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检索条件"任意字段=2014 IEEE Symposium on Computational Intelligence in Big Data, CIBD 2014"

共 248 条记录，以下是81-90 订阅

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Robustness Threshold Methodology for Multicriteria based Ranking using Imprecise data

Robustness Threshold Methodology for Multicriteria based Ran...

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2014 ieee symposium on computational intelligence in Multi-Criteria Decision-Making (MCDM)

作者： Rizzon, Bastien Galichet, Sylvie Cliville, Vincent Univ Savoie LISTIC Annecy France

ISBN: (纸本)9781479944675

It is well established that making decisions from defined data according to various criteria requires the use of MultiCriteria Decision Aiding or Analysis (MCDA) methods. However the necessary input data for these approaches are often ill-known especially when the data are a priori estimated. The common MCDA approaches consider these data as singular/scalar values. This paper deals with the consideration of more realistic, values by studying the impact of imprecision on a classical "precise" ranking established with ACUTA, a method based on additive utilities. We propose a generic approach to establish the concordance of pairwise relations of preference despite interval-based imprecision by complementing ACUTA with a computation of Kendall's index of concordance and of a threshold for maintaining this concordance. The methodology is applied to an industrial case subjected to Sustainable Development problems.

关键词： data analysis decision making information retrieval Kendall concordance index MCDA method imprecise data multicriteria based ranking multicriteria decision aiding method multicriteria decision analysis method pairwise preference relations robustness threshold methodology sustainable development Additives computational modeling Correlation data models Electronic mail Robustness Sustainable development Sustainable Development electronic mail data models computational modeling information retrieval Additives decision making Robustness data analysis

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Feature Transformation-Based computational Model and Tools for Heterogeneous Meta Program Design 15

Feature Transformation-Based Computational Model and Tools f...

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15th ieee International symposium on computational intelligence and Informatics

作者： Stuikys, Vytautas Bespalova, Kristina Burbaite, Renata Kaunas Univ Technol Studentu 50 LT-51368 Kaunas Lithuania

ISBN: (纸本)9781479953370

We propose an approach and tools for designing heterogeneous meta-program (MP) through feature model (FM) transformations. Tools implement the Abstract State Machine (ASM) computational model. Firstly, to map the problem domain FM onto the solution domain FM, we use FAMILIAR and SPLOT tools. Next, to perform Model-to MP transformations, we use newly developed tools. We present the process-based framework and background of the approach along with a case study, experimental validation and evaluation. The main contribution of the paper is the ASM-based transformation model, its implementation algorithm to describe the functioning of the developed tools.

关键词： finite state machines metacomputing program diagnostics source code (software) specification languages ASM-based transformation model FAMILIAR tool FM transformation SPLOT tool feature model transformation feature transformation-based computational model heterogeneous meta-program design model-to-MP transformation process-based framework state machine Abstracts Adaptation models Cognition computational modeling data models Engines Frequency modulation

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Algorithm for Graph Building Based on Google Maps and Google Earth 15

Algorithm for Graph Building Based on <i>Google Maps</i> and...

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15th ieee International symposium on computational intelligence and Informatics

作者： Toman, J. Olszewska, J. I. Univ Gloucestershire Cheltenham GL50 2RH Glos England

ISBN: (纸本)9781479953370

This paper proposes a graph building algorithm that transforms geo-location data into a network graph, which could be useful for applications involving navigation aids or robotics. At first, our new method takes data from source Keynote Markup Language 2.2 format (KML 2.2) files such as extracted from Google Maps. Then, our algorithm creates a directed, acyclical graph which is displayed and overlayed afterwards in Google Earth. A Java application has been built to implement and test the system based on the graph building algorithm for the analysis of ski piste maps. Testing of the graph, in context of ski resorts, demonstrates that the algorithm is accurate and suitable for use in Geographical Information Systems (GIS). Moreover, the performance of the algorithm has been analysed. It shows that, with the size of the network graphs produced and the available hardware, our algorithm is computationally efficient.

