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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是171-180 订阅

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A systematic approach towards development of universal software fault prediction model using object-oriented design measurement 3rd

A systematic approach towards development of universal softw...

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3rd international conference on Nanoelectronics, Circuits and Communication Systems, NCCS 2017

作者： Kumari, Dipti Rajnish, Kumar Department of Computer Science & Engineering BIT Mesra RanchiJharkhand835215 India

ISBN: (纸本)9789811307751

A new systematic approach towards development of a software fault prediction model for trustworthiness and excellence enhancement of software systems by predicting fault-proneness in different severity level of module after coding has been performed in this paper. The classification ability of data mining technique and important object-oriented metrics of object-oriented software system are used to develop this fault prediction model. This model predict the severity level of object-oriented software module based on the number of faults in nominal, low, mid, high and not fault prone (NFP). A different decision tree like Hoeffding tree (VFDT) is created for existing project data Eclipse 2.0 in order to gain information for the purpose of decision-making whether a particular module is faulty or not, and if faulty then in which category. The rules that are obtained from VFDT decision tree algorithm are transformed into fuzzy rules and join together with the fuzzy inference system (FIS) of soft computing approach to predict fault-proneness of object-oriented module in different categories of fault or also not fault free for target data that is other two version of existing project data Eclipse 2.1 and Eclipse 3.0. The main aim of developing this fault prediction model is that to give a direct help to software manager in identifying fault-prone module by including this model before testing phase and just after coding phase so that testing effort can be reduced. And indirectly, this model helps the common people in getting reliable and quality-based system. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Forecasting

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A Path Allocation Method Based on Source Routing in SDN Traffic Engineering 4

A Path Allocation Method Based on Source Routing in SDN Traf...

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4th IEEE international conference on Smart Cloud (IEEE SmartCloud) / 3rd IEEE international Symposium on Reinforcement Learning (IEEE ISRL)

作者： Dong, Qian Li, Jun Ma, Yuxiang Han, Shujun Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Foshan Univ Sch Elect Informat Engn Foshan 528000 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China

ISBN: (纸本)9781728155050

SDN traffic engineering plays an important role in today's Internet, cloud computing and sensor network. Source routing combined with SDN traffic engineering has broad application prospects. There are two processing procedures of SDN traffic engineering when building a model, the difference of them is whether to list all candidate paths firstly or not. In order to make the traffic engineering achieves the best results, for a source-destination pair, the form of the solution of general SDN traffic engineering model is the load on each link in the network. However, based on this form of the solution, we cannot configure source routing directly. In this paper, we propose the problem of path allocation based on source routing that helps to deploy source routing configuration at the source node. We model and analyze this problem, then a relatively simplified method that obtains forwarding paths sequentially is proposed. The evaluation results indicate that the proposed method can address this problem effectively, and can meet traffic demands as much as possible by using source routing. In addition, the favorable effects of the combination of source routing and SDN are also verified.

关键词： SDN traffic engineering source routing segment routing preferred path routing path allocation

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FLSTM: Feature Pattern-Based LSTM for Imbalanced Big data Analysis 3rd

FLSTM: Feature Pattern-Based LSTM for Imbalanced Big Data An...

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3rd international conference on Cyberspace data and Intelligence, Cyber DI 2019, and the international conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Xu, Liang Zeng, Xingjie Zhang, Weishan Yuan, Jiangru Ren, Pengcheng Zhang, Ruicong Guo, Wuwu Zhou, Jiehan University of Science and Technology Beijing Beijing100083 China China University of Petroleum QingdaoShandong266580 China Research Institute of Petroleum Exploration and Development Beijing100083 China University of Oulu Oulu90014 Finland

ISBN: (纸本)9789811519215

It is important for monitoring and predicting equipment failures. The existing fault prediction method has poor efficiency and accuracy on processing imbalanced data. This paper proposes a feature pattern-based LSTM method (called FLSTM, Feature based Long Short Term Memory) to analyze failures through processing imbalanced data. The method constructs a time-series feature matrix as the input to the LSTM model. In addition, we propose a failure prediction system based on Hadoop environment. The experimental results show that the FLSTM can improve failure prediction with imbalanced big data and the failure prediction system performs well. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Improved collusion-resisting secure nearest neighbor query over encrypted data in cloud

Improved collusion-resisting secure nearest neighbor query o...

