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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Feature-Extraction Methods for Lung-Nodule Detection: A Comparative Deep Learning Study 3

Feature-Extraction Methods for Lung-Nodule Detection: A Comp...

引用

3rd International Conference on intelligent systems and advanced computing Sciences, ISACS 2019

作者： Skourt, Brahim Ait Nikolov, Nikola S. Majda, Aicha Faculty of Sciences and Technology University of Sidi Mohammed Ben Abdellah Computer Science Department Fez Morocco University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9781728148137

Feature extraction has become a prerequisite step in computer vision problems, its importance resides in extracting significant hidden features from data, to help machine learning algorithms reach higher performance. Feature extraction techniques were behind the breakthrough in deep learning era, by providing relevant features. Deep learning architectures have overcome the state of the art in many different computer vision fields. In this work we are going to discuss and compare the accuracy of various global feature extraction methods, using deep learning for lung nodule detection. The experimental results show that feature extraction with convolutional neural networks (CNNs) outperforms the other methods including restricted boltzmann machines (RBMs). © 2019 ieee.

关键词： Feature extraction

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Delay-dependent robust H∞ control of discrete time fuzzy systems via delta operator approach 3

Delay-dependent robust H∞ control of discrete time fuzzy sy...

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3rd International Conference on intelligent computing in data Sciences, ICDS 2019

作者： Rachid, Hafsaa Hicham, El Aiss Houssaine, Tissir El Abdelaziz, Hmamed University Sidi Mohamed Ben Abdellah LESSI Department of Physics Faculty of Sciences Dhar El Mehraz B.P. 1796 Fes-Atlas Morocco

ISBN: (纸本)9781728100036

This paper addresses the problem of robust H∞ control in delta domain for a class of Takgi-Sugeno (TS) fuzzy systems with time delays. The main contributions of this work are to introduce some free weighting matrices and design an H∞ controller so that the proposed TS fuzzy system is robustly asymptotically stable with H∞ norm bound performance. Based on Lyapunov-krasovskii functionals, a new general stability theorem is provided for the delta operator TS fuzzy systems with time delays. Finally, numerical examples are given to demonstrate the effectiveness and potential of the proposed design technique. © 2019 ieee.

关键词： Time delay

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A Framework for Integrating Cloud computing and Big data Analytics into E-Governance Using Openstack Sahara 3rd

A Framework for Integrating Cloud Computing and Big Data Ana...

引用

3rd International Conference on Information and Communication Technology for intelligent systems (ICTIS)

作者： Jadhav, Bhushan Patankar, Archana B. Mumbai Univ Thadomal Shahani Engn Coll Mumbai Maharashtra India

ISBN: (纸本)9789811317477;9789811317460

The information and communication technologies (ICTs) have made significant development in its hardware, software, computing, storage, and communication technologies to leverage the modern-day computing. The E-governance uses ICT to deliver essential government services over the Internet. But due to the limited infrastructure, lack of funds, use of outdated technologies, the current E-governance solutions face many issues related to computing and data processing. The cloud computing is a recent advancement to traditional computing architectures which can overcome the challenges related to computing while big data analytics has emerged over the past data management technologies to solve the limitations related to data acquisition, storage, processing, and analytics. Therefore, the combination of cloud computing and big data analytics would be a welcome option in E-governance. The Openstack is an open-source cloud platform widely used inmost of the organizations because of its distinguished features over the existing computing architectures and having built-in support for big data analytics. The main objective of this paper is to propose a framework for E-governance based on Openstack that integrates cloud computing and big data analytics together to overcome the challenges in existing E-governance system.

关键词： E-governance Cloud computing Big data analytics

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A Survey on SCADA Security and Honeypot in Industrial Control System 3rd

A Survey on SCADA Security and Honeypot in Industrial Contro...

