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检索条件"丛书名=Advances in Intelligent Systems and Computing"

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Collection of Individual Packet Statistical Information in a Flow Based on P4-switch

Collection of Individual Packet Statistical Information in a...

引用

International Symposium on Computer Science, Digital Economy and intelligent systems (CSDEIS)

作者： Mankov, Vladimir A. Krasnova, Irina A. Training Ctr Nokia 8AAviamotornaya Moscow 111024 Russia Moscow Tech Univ Commun & Informat 8AAviamotornaya Moscow 111024 Russia

ISBN: (纸本)9783030392161;9783030392154

The paper addresses solutions based on machine learning methods to problems emerging at the withdrawal of statistical information about flows for real time classification of traffic in SDN networks. The solution is based on P4 switch with a specially designed for it system of memory organization allowing to store only the essential data about each flow per packet. Three ways of flow identification were discovered during the research. The method allows to avoid additional load both on the network between switches and SDN controller and the controller itself thanks to preliminary real time processing of data at the switch. The approach allows to avoid extension of delay of the controller response in the process of traffic flows classification. The theoretical foundation received experimental validation. Methodology is developed for defining optimal parameters of the switch. Optimal parameters for the switch planned for further research have been defined experimentally.

关键词： P4-switch SDN Packet monitoring Flows identification

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A Preliminary Study on Multivariate Time Series Clustering 14th

A Preliminary Study on Multivariate Time Series Clustering

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14th International Conference on Soft computing Models in Industrial and Environmental Applications (SOCO)

作者： Vaquez, Iago Villar, Jose R. Sedano, Javier Simic, Svetlana Inst Tecnol Castilla & Leon Burgos 09001 Spain Univ Oviedo Comp Sci Dept EIMEM Oviedo Spain Univ Novi Sad Clin Ctr Vojvodina Novi Sad Dept Neurol Novi Sad Serbia

ISBN: (纸本)9783030200558;9783030200541

Time Series (TS) clustering is one of the most effervescent research fields due to the Big Data and the IoT explosion. The problem gets more challenging if we consider the multivariate TS. In the field of Business and Management, multivariate TS are becoming more and more interesting as they allow to match events the co-occur in time but that is hardly noticeable. In this study, Recurrent Neural Networks and transfer learning have been used to analyze each example, measuring similarities between variables. All the results are finally aggregated to create an adjacency matrix that allows extracting the groups. Proof-of-concept experimentation has been included, showing that the solution might be valid after several improvements.

关键词： Recurrent neural networks

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Novel Approach of Intrusion Detection Classification Deeplearning Using SVM 1st

Novel Approach of Intrusion Detection Classification Deeplea...

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1st International Conference on Sustainable Technologies for Computational Intelligence (ICTSCI)

作者： Nagar, Pritesh Menaria, Hemant Kumar Tiwari, Manish Geetanjali Inst Tech Studies Udaipur Rajasthan India

ISBN: (纸本)9789811500299;9789811500282

The main objective of intrusion detection systems (IDS) is to discover the dynamic and the virulent form of network traffic that simply changes according to the characteristics of the network. The IDS methodology represents a prominent developing area in the field of computer network technology and its security. Different form of IDS has been developed working on distinctive approaches. One such kind of approach where it is used is the machine learning mechanism. In the proposed methodology, an experiment is applied on the data set named as KDD-99, including its subclasses such as denial of service (DOS), other types of attacks and the class without any form of attack. Depending upon the machine learning algorithms various distinct forms of IDS have been developed which further checks the optimization-based potential features in connectionwith the neural network classifier for the various forms of IDS-based attacks. This approach provides a comparative study between theANNand the optimizer-basedANNtechnology. The experimental analysis shows the convolution neural network with SVM show effective analysis providing accurate forms of IDS, thereby improving its detection based on individual class along with maintaining its results fundamentally.

关键词： Intrusion detection system Denial of service Artificial neural network

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Prediction of Human Maximum Forces - A Comparison of Four Approaches to Calculate Muscle-Torque

Prediction of Human Maximum Forces - A Comparison of Four Ap...