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13th international conference on Information Security Practice and Experience (ISPEC) / 3rd international Symposium on Security and Privacy in Social Networks and Big data (SocialSec)

作者： Zhu, Youwen Zhang, Yue Li, Xingxin Yan, Hongyang Li, Jing Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Safety Crit Software Nanjing 210016 Jiangsu Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Cryptog & Informat Secur Guilin 541004 Peoples R China Nankai Univ Coll Comp & Control Engn Tianjin 300350 Peoples R China Guangzhou Univ Sch Comp & Educ Software Guangzhou 510006 Guangdong Peoples R China

Securely performing nearest neighbor query over encrypted data in cloud is an important topic in the area of cloud computing, for which Wang et al recently put forward a scheme (ie, CloudBI-II) to address the challenging security problem: resisting the collusion of cloud server and query users. In this paper, we propose an efficient attack method that indicates CloudBI-II will reveal the difference vectors under the collusion attack. Furthermore, we show that the difference vector disclosure will result in serious privacy breach and, thus, attain an efficient attack method to break CloudBI-II. Namely, CloudBI-II cannot achieve their declared security. Through theoretical analysis and experiment evaluation, we confirm that our proposed attack approach can fast recover the original data from the encrypted data set in CloudBI-II. Finally, we provide an enhanced scheme that can efficiently resist the collusion attack.

关键词： cloud computing collusion attack privacy secure nearest neighbor query security problems

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Modelling and Forecasting Indoor Illumination Time Series data from Light Pipe System 3rd

Modelling and Forecasting Indoor Illumination Time Series Da...

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3rd international conference of Reliable Information and Communication Technology (IRICT)

作者： Waheeb, Waddah Ghazali, Rozaida Ismail, Lokman Hakim Abd Kadir, Aslila Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Univ Tun Hussein Onn Malaysia Fac Civil & Environm Engn Dept Design Engn & Architecture Batu Pahat 86400 Johor Malaysia Univ Tun Hussein Onn Malaysia Ctr Diploma Studies Dept Civil Engn Batu Pahat 86400 Johor Malaysia

ISBN: (纸本)9783319990071;9783319990064

Light pipe system allows the transmission of natural light from outside to inside of a room. It is needed where daylight cannot reach due to some reasons such as building design. Modeling and forecasting the indoor illumination amounts obtained using such system could help to estimate the needed artificial lighting. Thus, it provides occupants with a high satisfaction of visual comfort along with low energy consumption for lighting. In this research work, the indoor illumination data was obtained using a system under Malaysia climate condition. Four methods are used for modeling and forecasting tasks namely seasonal naive, seasonal autoregressive integrated moving-average, seasonal and trend decomposition using Loess (STL), and TBATS. Results showed that the forecasts of the decomposed time series using STL method outperforms other methods in terms of the widely used forecasting metrics such as root mean squared error, mean absolute error, and mean absolute percentage error. Therefore, it can be used to forecast the indoor illumination, and as a result the needed artificial light amounts per day can be provided for the room on time.

关键词： Time series Forecasting Green building Sustainable daylighting system Light pipes system Seasonal and trend decomposition STL

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A data-Driven Model Approach for DayWise Stock Prediction 1

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3rd international conference on Emerging Research in Electronics, Computer science and Technology (ICERECT)

作者： Unnithan, Nidhin A. Gopalakrishnan, E. A. Menon, Vijay Krishna Soman, K. P. Amrita Vishwa Vidyapeetham Ctr Computat Engn & Networking CEN Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (数字)9789811358029

ISBN: (纸本)9789811358029;9789811358012

Economy of a country is closely related to stock market. By analysing stock market performance, we can evaluate whether a country's economic growth is increasing or decreasing. Even though country's economic growth can be understood by predicting stock market, it is highly unpredictable. We used dynamic mode decomposition which is a spatio-temporal, equation-free, data-driven algorithm for stock market prediction Schmid (J FluidMech 656:5-28, [13]) by considering stock markets as a dynamical system. How the system evolves and prediction of future state is done using DMD by decomposing a spatio-temporal system to modes having predetermined temporal behaviour. We used this property of DMD to predict stock market behaviour. In Kuttichira et al. (2017 international conference on advances in computing, communications and informatics (ICACCI), IEEE, pp 55-60, [7]) DMD was used to predict Indian stock market for minutewise data. We used daywise data as our timescale. Time series data of stock price of companies listed inNational Stock Exchange were used as data. Sampled daywise stock price of companies across sector was used to predict the stock price for next few days. Predicted prices were compared with original prices and mean absolute percentage error was used to calculate the deviation for every companies. We analysed the stock price prediction for both intra- and intersector companies. We used dynamic mode decomposition to predict the stock price using historical data. We also did fine tuning of sampling windows to find out the best parameters for our data set.

关键词： Time series Stock market DMD

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Web crawler design, implementation and data analysis about sina weibo 3

Web crawler design, implementation and data analysis about s...

