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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Applying Deep learning to Balancing Network Intrusion Detection datasets

Applying Deep Learning to Balancing Network Intrusion Detect...

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international conference on Advanced Infocomm Technology (ICAIT)

作者： Po-Jen Chuang Dong-Ye Wu Department of Electrical and Computer Engineering Tamkang University Tamsui Taiwan 25137

In this investigation, we apply deep learning to generate a desirable data generation model which helps to balance the network intrusion detection datasets and enhance the detection performance. the basic idea is to adopt deep variational autoencoders to generate new data and adjust the imbalance of datasets into more favorable balanced datasets. We then use the generated balanced datasets to reduce the deviation of classifiers (incurred by data imbalance) in training and, as a result, to enhance the performance of intrusion detection. As experimental results exhibit, in comparison to unbalanced datasets, balanced datasets can practically lead to better classification accuracy, especially when facing unknown attacks. Balanced datasets also help to solve the over-fitting problem (due to imbalance of datasets) in intrusion detection model training, to ensure that the trained intrusion detection model will not misjudge new types of data - even if they are not in the training dataset.

关键词： Encoding Training data models Gaussian distribution Decoding Deep learning Training data

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A Multi-agent Framework for Medical Diagnosis Driven Smart data in a Big data Environment 11th

A Multi-agent Framework for Medical Diagnosis Driven Smart D...

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11th international conference on Interactive Mobile Communication Technologies and learning (IMCL)

作者： Elaggoune, Zakarya Maamri, Ramdane Boussebough, Imane Univ Constantine 2 Abdelhamid Mehri LIRE Lab Constantine Algeria

ISBN: (纸本)9783319751757;9783319751740

In the era of big data, recent developments in the field of information and communication technologies are facilitating organizations to innovate and grow. these technological developments and wide adaptation of ubiquitous computing enable numerous opportunities for government and companies to discover useful trends or patterns that are used in health-care decision making. A common problem affecting data quality is the presence of noise and irrelevant information which can lead decision makers to a wrong decision. intelligent Decision Support System (IDSS) an automated judgment that supports decision making is composed of human and computer interaction to help in decision-making accuracy. Also, multi-agent systems (MAS) are collections of independent intelligent entities that collaborate in the joint resolution of a complex problem. Multi-agent IDSS can be used to solve large-scale convention problem. In this paper, we introduce a multiagent-MapReduce framework based dimension reduction for medical diagnosis that can filter the noise and irrelevant information and keeps only smart data, which can lead to a reduced storage space in one hand and produce a better healthcare decision in the other hand.

关键词： Big data MapReduce Multi-agent Smart data intelligent Decision Support Systems Diagnosis Health-care

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Advanced transmission methods applied in remote consultation and diagnosis platform 11th

Advanced transmission methods applied in remote consultation...

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11th international KES conference on intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018

作者： Deng, Zhuofu Chen, Yen-wei Zhu, Zhiliang Li, Yinuo Li, He Wang, Yi College of Software Engineering Northeastern University Shenyang China College of Information Science and Engineering Ritsumeikan University Kyoto Japan

ISBN: (纸本)9783319922300

Remote consultation and diagnosis platform has been widespread in market for group diagnosis, education and etc. It provides convenience for people enjoying superior quality of medical service. However, most of platforms cannot meet increasing demands in practice. the most important reason is that they have troubles of navigating in complicated Internet condition. In general, remote rendering is preferred to support data transmission, by which server renders images locally, captures screenshot and delivers them to clients synchronously. As a result of neighborhood DICOM slices serving a high similarity, following frame is compressed according to the difference with last one. Even though necessary demand of bandwidth has been reduced a lot, remaining volume proves too large especially remote 3D volume rendering. In this paper, we proposed a novel method bit difference compression transmission. Compared with traditional algorithm, it redesigns a new data structure, which gives more significances to bit. Consequently, demands of network fall into a desirable amount. Specifically, we improved solution of 3D images sharing and collaboration. Instead of terrible screenshots, synchronization of cameras in clients has been introduced. With a minimal cost our platform realized the cumbersome process. Collected data from experiments demonstrated our proposed methods have an obvious superiority and run robust in different environment of Internet. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Image enhancement

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Traffic Flow Prediction Based On Expressway Operating Vehicle data 11

Traffic Flow Prediction Based On Expressway Operating Vehicl...

