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IEEE International Conference on Communications (IEEE ICC)

作者： Zhou, Tianci Zeng, Yong Chen, Yitao Liu, Zhihong Ma, Jianfeng Xidian Univ Sch Cyber Engn Xian Peoples R China WuHan Marine Commun Res Inst Wuhan Peoples R China

ISBN: (纸本)9781538674628

Recent studies show that the traffic fingerprints constructed by encrypted voice traffic features will uncover users' activities. Current researches have focused on using machine learning models to build traffic fingerprints, however, they pay less attention to the distribution of traffic data. We note that encrypted voice traffic data is bimodal distributed, which is different from other encrypted traffic following Gaussian distribution. It means that present encoding model may destroy the distribution characteristics of encrypted voice traffic data, leading to a decrease in classification accuracy. To ameliorate this issue, our study proposes the combination of Discrete Fourier Transform and stacked autoencoder as traffic feature encoding model. The former is able to map the encrypted voice traffic features from bimodal distribution to frequency-domain space while keeping the trend of its features. And the latter can further improve features utilization in classification model. Finally, we achieved 96.08% accuracy on Amazon Echo dataset and 99.53% accuracy on Google Home dataset, which get the SOTA(state of the art) results on both encrypted voice traffic datasets.

关键词： Encrypted traffic classification Discrete Fourier Transform stacked autoencoder Non-Gaussian distribution

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Deep Subspace Mapping in Hyperspectral Imaging 10

Deep Subspace Mapping in Hyperspectral Imaging

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Conference on Electro-Optical Remote Sensing X

作者： Wadstromer, Niclas Gustafsson, David Petersson, Henrik Bergstrom, David Swedish Def Res Agcy FOI Linkoping Sweden

ISBN: (纸本)9781510603806;9781510603813

We propose a novel Deep learning approach using autoencoders to map spectral bands to a space of lower dimensionality while preserving the information that makes it possible to discriminate different materials. Deep learning is a relatively new pattern recognition approach which has given promising result in many applications. In Deep learning a hierarchical representation of increasing level of abstraction of the features is learned. autoencoder is an important unsupervised technique frequently used in Deep learning for extracting important properties of the data. The learned latent representation is a non-linear mapping of the original data which potentially preserve the discrimination capacity.

关键词： hyperspectral imaging subspace mapping Deep learning autoencoder stacked autoencoder

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Deep Learning Application Trial to Lung Cancer Diagnosis for Medical Sensor Systems 13

Deep Learning Application Trial to Lung Cancer Diagnosis for...

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International SoC Design Conference (ISOCC)

作者： Shimizu, Ryota Yanagawa, Shusuke Monde, Yasutaka Yamagishi, Hiroki Hamada, Mototsugu Shimizu, Toru Kuroda, Tadahiro Keio Univ Fac Sci & Technol Yokohama Kanagawa Japan

ISBN: (纸本)9781509032198

Personal and easy-to-use health checking system is an attractive application of sensor systems. Sensing data analysis for diagnosis is important as well as preparing small and mobile sensor nodes because sensing data include variations and noises reflecting individual difference of people and sensing conditions. Deep Neural Network, or Deep Learning, is a well-known method of machine learning and it is effective for feature extraction from pictures. Then, we thought Deep Learning also can extract features from sensing data. In this paper, we tried to build a diagnosis system of lung cancer based on Deep Learning. Input data of the system was generated from human urine by Gas Chromatography Mass Spectrometer (GC-MS) and our system achieved 90% accuracy in judging whether the patient had lung cancer or not. This system will be useful for pre- and personal diagnosis because collecting urine is very easy and not harmful to human body. We are targeting installation of this system not only to gas chromatography systems but also to some combination of multiple sensors for detecting gases of low concentration.

