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A Double Channel CNN-LSTM Model for Text Classification

A Double Channel CNN-LSTM Model for Text Classification

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Shengbin Liang Bin Zhu Yuying Zhang Suying Cheng Jiangyong Jin School of Software Henan University Kaifeng China Branch China Petroleum Pipeline Engineering Co. Ltd Zhengzhou China

ISBN: (纸本)9781728176499;9781728176505

The CNN-LSTM model has the advantages of combining Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). It can perform timing analysis while extracting abstract features. It is widely used in Computer Vision and Natural Language Processing (NLP) fields and has achieved satisfactory results. However, for a large number of samples of complex text data, especially for words with ambiguous meanings, the word-level CNN-LSTM model is insufficient. Therefore, in order to solve this issue, this paper presents an improved Double Channel (DC) mechanism as a significant enhancement to CNN-LSTM. In this DC mechanism, two channels are used to receive word-level and char-level embedding respectively, at the same time. Hybrid Attention is proposed to combine the current time output with the current time unit state, and then using Attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed, and the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. After experimental comparison, the DC CNN-LSTM model proposed in this paper has significantly superior accuracy and F1-score compared with the basic CNN-LSTM model.

关键词： Computational modeling High performance computing conferences Text categorization Feature extraction data models Probability distribution

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An improved support vector machine for disease diagnosis 22

An improved support vector machine for disease diagnosis

引用

22nd international conference on Computer and Information Technology, ICCIT 2019

作者： Tania, Farjana Akter Chandra Shill, Pintu Khulna University of Engineering and Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9781728158426

Support Vector Machine is one of the most popular, promising and well-known machine learning tools for classification, regression and novelty detection. The performance of SVM totally depends on its core component of kernel functions like linear, polynomial and radial basis function (RBF). Conventional kernel functions have advantages and drawbacks, so it is very difficult to give the excellent performance while using only one kernel function for the large nonlinear dataset to nonlinear mapping from original input space to a high dimensional space. For this reason, in this paper, we proposed a hybrid kernel function combining linear and RBF kernel function and data preprocessing algorithms to reduce the noisy data for increasing learning ability and generalization performance. These two factors jointly improve the performance of SVM. In order to validate our proposed model, we used two different nonlinear breast cancer datasets and heart disease dataset with different characteristics. The proposed methods achieved an accuracy of 98.57%, 99.46% and 98.36% along with the sensitivity of 100% in the testing phase of these three datasets respectively. By comparing the simulation results, the results obtained from the proposed model are better than other SVMs constructed by ordinary kernel function and existing methods. © 2019 IEEE.

关键词： Support vector machines

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Efficient Network Administration for Smart Grid data Center

Efficient Network Administration for Smart Grid Data Center

引用

international conference on Advanced Communication Technology (ICACT)

作者： Seonyong Jeong Seungwon Shin Graduate School of Information Security KAIST Daejeon South Korea School of Electrical Engineering KAIST Daejeon South Korea

ISBN: (数字)9791188428045

ISBN: (纸本)9781728149431

Nowadays, the smart grid systems, which are intelligent electrical grid systems, are being installed in the real world. According to the statistics, about 79 million Advanced Metering Infrastructure is deployed in the United States and these devices generate terabytes to petabyte levels of electricity data. As IT technology developed, the energy provider can get beneficial information by processing the data in the way such as Machine learning or Deep learning. So smart grid administrator utilizes a data center to store the energy data and to process them. The amount of power data is increasing rapidly and the type of power data becoming more and more diverse. In addition, as computing power improves, a lot of advantageous applications such as blackout prediction is developed. In the smart grid, the importance of the data center becomes higher and higher. However, the traditional data center is designed in strict. Because the traditional data centers are designed by vendors, the operations of each component of centers are ineffective on the smart grid system. So in this paper, we applied the Software-Defined data Center, which controls the data center in a virtualized and programmable manner, to operate the data in a more flexible and interoperable way.