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11th international conference on intelligent Computation Technology and Automation (ICICTA)

作者： Ai, Yunfei Bai, Zhiming Su, Hang Zhong, Nan Sun, Yunhua Zhao, Jiandong Natl Engn Lab Transportat Safety & Emergency Info Beijing 100011 Peoples R China Beijing Jiaotong Univ Sch Mech & Elect Control Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Minist Educ Key Lab Urban Transportat Complex Syst Theory & T Beijing 100044 Peoples R China

ISBN: (纸本)9781538683088

Expressway traffic flow prediction is an important basis for traffic and transportation management department, and it is also an important support for promoting the development of intelligent transportation. therefore, this paper predicts the expressway traffic flow based on the data of expressway operating vehicles in Jiangxi Province. the main work includes: cleaning the abnormal data of the original data, then fill in the missing data with the trend-history filling algorithm, and next calculating the speed time series in the cycle of 10 minutes. Finally, the support vector machine algorithm is used to predict the expressway traffic flow, and the prediction result is great. It can provide data foundation and technical support for decision management of traffic management departments.

关键词： Support vector machine Traffic flow prediction Expressway

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Art and Automation of Teaching Malware Reverse engineering 5th

Art and Automation of Teaching Malware Reverse Engineering

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5th international conference on learning and Collaboration Technologies (LCT) Held as Part of 20th international conference on Human-Computer Interaction (HCI international)

作者： Lepik, Toomas Maennel, Kaie Ernits, Margus Maennel, Olaf Tallinn Univ Technol Tallinn Estonia

ISBN: (纸本)9783319911526;9783319911519

the threat environment is rapidly changing and the cyber security skill shortage is a widely acknowledged problem. However, teaching such skills and keeping professionals up-to-date is not trivial. New malware types appear daily, and it requires significant time and effort by a teacher to prepare a unique, current and challenging courses in the malware reverse engineering. Novel teaching methods and tools are required. this paper describes an experience with an automated hands-on learning environment in a malware reverse engineering class taught at Tallinn University of Technology in Estonia. Our hands-on practical lab is using a fully automated Cyber Defense Competition platform intelligent Training Exercise Environment (i-tee) [1] combined with typical Capture-the-Flag competition structure and open-source tools where possible. We describe the process of generating a unique and comparable reverse-engineering challenge and measuring the students' progress through the process of analysis, reporting flags and debugging data, recording and taking into account their unique approach to the task. We aim to measure the students' using the Bloom's taxonomy, i.e., mastering the art of malware reverse engineering at the higher cognitive levels. the presented teaching and assessment method builds foundation for enhancing the future malware reverse engineering training quality and impact.

关键词： Higher education teaching Cyber defence exercises Malware reverse engineering

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PCA Kernel Based Extreme learning Machine Model for Detection of NS1 from Salivary SERS Spectra 11th

PCA Kernel Based Extreme Learning Machine Model for Detectio...

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11th Asian conference on intelligent Information and database Systems (ACIIDS)

作者： Othman, Nur Hainani Lee, Khuan Y. Radzol, Afaf Rozan Mohd Mansor, Wahidah Zulkimi, N. A. Z. M. Univ Teknol MARA Fac Elect Engn Shah Alam Selangor Malaysia Univ Teknol MARA Pharmaceut & Lifesci Communities Res Computat Intelligence Detect RIG Shah Alam Selangor Malaysia

ISBN: (纸本)9783030148027

Use of NS1 as a biomarker in saliva has led to the non-invasive and early detection of Flaviviridae related diseases. Saliva is preferred as medium of detection because of its advantages such as non-invasive, painless and easy to collect. Work here intends to compare the performance of KELM classifier with linear and RBF kernels for classification of NS1 from salivary SERS spectra. Prior to KELM, PCA with different termination criteria (Cattle Scree test, CPV and EOC) are used to extract important features and reduce the dimension of SERS spectra dataset. Regularization coefficient (C-value) for linear kernel and Regularization coefficient (C-value) and gamma-value for RBF kernel are varied to find the optimum KELM classifier model. For linear kernel, 100% accuracy, precision, sensitivity, specificity is achieved for Linear model with EOC criterion and C-value set to 0.1, 0.2, 0.5, 1 and 2. For RBF kernel, 100% performance of accuracy, precision, sensitivity and specificity is achieved with RBF model with EOC criterion and values of 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8 and 1. the C-value is fixed to 1. the best Kappa value of 1 is obtained when all performance indicators scored 100%. For both Linear-KELM and RBF-KELM, EOC termination criterion gives the highest performance. It also observed that KELM classifier is data dependent.

关键词： NS1 Saliva SERS PCA KELM Linear kernel and RBF kernel

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OFViser: An Interactive Visual System for Spatiotemporal Analysis of Ocean Front

OFViser: An Interactive Visual System for Spatiotemporal Ana...

