peoples R China

ISBN: (数字)9781510639225

ISBN: (纸本)9781510639225

Diffuse Optical Tomography (DOT) is a promising non-invasive optical imaging technology that can provide structural and functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the reverse problem of DOT is ill-posed and ill-conditioned. In order to overcome these limitations, two types of neural networks, back-propagation neural network (BPNN) and stacked autoencoder (SAE) were applied in DOT image reconstruction, which use the internal optical properties distribution and the boundary measurement of biological tissues as the input and output data sets respectively to adjust the neural network parameters, and directly establish a nonlinear mapping of the input and output. To verify the effectiveness of the methods, a series of numerical simulation experiments were conducted, and the experimental results were quantitatively assessed, which demonstrated that both methods can accurately predict the position and size of the inclusion, especially in the case of higher absorption contrast. As a whole, SAE can get better reconstructed image results than BPNN and the training time was only a quarter of BPNN.

关键词： diffuse optical tomography back-propagation neural network image reconstruction stacked autoencoder

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Drug-Target Interaction Prediction in Coronavirus Disease 2019 Case Using Deep Semi-Supervised Learning Model 12

Drug-Target Interaction Prediction in Coronavirus Disease 20...

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12th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Sulistiawan, Faldi Kusuma, Wisnu Ananta Ramadhanti, Nabila Sekar Tedjo, Aryo IPB Univ Fac Math & Nat Sci Dept Comp Sci Bogor Indonesia IPB Univ Dept Comp Sci Bogor Indonesia IPB Univ Trop Biopharmaca Res Ctr Bogor Indonesia Univ Indonesia Fac Med Dept Med Chem Jakarta Indonesia

ISBN: (纸本)9781728192796

Coronavirus disease 2019 (COVID-19) is an infectious disease of the respiratory system that caused a pandemic in 2020. There is still not any effective special treatment to cure it. Drug repositioning is used to find an effective drug for curing new diseases by finding new efficacy of registered drug. The new efficacy can be conducted by elaborating the interactions between compounds and proteins (DTI). Deep Semi-Supervised Learning (DSSL) is used to overcome the lack of DTI information. DSSL utilizes unsupervised learning algorithms such as stacked Auto Encoder (SAE) as pre-training for initializing weights on the Deep Neural Network (DNN). This study uses DSSL with a feature-based chemogenomics approach on the data resulted from the exploration of potential anti-coronavirus treatment. This study finds that the use of fingerprints for compound features and Dipeptide Composition (DC) for protein features gives the best results on accuracy (0.94), recall (0.83), precision (0.817), F-measure (0.822), and AUROC (0.97). From the test data predictions, 1766 and 929 positive interactions are found on the test data and herbal compounds, respectively.

关键词： coronavirus disease 2019 drug repositioning deep semi-supervised learning stacked autoencoder deep neural network

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FORECAST OF LOGISTICS DEMAND BASED ON GREY DEEP NEURAL NETWORK MODEL 17

FORECAST OF LOGISTICS DEMAND BASED ON GREY DEEP NEURAL NETWO...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yuan, Wen-Jing Chen, Jian-Hua Cao, Jing-Jing Jin, Ze-Yi Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China

ISBN: (纸本)9781538652145

Aiming at the current problem of limited feature extraction ability and prediction accuracy of shallow neural networks, this paper proposes a grey deep neural network model by combining grey model and stacked autoencoder (SAE) to take advantage of their complementary merits for the particularity of logistics demand forecasting issue. Multiple grey model processes the original data first, then the output data is input into stacked autoencoder prediction (SAEP) model, applying SAE for feature representation at the bottom and extreme learning machine (ELM) for accurate prediction at the top, then the model gets a final prediction value. The proposed method is applied in the empirical research on the logistics demand in a Brazilian logistics company, and the predictive value obtains a relatively better accuracy than ordinary grey neural network model, thus the effectiveness of the proposed model has been verified.

关键词： Logistics demand prediction stacked autoencoder Grey model

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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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Historical Document Image Segmentation Combining Deep Learning and Gabor Features 17th

Historical Document Image Segmentation Combining Deep Learni...

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17th International Conference on Document Analysis and Recognition (ICDAR)

作者： Mehri, Maroua Sellami, Akrem Tabbone, Salvatore Univ Sousse LATIS Lab Adv Technol & Intelligent Syst Ecole Natl Ingenieurs Sousse Sousse 4023 Tunisia Univ Lorraine IDMC Inst Sci Digital Management & Cognit Pole Herbert Simon 13 Rue Michel Ney F-54000 Nancy France Univ Lille CNRS UMR 9189 CRIStAL Campus SciBatiment ESPRITAve Henri Poincare F-59655 Villeneuve Dascq France Univ Lorraine LORIA CNRS UMR 7503 Campus Sci615 Rue Jardin Bot F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783031416842;9783031416859

Due to the idiosyncrasies of historical document images (HDI), growing attention over the last decades is being paid for proposing robust HDI analysis solutions. Many research studies have shown that Gabor filters are among the low-level descriptors that best characterize texture information in HDI. On the other side, deep neural networks (DNN) have been successfully used for HDI segmentation. As a consequence, we propose in this paper a HDI segmentation method that is based on combining Gabor features and DNN. The segmentation method focuses on classifying each document image pixel to either graphic, text or background. The novelty of the proposed method lies mainly in feeding a DNN with a Gabor filtered image (obtained by applying specific multichannel Gabor filters) instead of an original image as input. The proposed method is decomposed into three steps: a) filtered image generation using Gabor filters, b) feature learning with stacked autoencoder, and c) image segmentation with 2D U-Net. In order to evaluate its performance, experiments are conducted using two different datasets. The results are reported and compared with those of a recent state-of-the-art method.