关键词： data centers Smart grids Access control Feature extraction Quality of service Software

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Deep ConvLSTM-Inception Network for Traffic Prediction in Smart Cities

Deep ConvLSTM-Inception Network for Traffic Prediction in Sm...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Penggui Huang Bi Huang Fan Zhao Yanfeng Zhang Mingong Chen School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781728176499;9781728176505

Accurate and real-time traffic prediction is essential for smart cities. However, due to the diversity of traffic point of interest in the city, the spatial correlation of urban traffic is extremely complicated. The use of ordinary spatial correlation feature extraction methods cannot cope with this complex spatial correlation. In addition, traffic flow changes also have temporal correlation. The traditional time series feature extraction method can't extract the temporal correlation of traffic data well. Therefore, we propose a ConvLSTM-Inception Network(CL-IncNet) model for traffic prediction. The CL-IncNet uses the Inception module to extract the multi-granularity spatial correlation and uses the Inception module's stack to extract the indirectly affected spatial correlation. In terms of extracting time-related features, we chose ConvLSTM structure. Compared with LSTM and GRU, this structure has fewer parameters and can guarantee the spatial relative relation of output information. The experimental results on the NYC-Taxi dataset show that our method outperforms four well-known baseline results.

关键词： Correlation Smart cities Time series analysis data visualization Predictive models Feature extraction Real-time systems

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Dual-loss path CNNs fusion architecture for image restoration with scattered data

Dual-loss path CNNs fusion architecture for image restoratio...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Linli Xu Jing Han Lianfa Bai Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728176505

High-resolution scattered data restoration is vital for series analysis of imaging through scattering medium. However, the existence of traditional methods mostly using deterministic transmission matrix, which limits the speckle de-correlations. Thus, learning based networks are applied to do image restoration through different random scattering mediums. In this work, we develop a novel dual-loss path networks fusion architecture for scattered image restoration focusing on improving reconstructed image details and the edge information. Our architecture consists of dual-loss path of convolution neural network (CNN) modules. In particular, we use the perceptual loss to extract the high level feature images, and the objective loss to extract the low level feature images, individually. After the both low level and high level feature manners are reconstructed by dual-path CNNs model, we use our proposed fusion method to combine them in frequency domain. Our network architecture is verified effective on two kinds of public datasets (MINST and Fashion-MINST). The experimental results show that our proposed method achieving better restoration than common CNN methods especially in the edges and the corner information of the predicted images.

关键词： Training Frequency-domain analysis Image edge detection Neural networks Scattering Computer architecture Network architecture

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Towards Aggregated Features: A Novel Proxy Detection Method Using NetFlow data

Towards Aggregated Features: A Novel Proxy Detection Method ...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Peipei Fu Qingya Yang Chang Liu Zhenzhen Li Gaopeng Gou Gang Xiong Li Guo Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728176499;9781728176505

Proxies can provide privacy and anonymity protection for users, but can also be exploited by attackers to hide their malicious behaviors. In order to strengthen the monitoring and management of network, proxy detection has become an urgent and challenging task. Although a great deal of efforts has been made for proxy detection, existing methods mostly rely on the packet-level features of a single flow, such as packet inter-arrival time and payload size. In addition, handling the raw traffic throughout the communication process may lead to user privacy leakage to a certain extent. Considering the use of multi-flow statistics and reducing the invasion of user privacy, in this paper, we propose a machine learning based approach for proxy detection with NetFlow data, which only contains session-level statistical information. We extract features through NetFlow data aggregation to build machine learning model and verify the performances on different combinations of features to get the optimal feature sets. Furthermore, the importance of appropriate time windows and minimum flow numbers of NetFlow data aggregation are demonstrated by comprehensive experiments. Based on the real-world datasets, our detection method can distinguish proxy and normal traffic accurately, and achieve about 96% True Positive Rate(TPR) in our experiments with random forest classifier.