关键词： Java directed graphs geographic information systems navigation GIS Google Earth Google Maps Java application KML 2.2 files Keynote Markup Language 2.2 format files directed acyclical graph geo-location data transformation geographical information systems graph building algorithm graph testing navigation aids network graph size robotics ski piste map analysis ski resort Algorithm design and analysis Buildings computational intelligence Earth Google Testing geographic information systems Java application Java Java programming language Algorithm design and analysis Google Buildings Artificial intelligence computational intelligence robotics directed graphs topsoil Earth NAVIGATION AIDS navigation

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A Google Approach for computational intelligence in big data

A Google Approach for Computational Intelligence in Big Data

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International Joint Conference on Neural Networks (IJCNN)

作者： Antoniades, Andreas Took, Clive Cheong Univ Surrey Dept Comp Guildford GU2 5XH Surrey England

ISBN: (纸本)9781479914845

With the advent of the emerging field of big data, it is becoming increasingly important to equip machine learning algorithms to cope with volume, variety, and velocity of data. In this work, we employ the MapReduce paradigm to address these issues as an enabling technology for the well-known support vector machine to perform distributed classification of skin segmentation. An open source implementation of MapReduce called Hadoop offers a streaming facility, which allows us to focus on the computational intelligence problem at hand, instead of focusing on the implementation of the learning algorithm. This is the first time that support vector machine has been proposed to operate in a distributed fashion as it is, circumventing the need for long and tedious mathematical derivations. This highlights the main advantages of MapReduce - its generality and distributed computation for machine learning with minimum effort. Simulation results demonstrate the efficacy of MapReduce when distributed classification is performed even when only two machines are involved, and we highlight some of the intricacies of MapReduce in the context of big data.

关键词： MapReduce

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Energy efficient handling of big data in embedded, wireless sensor networks

Energy efficient handling of big data in embedded, wireless ...

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9th ieee Sensors Applications symposium (SAS)

作者： Bergelt, Rene Vodel, Matthias Hardt, Wolfram Tech Univ Chemnitz Chair Comp Engn D-09111 Chemnitz Germany

ISBN: (纸本)9781479921805

point where each individual node of a network may store and deliver a massive amount of (sensor-based) information at once or over time. In the future, massively connected, highly dynamic wireless sensor networks such as vehicle-2-vehicle communication scenarios may hold an even greater information potential. This is mostly due to the increase in node complexity. Consequently, data volumes will become a problem for traditional data aggregation strategies traffic-wise as well as with regard to energy efficiency. Therefore, in this paper we suggest to call such scenarios big data scenarios as they pose similar questions and problems as traditional big data scenarios. Although the latter focus mostly on business intelligence problems. We then propose an aggregation strategy tied to technological prerequisites which enables the efficient use of energy and the handling of large data volumes. Furthermore, we demonstrate the energy conservation potential based on experiments with actual sensor platforms.

关键词： energy conservation intelligent sensors wireless sensor networks aggregation strategy big data scenarios business intelligence problems energy conservation wireless sensor networks data handling data storage systems Energy conservation Information management Routing Temperature sensors Wireless sensor networks

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Application of Fuzzy Inference Engine as an Automatic Switch between ensembles of Clustering Methods 7

Application of Fuzzy Inference Engine as an Automatic Switch...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International symposium on Advanced Intelligent Systems (ISIS)

作者： Zolkepli, Maslina Dong, Fangyan Hirota, Kaoru Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Yokohama Kanagawa Japan

ISBN: (纸本)9781479959556

An automatic switch between ensembles of clustering algorithms is proposed as a part of the Bibliographic big data Retrieval System by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy c-means clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O(n3) to O(n). The automatic switch is developed a fuzzy logic controller written in Java and the experimental results demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 7 out of 9 cases with the highest percentage of 90.50%, completed in 161 seconds and the individual clustering algorithms were selected once each with the Newman- Girvan algorithm at 84.85% in 152 seconds and the self- adapted fuzzy cmeans at 67.54% in 259 seconds. The automatic switch is to be incorporated into the Bibliographic big data Retrieval System that focuses on visualization of fuzzy relationship using hybrid approach combining fuzzy c-means and Newman-Girvan algorithm, planning to be released to the public through the Internet.

关键词： fuzzy inference fuzzy control language bibliographic big data visualization jFuzzyLogic

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Evolving maximum likelihood clustering algorithm

Evolving maximum likelihood clustering algorithm

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2014 ieee symposium on Evolving and Autonomous Learning Systems, EALS 2014

作者： Filho, Orlando Donato Rocha De Oliveira Serra, Ginalber Luiz Federal Institute of Education Science and Technology Department of Electroelectronics Laboratory of Computational Intelligence Applied to Techonology São Luís-MA Brazil

ISBN: (纸本)9781479944958

This paper proposes an online evolving fuzzy clustering algorithm based on maximum likelihood estimator. In this methodology, the distance from a point to center of the cluster is computed by maximum likelihood similarity of data. The mathematical formulation is developed from the Takagi-Sugeno (TS) fuzzy inference system. The performance and application of the proposed methodology is based on prediction of the Box-Jenkins (Gas Furnace) time series. computational results of a comparative analysis with other methods widely cited in the literature illustrates the effectiveness of the proposed methodology. © 2014 ieee.