引用

3rd IEEE international conference on Electronic Information Technology and Computer Engineering, EITCE 2019

作者： Dawei, Wang Hamzah, Muzaffar Weng, Ng Giap Hebei University of Engineering Graduate Department HanDan China FCI UNIVERISTY S MALAYSIA SABAH. UMS Faculty of Computing and Informatics Kota kinabalu Malaysia

ISBN: (纸本)9781728135847

In this paper, discussed designed and implemented a crawler base on python language and use this program crawl posts from Sina weibo. The key word is academic library. The time span is from 1/1/2018 to 31/12/2018 and the total number is 39374 and make data clean, cut words and delete all text that no related to academic library. After that, analyzed this data set for knowing the user's attitude about library. In order to describe the situation better, high frequency words rank, cloud word, semantic network about image of Library service, semantic network been used. Finally, according to the user's attitude, make the the necessary discuss. © 2019 IEEE.

关键词： Libraries

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Deep analytics based on triathlon athletes’ blogs and news 23rd

Deep analytics based on triathlon athletes’ blogs and news

引用

23rd international conference on soft computing, MENDEL 2017

作者： Fister, Iztok Fister, Dušan Rauter, Samo Mlakar, Uroš Brest, Janez Faculty of Electrical Engineering and Computer Science University of Maribor Smetanova 17 Maribor2000 Slovenia Faculty of Mechanical Engineering University of Maribor Smetanova 17 Maribor2000 Slovenia Faculty of Sport University of Ljubljana Gortanova 22 Ljubljana1000 Slovenia

ISBN: (纸本)9783319978871

Studying the lifestyle of various groups of athletes has been a very interesting research direction of many social sport scientists. Following the behavior of these athletes’ groups might reveal how they work, yet function in the real-world. Triathlon is basically depicted as one of the hardest sports in the world (especially long-distance triathlons). Hence, studying this group of people can have a very positive influence on designing new perspectives and theories about their lifestyle. Additionally, the discovered information also helps in designing modern systems for planning sport training sessions. In this paper, we apply deep analytic methods for discovering knowledge from triathlon athletes’ blogs and news posted on their websites. Practical results reveal that triathlon remains in the forefront of the athletes’ minds through the whole year. © Springer Nature Switzerland AG 2019.

关键词： data mining

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data dimensionality reduction (DDR) scheme for intrusion detection system using ensemble and standalone classifiers 3rd

Data dimensionality reduction (DDR) scheme for intrusion det...

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3rd international conference on Advances in computing and data sciences, ICACDS 2019

作者： Bansal, Ashu Kaur, Sanmeet Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9789811399381

The growth in IT sector is touching new pinnacles day by day, and hence the number of devices that are connected through Internet have increased tremendously, resulting into Big data issue, more computation time and an increased rate of malicious activities. Thus, to provide more security, Intrusion Detection System (IDS) were introduced which played a major role in the past few years, when it comes to security. With an intent to develop a more efficient IDS, one needs to explore several data Mining Strategies in the domain of data Analytics. While consulting the domain of data Analytics one fundamental problem that is encountered is high dimensional data. Hence, for reducing the dimensions of data a data Dimensionality Reduction Scheme has been proposed which minimizes the number of features, dimensions and tuples in the Training set in order to increase detection rates for IDS. The scheme proposed has been evaluated with two approaches - Ensemble approach and the Standalone Classifier approach. The dataset used for the experiment is benchmark dataset NSL-KDD and latest intrusion dataset CICIDS 2017. © Springer Nature Singapore Pte Ltd 2019.

关键词： data Analytics

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Improved U-Net for guidewire tip segmentation in X-ray fluoroscopy images 3

Improved U-Net for guidewire tip segmentation in X-ray fluor...

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3rd international conference on Advances in Image Processing, ICAIP 2019

作者： Guo, Shuai Tang, Songyuan Zhu, Jianjun Fan, Jingfan Ai, Danni Song, Hong Liang, Ping Yang, Jian Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Electronics Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Department of Interventional Ultrasound Chinese PLA General Hospital 28 Fuxing Road Beijing100853 China

ISBN: (纸本)9781450376754

In percutaneous coronary intervention (PCI), physicians use a guidewire tip to implant stents in vessels with stenosis. Given the small scale and low signal-to-noise ratio of guidewire tips in X-ray fluoroscopy images, physicians experience difficulty in recognizing and locating the tip. The automatic segmentation of the guidewire tip can ease navigation when the physicians implant stents for PCI. In this paper, we propose an end-to-end convolutional neural network-based method for guidewire tip segmentation. The network framework is derived from U-Net, and two specific designs involving reduced dense block and connectivity supervision are embedded in the framework to improve the accuracy and robustness of guidewire tip segmentation. Experiments are performed on clinical data. The proposed method achieves mean sensitivity, F1-score, Jaccard index, Hausdorff distance of 92.95%, 91.35%, 84.14%, and 0.531 mm on testing data, respectively. In addition, the segmentation time is 0.02 s/frame, which can satisfy the requirements for clinical intra-practice. © 2019 Association for computing Machinery.

关键词： Stents

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