关键词： Deep Learning Deep Neural Network stacked autoencoder Gas Chromatography Mass Spectrometer(GC-MS)

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Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

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Journal of Harbin Institute of Technology(New Series) 2022年第4期29卷 1-6页

作者： Lasheng Yu Xiaopeng Zheng School of Computer Science and Engineering Central South UniversityChangsha 410083China

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they *** this stage,there are a lot of intelligent tutoring *** these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available *** these data,personalized education that suits each student can be *** improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep ***,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the *** some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning *** main contribution of this paper is in two *** is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political *** other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting *** paper proposes an adaptive algorithm for the original DKT *** using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳*** model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

关键词： LSTM knowledge of tracking DKT stacked autoencoder forgetting behavior feature information

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Emotion Recognition from Face Dataset Using Deep Neural Nets

Emotion Recognition from Face Dataset Using Deep Neural Nets

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International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Das, Deepjoy Chakrabarty, Alok Natl Inst Technol Meghalaya Dept Comp Sci & Engn Shillong Meghalayn India

ISBN: (纸本)9781467399104

The work presents an approach towards facial emotion recognition using face dataset consisting of four classes of emotions (happy, angry, neutral and sad) with different models of deep neural networks and compares their performance. We take the raw pixels values of all images in CMU face images dataset. The pixels values were represented by higher level concepts by feeding them into Restricted Boltzmann Machine, Deep Belief Networks and stacked autoencoder with Softmax Function. We observe that the later model could learn to recognize emotion with significantly higher accuracy compared to the former two models. Also, its performance improves with an increase in the number of hidden nodes in autoencoders, unlike the other two models.

关键词： Emotion Recognition CMU Face Images Data Set Restricted Boltzmann Machine Deep Belief Networks stacked autoencoder Softmax Function

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Classification of Schizophrenia Versus Normal Subjects Using Deep Learning 16

Classification of Schizophrenia Versus Normal Subjects Using...

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10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)

作者： Patel, Pinkal Aggarwal, Priya Gupta, Anubha Indraprastha Inst Informat Technol Delhi IIIT D Dept Elect & Commun Engn SBILab Delhi India

ISBN: (纸本)9781450347532

Motivated by deep learning approaches to classify normal and neuro-diseased subjects in functional Magnetic Resonance Imaging (fMRI), we propose stacked autoencoder (SAE) based 2-stage architecture for disease diagnosis. In the proposed architecture, a separate 4-hidden layer autoencoder is trained in unsupervised manner for feature extraction corresponding to every brain region. Thereafter, these trained autoencoders are used to provide features on class-labeled input data for training a binary support vector machine (SVM) based classifier. In order to design a robust classifier, noisy or inactive gray matter voxels are filtered out using a proposed covariance based approach. We applied the proposed methodology on a public dataset, namely, 1000 Functional Connectomes Project Cobre dataset consisting of fMRI data of normal and Schizophrenia subjects. The proposed architecture is able to classify normal and Schizophrenia subjects with 10-fold cross-validation accuracy of 92% that is better compared to the existing methods used on the same dataset.

关键词： Classification Support Vector Machine stacked autoencoder Schizophrenia

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SAR-images Categorization and Applications 59

SAR-images Categorization and Applications

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59th ELMAR International Symposium (ELMAR)

作者： Planinsic, Peter Gleich, Dusan Univ Maribor Fac Elect Engn & Comp Sci Smetanova 17 SLO-2000 Maribor Slovenia

ISBN: (纸本)9789531842303

Radars, especially Synthetic Aperture Radars (SAR) are key sensors for satellite and terrestrial remote sensing applications. The main advantage of SAR-technology over optical sensor systems (LIDAR, visible, infrared and hyperspectral cameras), is its ability to capture SAR-images in all weather conditions, day and night. The high resolution (of one meter) TerraSAR-X mission and Tandem TerraSAR-X mission, launched by the German Aerospace Agency in 2007 and 2010, respectively, and free accessible images from Sentinel missions of the European Space Agency (ESA), started in 2016, enable an enormous amount of remote sensing data for different Earth observation applications, such as disaster and pollution monitoring and prevention, for military and civil applications. Tandem sensors provide precise interferometric measurements with resolution of a few millimeters. For all applications, appropriate SAR-images' analysis is crucial. In this paper, we will present some of our own developed algorithms for SAR-images' analysis, classification and characterization, parametric and nonparametric, based mainly on wavelet multiresolution analysis, Bayesian statistics and Deep learning classification. Some applications will be reviewed.

关键词： SAR-imagery Remote Sensing Categorization Classifcation Multiresolution Analysis stacked autoencoder

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A Deep Learning Framework to Predict Rating for Cold Start Item Using Item Metadata 28

A Deep Learning Framework to Predict Rating for Cold Start I...