引用

3rd International Conference of Reliable Information and Communication Technology (IRICT)

作者： Lu, Kuan-Chu Liu, I-Hsien Sun, Meng-Wei Li, Jung-Shian Natl Cheng Kung Univ Inst Comp & Commun Engn Dept Elect Engn 1 Univ Rd Tainan 701 Taiwan

ISBN: (纸本)9783319990071;9783319990064

Nowadays, the infrastructure employs SCADA (supervisory control and data acquisition) system for monitoring, such as power plants, reservoir, manufacturing, and logistics industry. With the rapid development of the internet, IOT (internet of things) makes every massive infrastructure more convenient to be control and monitoring. However, the convenience of remote controlling to the infrastructure may cause serious damage which could affect people's livelihood. Consequently, an effective and protective mechanism to protect the SCADA system is necessary. We have investigated the related attacks and defensive methods. This paper surveys varying SCADA system and the honeypot system of SCADA. Then we proposed a defensive architecture with honeypot system to protect the SCADA system.

关键词： SCADA Infrastructure security Industrial control system Honeypot

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Feature Selection Techniques for Cancer Classification applied to Microarray data: A survey 3

Feature Selection Techniques for Cancer Classification appli...

引用

3rd International Conference on intelligent systems and advanced computing Sciences, ISACS 2019

作者： Qaraad, Mohammed Amjad, Souad Fathi, Hanaa Manhrawy, Ibrahim I.M. Faculty of Science Abdelmalek Essaadi University Department of Computer Science Tetouan Morocco Faculty of Science Menoufia University Department of Math and Computer Science Menoufia Egypt Modern Academy for Engineering and Technology Department of Basic Sciences Cairo Egypt

ISBN: (纸本)9781728148137

In multidimensional microarrays, data that collect gene expression profiles that fulfill the state of the cell at the molecular level. Feature selection and extraction have become an obvious need for the analysis of this microarray. There are many different methods for selecting and extracting attributes, and they are widely used. One of the serious tasks is to learn how to extract useful information from huge microarrays datasets with complex relationships between different genes. These methods are aimed at removing excess and irrelevant traits and extruding marker genes that effectively maintain classification accuracy. This report gives an overview of the various ways of performing dimensional reduction methods that were used in these microarrays to select important features and presents a comparison between them. The advantages and disadvantages of several methods are described in order to show an obvious idea of when to use each of them to save computational time and resources. © 2019 ieee.

关键词： Feature Selection

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Performance evaluation of machine learning based big data processing framework for prediction of heart disease 3

Performance evaluation of machine learning based big data pr...

引用

3rd International Conference on intelligent systems and advanced computing Sciences, ISACS 2019

作者： Ed-Daoudy, Abderrahmane Maalmi, Khalil LTTI Laboratory Higher School of Technology Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (纸本)9781728148137

Heart disease is one of the most prominent and dangerous diseases that threaten public health around the world, often leading to heart attacks and strokes. More recently, the amount of heart disease patients under supervision has been increasing significantly owing to lack of awareness and lifestyle related factors affecting health. There is therefore a need to ensure an effective and scalable solution to effectively find and prevent the heart disease within a short and very specific timeline. At this stage, the performance of four well-known classification algorithms;SVM, Decision Tree, Random Forest and Logistic Regression was evaluated for prediction of heart disease using Apache Spark, a fast and general engine for big data processing with its machine learning library, MLlib for batch data processing. The overall performance comparison was assessed in terms of prediction accuracy, building time and prediction time. Experimental results on processed cleveland data from heart disease dataset show that the highest classification accuracy of 87.50 % was reported using Random Forest with sensitivity and specificity of 86.67 and 88.37 %, respectively. On the other hand, the fast algorithm will be logistic regression. © 2019 ieee.

关键词： Big data

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Feasibility study of Grid connected Photovoltaic system for electricity generation with peak demand deficit 3

Feasibility study of Grid connected Photovoltaic system for ...