关键词： Historical document image segmentation Gabor filters Deep neural networks stacked autoencoder 2D U-Net architecture

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Efficient Framework for Predicting ncRNA-Protein Interactions Based on Sequence Information by Deep Learning 14th

Efficient Framework for Predicting ncRNA-Protein Interaction...

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14th International Conference on Intelligent Computing (ICIC)

作者： Zhan, Zhao-Hui You, Zhu-Hong Zhou, Yong Li, Li-Ping Li, Zheng-Wei China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 21116 Jiangsu Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China

ISBN: (纸本)9783319959320;9783319959337

The interactions between proteins and RNA (RPIs) play a crucial role in most cellular processes such as RNA stability and translation. Although there have been many high-throughput experiments recently to detect RPIs, these experiments are largely time-consuming and labor-intensive. Therefore, it is imminent to propose an efficient computational method to predict RPIs. In this study, we put forward a novel approach for predicting protein and ncRNA interactions based on sequences information only. By employing the bi-gram probability feature extraction method and k-mer algorithm, the represent features from protein and ncRNA were extracted. To evaluate the performance of the proposed model, two widely used datasets named RPI1807 and RPI2241 were trained with the adoption of random forest classifier by using five-fold cross-validation. The experimental results with the AUC of 0.992 and 0.947 on dataset RPI1807 and RPI2241 respectively indicated the effectiveness of our experimental approach for predicting RPIs, which provided the guidance for reference for future research in the biological field.

关键词： Protein-ncRNA interaction Bi-gram Deep learning stacked autoencoder PSSM

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Deep Learning for Integrated Analysis of Breast Cancer Subtype Specific Multi-omics Data

Deep Learning for Integrated Analysis of Breast Cancer Subty...

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IEEE-Region-10 Conference (IEEE TENCON)

作者： Rakshit, Somnath Saha, Indrajit Chakraborty, Subha Shankar Plewczyski, Dariusz Jalpaiguri Govt Engn Coll Dept Comp Sci & Engn Jalpaiguri India Natl Inst Tech Teachers Training & Res Dept Comp Sci & Engn Kolkata India Univ Warsaw Lab Funct & Struct Genom Ctr New Technol Warsaw Poland

ISBN: (纸本)9781538654576

Breast cancer is a deadly disease which commonly occurs all over the world and has been found to be the largest cause of cancer in females. Its detection is still a major challenge, both from a computational and biological point of views. Next Generation Sequencing (NGS) techniques have accelerated the mapping of human genomes rapidly. Involvement of advanced NGS techniques reveals that multiple genetic molecules are responsible for the cause of breast cancer and its subtypes. However, the high volume of data that is produced by the NGS techniques is difficult to study because of their high dimensionality and complexity. Thus, the integrated study of multi-omics data is one of the major challenges in medical science. This fact motivated us to study the NGS based high throughput expression data of miRNAs and mRNAs as well as Beta values of DNA Methylation of the corresponding mRNAs. In this regard, first, these datasets, together consisting of 33564 features of 305 patients in five classes viz. Luminal A, Luminal B, HER2-enriched, Basal-like and Control, are analysed in an integrated fashion using deep learning technique to classify the breast cancer subtypes properly. Second, the results of the deep learning technique are further analysed in order to identify the deeply connected features, i.e. either miRNA or mRNA or DNA Methylation, which are pivotal in the classification of breast cancer subtypes as well as play a crucial role in its formation. For this purpose, a deep learning technique, called stacked autoencoder is used to encode/transform the features into a low dimensional space, which is then fed to the five well known classifiers for classification. Moreover, the same encoded data is used to select the potential features after performing multiplication with the original data and Bonferroni correction on the p-values produced by the one-sample t-test. The results have been validated quantitatively and through biological significance analysis where oncogene

关键词： Breast cancer subtypes Deep learning Integrated analysis Multi-omics data stacked autoencoder

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Facial expression recognition via deep learning 1

Facial expression recognition via deep learning

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International Conference on Smart Computing (SMARTCOMP)

作者： Lv, Yadan Feng, Zhiyong Xu, Chao Tianjin Univ Sch Comp Software Tianjin 300072 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China

ISBN: (纸本)9781479957118

This paper mainly studies facial expression recognition with the components by face parsing (FP). Considering the disadvantage that different parts of face contain different amount of information for facial expression and the weighted function are not the same for different faces, an idea is proposed to recognize facial expression using components which are active in expression disclosure. The face parsing detectors are trained via deep belief network and tuned by logistic regression. The detectors first detect face, and then detect nose, eyes and mouth hierarchically. A deep architecture pretrained with stacked autoencoder is applied to facial expression recognition with the concentrated features of detected components. The parsing components remove the redundant information in expression recognition, and images don't need to be aligned or any other artificial treatment. Experimental results on the Japanese Female Facial Expression database and extended Cohn-Kanade dataset outperform other methods and show the effectiveness and robustness of this algorithm.

关键词： face parse Deep Belief Network stacked autoencoder expression recognition local feature

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