关键词： Privacy data privacy High performance computing data aggregation Feature extraction data models Task analysis

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Troubleshooting Test Method Based on Industrial Control Grammar Model 22

Troubleshooting Test Method Based on Industrial Control Gram...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Cai, Junhui Li, Qianmu Chen, Yini Liu, Yaozong Xia, Yi Rahmany, Sajjad Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Jiangmen Peoples R China PT Sinoma Engn Indonesia Jakarta Indonesia Damghan Univ Sch Math & Comp Sci Damghan Iran

ISBN: (纸本)9781728116648

This paper starts with the network level in the realization of industrial control protocol, and gives a fuzzy security test method based on the grammatical model. This paper first expounds the concept of protocol description model, then gives the definition of related grammar, and proposes a grammar model for industrial control protocol based on high-order attribute grammar. The model can accurately describe the format and constraint relationship of the structured data of the industrial control protocol. On this basis, the model proposes a fuzzy security test algorithm, combined with the characteristics of the industrial control protocol, elaborates on the analysis tree structure, test case generation and mutation strategy. At the same time, the model performs comparative experiments by simulating Modbus/TCP communication between the master and slave stations in the industrial control system (based on Modbus/TCP and IEC-104 protocols), and statistically tests the test results. The above method verifies that anomalous results can still be found at a lower time cost when generating fewer test cases. To a certain extent, the experimental results reflect the improved relevance and effectiveness of test cases.

关键词： Industrial Control Grammar Model Fuzzy security test method industrial control protocol

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A Self-generating Prototype Method Based on Information Entropy Used for Condensing data in Classification Tasks 20th

A Self-generating Prototype Method Based on Information Entr...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Manastarla, Alberto Silva, Leandro A. Univ Prebiteriana Mackenzie Elect Engn & Comp Sao Paulo SP Brazil Univ Prebiteriana Mackenzie Elect Engn & Comp Comp & Informat Fac Sao Paulo SP Brazil

ISBN: (纸本)9783030336073;9783030336066

This paper presents a new self-generating prototype method based on information entropy to reduce the size of training datasets. The method accelerates the classifier training time without significantly decreasing the quality in the data classification task. The effectiveness of the proposed method is compared to the K-nearest neighbour classifier (kNN) and the genetic algorithm prototype selection (GA). kNN is a benchmark method used for data classification tasks, while GA is a prototype selection method that provides competitive optimisation of accuracy and the data reduction ratio. Considering thirty different public datasets, the results of the comparisons demonstrate that the proposed method outperforms kNN when using the original training set as well as the reduced training set obtained via GA prototype selection.

关键词： Prototype Selection (PS) data reduction data classification Genetic Algorithm (GA)

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LSTM based approach for diabetic symptomatic activity recognition using smartphone sensors 22

LSTM based approach for diabetic symptomatic activity recogn...

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22nd international conference on Computer and Information Technology, ICCIT 2019

作者： Bahadur, Erfanul Hoque Kadar Muhammad Masum, Abdul Barua, Arnab Rabiul Alam, Md. Golam Zaman Chowdhury, Md. Akib Uz Alam, Mohammad Robiul International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781728158426

Being concerned about the rising rate of the usage level of smartphone for last few years, researchers are striving to let out the disguised assets of smartphones. Regarding this phrase, the embedding of a variety of sensors such as accelerometer sensor, gyroscope sensor, humidity sensor etc. has attained the considerable attention of researchers for facilitating the human activity recognition task employing the sensor's applications. In this paper, we practiced the Long Short-Term Memory a.k.a. LSTM deep learning model with an aim to recognize thirteen human activities stated as walking, walking upstairs, walking downstairs, sitting, standing, jogging, squatting in the toilet, fallen down, lying, cycling, drinking, eating and genital itching. We opted for these activities with an intention of early diagnosing of diabetes in near future. We amassed data from four sensors stated accelerometer sensor, gyroscope sensor, humidity sensor and temperature sensor subjecting ten volunteers implying a frequency of 10Hz. The data was attained using an android application which was developed by us for the purpose of accumulation of sensor data from the smartphone. © 2019 IEEE.

关键词： Long short-term memory

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Automotive IVHM: Towards intelligent personalised systems healthcare 22

Automotive IVHM: Towards intelligent personalised systems he...

引用

22nd international conference on engineering Design, ICED 2019

作者： Campean, Felician Neagu, Daniel Doikin, Aleksandr Soleimani, Morteza Byrne, Thomas Sherratt, Andrew University of Bradford Automotive Research Centre United Kingdom Jaguar Land Rover

Underpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems. © 2019 Design Society. All rights reserved.

关键词： Life cycle

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