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international conference on Awareness Science and Technology (iCAST)

作者： Jian Song Cui Xie Junyu Dong College of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9781728191195

ISBN: (纸本)9781728191201

the ocean temperature front is a narrow transition zone where the temperature changes dramatically, which can be described by gradient of temperature. the temporal and spatial variations and the patterns of ocean fronts are of great concern to researchers by tedious observation and comparison of many spatial distribution maps of ocean front at different moments. However, a particular number of spatial states may only reflect certain spatial or temporal aspects of ocean front. this study designed a collaborative interactive visualization system to simultaneously integrate temporal and spatial analysis of ocean fronts with experts' knowledge, obtaining higher analysis efficiency and more comprehensiveness. the interactive statistical charts facilitate focus + context selection of points of interest in time and space, while the interactive Map-View and Map-Gallery support spatial analysis from overview to details. Moreover, this paper uses an unsupervised learning model named Self-Organizing Mapping network (SOM) to conduct spatio-temporal cluster analysis on different ocean fronts near the China Sea. the clustering results can be customized by user's colors specification, evaluated and interactively adjusted by researchers' knowledge. the spatio-temporal patterns of clustering result can easily mined by collaborative linkage of multi-graphs including unified distance matrix (U-Matrix), component plane, feature parallel coordinates plot, Map-View and other charts. the effectiveness and usability of the proposed system are demonstrated with two case studies.

关键词： Oceans data visualization Visualization Ocean temperature Self-organizing feature maps Temperature distribution Collaboration

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A Transient Stability Prediction Method based on Multi-Channel Convolutional Neural Networks Using Time Series of PMU Measurements

A Transient Stability Prediction Method based on Multi-Chann...

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international conference on Electrical and Electronics engineering, ELECO

作者： Nazanin Moarref Sevda Jafarzadeh Yusuf Yaslan V. M. Istemihan Genc Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical Engineering Istanbul Technical University Istanbul Turkey

Real-time transient stability assessment (TSA) is an important task which ensures the stability, and therefore, enhances the reliability of power systems. Various types of learning-based methodologies in which machine learning and deep learning algorithms are adopted for real-time TSA exist in literature. Convolutional neural network (CNN) is a deep-learning-based method which mostly demonstrates high performance for image classification. However, employing the conventional structure of CNN classifier for time series data may result in high computational complexity or low prediction accuracy. In this paper, a novel methodology is proposed for real-time stability prediction of a power system in which voltage angle measurements obtained from PMUs are utilized to train a multichannel deep CNN (MC-DCNN), which is a modified version of CNN classifier and appropriate for multivariate time series data. To evaluate the performance of the proposed method for real-time transient stability prediction, it is applied to the 127-bus WSCC test system.

关键词： Power system stability Stability analysis Feature extraction Time series analysis Voltage measurement Real-time systems Power system transients

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Development of a Preliminary Model Guide for Using Mobile learning Technology in Resource - Limited Primary Schools in thailand 11

Development of a Preliminary Model Guide for Using Mobile Le...

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11th international conference on Developments in eSystems engineering (DeSE)

作者： Trongtortam, Suparawadee Taylor, Mark Symons, Andrew Francis, Hulya Sudirman, Sud Liverpool John Moores Univ Dept Comp Sci Liverpool L3 3AF Merseyside England

ISBN: (纸本)9781538667125

this paper examines a preliminary model guide for planning the use of Mobile learning Technologies (MLT) in support of primary school teaching practice and delivery. A Soft Systems Methodology (SSM) approach is used in this study. the use of the SSM approach is aimed at allowing the analysis of participants' viewpoints to direct future developments in this area. Qualitative data has been collected through semi-structured interviews. the study sampled fifteen (15) primary schools from various urban and rural parts of the Phitsanulok Province in thailand. the group of respondents consisted of seventeen (17) teachers and ten (10) school administrators, who were asked their opinion of and attitude towards the proposed use of Mobile learning Technology (MLT) at their school. the results of this study have enabled the development of a model guide to facilitate the use of mobile learning technology to support and extend the reach of teaching in primary schools in thailand.

关键词： Mobile learning technology Soft System Methodology teaching and learning

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ALoT: A Time-Series Similarity Measure Based on Alignment of Textures 19th

ALoT: A Time-Series Similarity Measure Based on Alignment of...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Ogul, Hasan Ostfold Univ Coll Fac Comp Sci POB 700 N-1757 Halden Norway

ISBN: (纸本)9783030034931;9783030034924

Inferring the similarity between two time-series signals is a key step in several data analysis tasks for a variety of engineering applications. In this paper, we introduce a novel elastic similarity measure (ALoT) based on the alignment of textures, instead of observed values, extracted from input time-series signals. To obtain the texture information, Local Binary Patterns are adapted for one-dimensional signals. According to experiments performed on a large number of benchmark time-series classification datasets, the proposed method achieves higher accuracy than current pairwise similarity measures for several cases in a 1-Nearest Neighbor classification setup.

关键词： Time-series analysis Similarity measure Classification Sensory data

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