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AHFE International Conference on Additive Manufacturing, Modeling systems and 3D Prototyping

作者： Kaiser, Andre Bullinger, Angelika C. Tech Univ Chemnitz Chair Ergon & Innovat Erfenschlager Str 73 D-09125 Chemnitz Germany

ISBN: (纸本)9783030202163;9783030202156

Analyses of human action forces is a standard function of current digital human models. However, research has been indicating that this feature needs to be improved as it lacks accuracy. Muscle-torque based modeling can be used to calculate action forces. Because it is not efficient to measure torques in all spatial directions, different calculating approaches have been developed. The question is which one is the best to calculate action forces? In this paper, we will therefor explain four different calculations - a spherical, linear, independent and fluid approach. To compare their quality, a study based on 366 measurements of maximum torques has been conducted. Results show that the spherical approach predicts maximum muscle torques to about 1.7% accuracy. The fluid approach increases the accuracy significantly (R-2 0.9), but needs more input data. Hence, a mix using both approaches is proposed as the best solution to calculate action forces in digital human models.

关键词： Digital human modeling Force analyses Joint torque Muscle-torque MVIC Maximum-torque-bodies

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Critical Success Factors for Implementing Artificial Intelligence (AI) Projects in Dubai Government United Arab Emirates (UAE) Health Sector: Applying the Extended Technology Acceptance Model (TAM) 5th

Critical Success Factors for Implementing Artificial Intelli...

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5th International Conference on Advanced intelligent systems and Informatics (AISI)

作者： Alhashmi, Shaikha F. S. Salloum, Said A. Abdallah, Sherief British Univ Dubai Fac Engn & IT Dubai U Arab Emirates Univ Fujairah Fac Engn & IT Fujairah U Arab Emirates

ISBN: (纸本)9783030311292;9783030311285

Recently, the government of United Arab of Emirates (UAE) is focusing on Artificial Intelligence (AI) strategy for future projects that will serve various sectors. Health care sector is one of the significant sectors they are focusing on and the planned (AI) projects of it is aiming to minimize chronic and early prediction of dangerous diseases affecting human beings. Nevertheless, project success depends on the adoption and acceptance by the physicians, nurses, decision makers and patients. The main purpose of this paper is to explore out the critical success factors assist in implementing artificial intelligence projects in the health sector. Besides, the founded gap for this topic was explored as there is no enough sharing of multiple success factors that assist in implementing artificial intelligence projects in the health sector precisely. A modified proposed model for this research was developed by using the extended TAM model and the most widely used factors. Data of this study was collected through survey from employees working in the health and IT sectors in UAE and total number of participants is 53 employees. The outcome of this questionnaire illustrated that managerial, organizational, operational and IT infrastructure factors have a positive impact on (AI) projects perceived ease of use and perceived usefulness.

关键词： United Arab Emirates Critical Success Factor (CSF) Technology Acceptance Model (TAM) Artificial Intelligence (AI) Health sector project

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Research on the Application of the Computer Big Data Technology in the Urban Power Energy Conservation

Research on the Application of the Computer Big Data Technol...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Shen, Jieran Zhao, Baocai Liu, Rui Jiao, Feng Univ Calif San Diego La Jolla CA 92093 USA State Grid Liaoning Elect Power Supply Co Ltd Shenyang 110000 Liaoning Peoples R China Beijing Bochao Times Software Co Ltd Beijing 100001 Peoples R China

ISBN: (纸本)9783030152352;9783030152345

With the arrival of the era of the big data, the power energy conservation has attracted much attention. The smart grid technology is a deep integration of the traditional power technology and the information, and the control and automation technology. By collecting a large amount of the data in the power generation, transmission, distribution, the power consumption and dispatching, marketing and other links, we can conduct the in-depth analysis and mining of the big data information to guide the decision-making and optimization in all links, in order to improve the efficiency of the power enterprises, enhance the stability of the power grid operation, better meet the needs of the power customers, and further ensure the efficiency of the urban power energy conservation.

关键词： Computer big data Urban power Energy saving Application

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On Stability of Feature Selection Based on MALDI Mass Spectrometry Imaging Data and Simulated Biopsy 21st

On Stability of Feature Selection Based on MALDI Mass Spectr...