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3rd International Conference on intelligent computing in data Sciences, ICDS 2019

作者： Idrissi, Rajaa Naji El Ouassaid, Mohammed Maaroufi, Mohamed Mohammed V University Engineering for Smart and Sustainable Systems Research Center Mohammadia School of Engineers Rabat Morocco

ISBN: (纸本)9781728100036

Due to great interest in the development of renewable energy technologies in Morocco, this paper studied the performance of a solar photovoltaic grid-connected system located in Rabat, Morocco. Firstly a demand side management strategy has been developed to maximize energy efficiency of the system using a load shifting day-ahead approach. In the second part, an examination of resulted load requirement using a simulation model based on HOMER software is carried out. The analysis of the results has shown that the proposed model is an effective and feasible tool of electricity generation. © 2019 ieee.

关键词： Smart power grids

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Middle-Frequency Limited Model Reduction Techniques for T-S fuzzy discrete-time systems 3

Middle-Frequency Limited Model Reduction Techniques for T-S ...

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3rd International Conference on intelligent computing in data Sciences, ICDS 2019

作者： El-Amrani, Abderrahim Boukili, Bensalem Boumhidi, Ismail Hajjaji, Ahmed El Hmamed, Abdelaziz LESSI Faculty of Sciences Dhar El Mehraz B.P. 1796 Fes-Atlas Morocco MIS Lab University of Picardie Jules-Vernes Amiens80000 France

ISBN: (纸本)9781728100036

This paper deals with the problem of Middle-Frequency (MF) approach H-{\infty} model reduction for nonlinear discrete-time systems in the Takagi-Sugeno (T-S) fuzzy models. The objective is to propose a new design sufficient condition via linear matrix Inequality (LMIs) formulations, ensuring that the error system is stable and has a minimized H performance when frequency ranges of noises are known beforehand. Less conservative results are obtained by using the new finite frequency H performance index is firstly defined, Finsler's lemma and some independent matrices. Simulation example demonstrate the technique and its advantage. © 2019 ieee.

关键词： Linear matrix inequalities

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Joint 8th International Conference on Informatics, Electronics and Vision (ICIEV) / 3rd International Conference on Imaging, Vision and Pattern Recognition (icIVPR) / International Conference on Activity and Behavior computing (ABC)

作者： Arafat, Hossain Md Sagar, Didar Hossain Ahmed, Kawsar Paul, Bikash Kumar Rahman, Md Zamilur Habib, Md Ahsan Mawlana Bhashani Sci & Technol Univ Dept ICT Tangail 1902 Bangladesh Daffodil Int Univ Dept Software Engn SWE Dhaka Bangladesh Mawlana Bhashani Sci & Technol Univ Grp Biophotomatix Tangail 1902 Bangladesh

ISBN: (纸本)9781728107882

The objective of the research study is to predict the popularity of printed as well as online news articles that are publicized on the online social network. Keywords are extracted from the collected data sets and compared with trained data sets. The popularity of any news contents are calculated through the proposed prediction model according to training data sets and lifetime of that news. 100 newly published news items have collected and analyzed them. We have found an accuracy of 70% which is more than prior studies. Our proposed system will make a significant way for the user to explore the news articles.

关键词： Popularity prediction News item News sharing intelligent systems Social Media Response

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A Time Sequence data Processing Method Based on Random Matrix of Smart Electric Meter 3

A Time Sequence Data Processing Method Based on Random Matri...

引用

3rd ieee advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019

作者： Du, Jie Cheng, Yingying Zhang, Jiaming Li, Gang Zhou, Jianbo Tian, Zhihua State Grid Chongqing Electric Power Co. Electric Power Research Institute China

ISBN: (纸本)9781728105130

With the expansion of the scale of intelligent power distribution network and the complexity of power grid structure, the amount of data in power system increases rapidly, and the power equipment are faced with new challenges. Based on the principle of big data and data mining analysis, this paper proposes a method based on random matrix theory and clustering algorithm to evaluate the running state of electric energy meter. Specifically, time series data of various indicators is characterized by real-time separation window technology. Based on the random matrix theory, the random matrix based analysis model is constructed to calculate and analyze the statistical timing characteristics in real time. The proposed method has good robustness, reliability and timeliness, which provides a new idea for the application research of power grid detection technology. © 2019 ieee.

关键词： Big data

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