关键词： Fuzzy inference

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An Optimization for Amazons Search and Evaluation 7

An Optimization for Amazons Search and Evaluation

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7th International symposium on computational intelligence and Design (ISCID)

作者： Zhong, Runxing Zhou, Ke Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Univ Sci & Technol Beijing Sch Adv Engn Beijing Peoples R China

ISBN: (纸本)9781479970056

Computer chess is a computer program which plays chess by itself without human intervention. With the development of related fields of study, the capability of computer chess has improved greatly since its emergence. The Game of the Amazons is a new and challenging game in computer chess. Amazons has attracted attention in the game programming because of its difficulty. In this paper, we implemented and optimized an Amazons playing program that consists of four components, including View, data, AI, and Operation. These components facilitate the debugging and optimizing process. It has a clear logical structure that simulates Amazons playing and implements an optimized alpha-beta pruning search algorithm. An optimization is made to improve the get-moves method, and a new evaluation factor is proposed and added to the evaluation function to further enhance its artificial intelligence capability. Then we conducted experiments to test its performance. The optimization is proved to be effective and helps the program to defeat some strong Amazons-playing programs.

关键词： artificial intelligence Amazons search algorithm evaluation function optimization

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Three-Term Relation Neuro-Fuzzy Cognitive Maps 15

Three-Term Relation Neuro-Fuzzy Cognitive Maps

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15th ieee International symposium on computational intelligence and Informatics

作者： Puheim, Michal Vascak, Jan Madarasz, Ladislav Tech Univ Kosice Dept Cybernet & Artificial Intelligence Letna 9 Kosice 04200 Slovakia

ISBN: (纸本)9781479953370

In this paper, we propose a novel approach to modeling using fuzzy cognitive maps, which we refer to as the Three-Term Relation Neuro-Fuzzy Cognitive Map or simply the TTR NFCM. The proposed method is mostly suited to model complex nonlinear technical systems with dynamic internal characteristics. With this method we aim to solve some of the most critical problems of the conventional fuzzy cognitive maps. We target two of these problems by hybridization with artificial neural networks. First of them is a linear nature of relations between the concepts. The second is a lack of mutual dependence between the relations connecting to the same concept. Finally, we tackle a problem of relation dynamics using an inspiration from the control engineering. While focusing on bringing these advanced additional methods to the design of cognitive maps, we also aim to keep the degree of dependency on expert knowledge on the same level as with the conventional fuzzy cognitive maps. We achieve this by utilizing the machine learning methods. However, since the proposed method is heavily dependent on automated data-driven learning, it is suitable mainly for systems which are well observable and can produce sufficient training datasets.

关键词： fuzzy cognitive map artificial neural network PID control modelling complex systems concept relations

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Towards a Personal Health Records System for Patients with Autism Spectrum Disorders 2

Towards a Personal Health Records System for Patients with A...

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ieee symposium on computational intelligence in Healthcare and e-health

作者： Coronato, Antonio Paragliola, Giovanni Natl Res Council CNR Inst High Performance Comp & Networking ICAR Rome Italy

ISBN: (纸本)9781479953752

Patients with Autism Spectrum Disorders ( ASD) show symptoms that in general fall into three areas: 1) social impairment;2) communication difficulties;and, 3) repetitive and stereotyped behaviors. The growing of people affected by such as diseases increases the need of technologies able to help better clinicians in the medical treatment. In this paper we present the designing and the developing of a Personal Health Records ( PHR) system to assist clinicians and caregivers in the analyzing of clinical data and monitoring of anomalous gestures of patients with autism diseases. The detecting of anomalous gesture is made by using both Artificial intelligence ( AI) techniques and a framework based on formal methods. The research activity has been conducted in cooperation with clinicians of the Department of Child Psychiatry at Children's Hospital Santobono-Pausilipon in Naples.

关键词： Artificial intelligence Autism Spectrum Disorders Monitoring System

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