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21st Polish Conference on Biocybernetics and Biomedical Engineering (PCBBE)

作者： Wilk, Agata Gawin, Marta Fratczak, Katarzyna Widlak, Piotr Fujarewicz, Krzysztof Silesian Tech Univ Inst Automat Control Akad 16 PL-44100 Gliwice Poland Maria Sklodowska Curie Inst Oncol Ctr Gliwice Branch Wybrzeze Armii Krajowej 15 PL-44101 Gliwice Poland

ISBN: (纸本)9783030298852;9783030298845

In this work we analyse MALDI mass spectrometry imaging data for thyroid cancer samples. Such a data, containing information about spatial distribution of proteins/peptides, makes possible to make a virtual analysis how a technique of fine needle aspiration (FNA) biopsy, a routine diagnosis procedure for thyroid, influences the outcome i.e. a set of discriminative features between cancerous and normal tissue. We hypothesised that an impure dataset (consisting of normal cell contaminated cancer samples) would be beneficial in the terms of stable feature selection. We compared several methods of predictor selection on different datasets to perform an in-depth feature ranking stability analysis for thyroid cancer mass spectrometry data. Furthermore we examined the impact of sample contamination level on the selection.

关键词： MALDI imaging mass spectrometry Machine learning Feature selection Fine needle biopsy

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Construction of the Job Duration Distribution in Network Models for a Set of Fuzzy Expert Estimates 15th

Construction of the Job Duration Distribution in Network Mod...

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15th International Scientific Conference on Intellectual systems of Decision Making and Problems of Computational Intelligence (ISDMCI)

作者： Samokhvalov, Yuri Taras Shevchenko Natl Univ Kyiv Kiev Ukraine

ISBN: (纸本)9783030264741

This article presents the mechanisms for processing fuzzy expert estimates in network models for project management. The distribution of the probability function of the execution time is proposed. This function allows us to build a beta-distribution of a random variable over the entire domain of its definition for a combination of approximate points and interval expert estimates. This article describes also the approximation of linguistic estimates by using fuzzy triangular and trapezoidal numbers. It is based on the construction of the membership function of a fuzzy triangular number, accounting for its scale. The proposed approach makes it possible to obtain more accurate prediction estimates for making informed decisions during informational uncertainty.

关键词： Project management Duration of the work beta-distribution Expert estimates Fuzzy estimates Fuzzy numbers Membership function

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Blood Flow Under Mechanical Stimulations 12th

Blood Flow Under Mechanical Stimulations

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12th International Symposium of Computer Science in Sport (IACSS)

作者： Gamilov, Timur Simakov, Sergey Sechenov Univ 19S1 Moscow 119146 Russia Moscow Inst Phys & Technol 9 Inst Sky Per Dolgoprudnyi 141701 Russia

ISBN: (纸本)9783030350482;9783030350475

We propose a one-dimensional blood flow model taking into account muscle pump, external contraction and autoregulation. This model is used to study two effects: blood flow during running and the impact of enhanced external counterpulsation on the coronary blood flow on the basis of patient-specific data. On the basis of mathematical modelling we observe optimal stride frequency, which maximizes venous return.

关键词： Coronary blood flow Muscle pump Enhanced external counterpulsation Stride frequency

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Analytical Decision of Adaptive Estimation Task for Measurement Noise Covariance Matrix Based on Irregular Certain Observations 4th

Analytical Decision of Adaptive Estimation Task for Measurem...

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4th International Scientific Conference on intelligent Information Technologies for Industry (IITI)

作者： Sokolov, Sergey, V Sukhanov, Andrey, V Chub, Elena G. Manin, Alexander A. Rostov State Transport Univ Rostov Na Donu Russia JSC NIIAS Rostov Branch Rostov Na Donu Russia Moscow Tech Univ Commun & Informat Rostov Na Donu Russia

ISBN: (纸本)9783030500979

The problem of adaptive estimation for measurement noise covariance matrix in Kalman filter is analytically solved based on accurate observations obtained irregularly. The results of numerical modeling are provided. These results illustrate the key advantages of state vector stochastic estimation algorithm based on proposed approach in comparison to conventional one.

关键词： Irregular accurate observations Measurement noise covariance matrix Kalman filter Adaptive estimation

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