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28th IEEE International Conference on Enabling Technologies - Infrastructure for Collaborative Enterprises (WETICE)

作者： Anwaar, Fahad Iltaf, Naima Afzal, Hammad Abbas, Haider Natl Univ Sci & Technol NUST Mil Coll Signals Islamabad Pakistan

ISBN: (纸本)9781728106762

Recommender systems improve browsing experience of users for large amount of items by assisting selection and classification of items utilizing item metadata. The performance of recommender system usually deteriorates when implicit data is used with limited user interaction history also regarded as cold start (CS) problem. This paper proposes a model to address cold start problem using content based technique where user or item metadata is used to break this ice barrier. The proposed method utilizes the feature extraction techniques (such as term frequency-Inverse document frequency(TF-IDF)) and word embedding technique (Word2Vec). These content features are then used to predict the ratings for CS items by constructing user profiles using stacked auto-encoder. Experiments performed on largest real world dataset provided by Movielens 20M shows that proposed model outperforms the state-of-the-art approaches in CS item scenario.

关键词： Collaborative Filtering Neural Network Word2vec Cold Start profile Learner stacked autoencoder

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An Adaptive Soft Sensor Method based on Online Deep Evolving Fuzzy System for Industrial Process Data Streams 12

An Adaptive Soft Sensor Method based on Online Deep Evolving...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Gao, Yu Jin, Huaiping Wang, Bin Yang, Biao Yu, Wangyang Kunming Univ Sci & Technol Fac Informat Engn & Automat Dept Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China Wuhan Maritime Commun Res Inst Wuhan 430223 Peoples R China

ISBN: (纸本)9798350321050

In recent years, deep learning techniques have been widely applied in soft sensor modeling. stacked autoencoder (SAE) networks are particularly effective at discovering complex data patterns due to their hierarchical structures. However, process data are typically generated as data streams, which poses a great challenge to capture the time-varying characteristics of the process for traditional soft sensor models based on SAE. Furthermore, the insufficiency of offline pre-training data further limits the feature representation capability of SAE. To address these problems, an online deep evolving fuzzy system (ODEFS) based adaptive soft sensor method for process data streams is proposed. In the offline modeling phase, quality-related stacked autoencoder (QSAE) is pre-trained as representation layer to mine quality-related feature representations, while an evolving fuzzy system with self-organization capability is built as the prediction layer. In the online implementation phase, the topology-preserving loss is added to the learning process of QSAE feature network to enable continuous learning of feature representations and alleviate the catastrophic forgetting problem. Meanwhile, the shallow EFS network handles concept drift in data patterns by self-adjusting the structure and parameters. The proposed ODEFS method can improve the feature representation capability of SAE in a data streaming environment and the ability to handle time-varying characteristics, thus ensuring better prediction accuracy. The effectiveness and superiority of the proposed method are verified on TE process.

关键词： Industrial process data streams Adaptive soft sensor Evolving fuzzy system stacked autoencoder Topology preserving loss

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Deep Learning in Indoor Localization Using WiFi 1st

Deep Learning in Indoor Localization Using WiFi

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1st International Telecommunications Conference (ITelCon)

作者： Turgut, Zeynep Ustebay, Serpil Aydin, Gulsum Zeynep Gurkas Sertbas, Ahmet Halic Univ Istanbul Turkey Istanbul Univ Istanbul Turkey

ISBN: (纸本)9789811304088;9789811304071

In this study, the indoor localization was performed on indoor networks. WiFi technology is located in almost every building. For this reason, WiFi technology has been selected to perform positioning, and RSSI values from WiFi technology access points have been examined. For this purpose, RFKON_MB_WIFI dataset in RFKON database which is a sample database is used and data of 18480 signal strength are analyzed. The Fingerprinting method of Scene Analysis methods is used to perform the localization process. As a first step, the signal strengths in the data set are normalized by preprocessing. In the second step, positioning was performed using SVM, PCA, LDA, KNN, N3, BNN, Naive Bayes Classification, and Deep Learning methods. When the results obtained are compared, the most successful result is obtained from deep learning that is known to have a high accuracy on big data with an accuracy of 95.95%.

关键词： Indoor Localization Deep learning stacked autoencoder Fingerprinting

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