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Healthcare data Mining: Prediction Inpatient Length of Stay

Healthcare Data Mining: Prediction Inpatient Length of Stay

引用

International ieee Conference on intelligent systems, IS

作者： Peng Liu Lei Lei Junjie Yin Wei Zhang Wu Naijun Elia El-Darzi School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China Health Care Computing Group School of Computer Science University of Westminster London UK

data mining approaches have been widely applied in the field of healthcare. At the same time it is recognized that most healthcare datasets are full of missing values. In this paper we apply decision trees, Naive Bayesian classifiers and feature selection methods to a geriatric hospital dataset in order to predict inpatient length of stay, especially for the long stay patients

关键词： Medical services data mining Decision trees Classification tree analysis Hospitals Bayesian methods Robustness Learning systems Niobium compounds intelligent systems

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Merging Probabilistic & Null Values Utilising an Intuitionistic Fuzzy Relational Mediator

Merging Probabilistic & Null Values Utilising an Intuitionis...

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International ieee Conference on intelligent systems, IS

作者： Boyan Kolev Krassimir Atanassov Panagiotis Chountas Ilias Petrounias CLBME Bulgarian Academy of Sciences Sofia Bulgaria Health Care Computing Group-HSCS University of Westminster London UK Information Systems Group School of Informatics University of Manchester Institute of Science and Technology Manchester UK

An integrated data management system is required for representing and managing indicative information from multiple sources describing the state of an enterprise. Current data management systems model enterprises that are crisp. A crisp enterprise is one that is highly quantifiable; relationships are fixed and attributes are atomic valued. The premises for this paper are precise enterprises, data maybe uncertain; multiple sources of information do exist, but uncertainty may be described using different models

关键词： Merging Uncertainty Conference management Null value intelligent systems Fuzzy systems Information management Information resources Fuzzy logic Medical services

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An Incremental Learning Structure using Granular computing and Model Fusion With Application to Materials Processing

An Incremental Learning Structure using Granular Computing a...

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International ieee Conference on intelligent systems, IS

作者： George Panoutsos Mahdi Mahfouf Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper introduces a neural-fuzzy (NF) modeling structure for offline incremental learning. Using a hybrid model updating algorithm (supervised/unsupervised) this NF structure has the ability to adapt in an additive way to new input-output mappings and new classes. data granulation is utilised along with a NF structure to create a high performance yet transparent model that entails the core of the system. A model fusion approach is then employed to provide the incremental update of the system. The proposed system is tested against a multidimensional modeling environment consisting of a complex, non-linear and sparse database

关键词： Materials processing Noise measurement intelligent systems Competitive intelligence Neural networks Multidimensional systems databases Computational intelligence Maintenance engineering systems engineering and theory

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On the Advantages of Weighted L1-Norm Support Vector Learning for Unbalanced Binary Classification Problems

On the Advantages of Weighted L1-Norm Support Vector Learnin...

引用

International ieee Conference on intelligent systems, IS

作者： Tatjana Eitrich Bruno Lang Central Institute for Applied Mathematics Research Center Juelich GmbH Germany Applied Computer Science and Scientific Computing Group Department of Mathematics University of Wuppertal Germany

In this paper we analyze support vector machine classification using the soft margin approach that allows for errors and margin violations during the training stage. Two models for learning the separating hyperplane do exist. We study the behavior of the optimization algorithms in terms of training characteristics and test accuracy for unbalanced data sets. The main goal of our work is to compare the features of the resulting classification functions, which are mainly defined by the support vectors arising during the support vector machine training

关键词： Support vector machine classification Support vector machines Learning systems Kernel Testing Supervised learning Mathematics intelligent systems Machine learning Machine learning algorithms

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Deploying MIB data Mining for Proactive Network Management

Deploying MIB Data Mining for Proactive Network Management

引用

International ieee Conference on intelligent systems, IS

作者： P. G. Kulkarni S. I. McClean G. P. Parr M. M. Black School of Computing and Information Engineering University of Ulster Coleraine Northern Ire-land

Due to increasing reliance on computer communication networks, it is highly desirable that networks should have the ability to detect symptoms of oncoming exception conditions and take measures to prevent them thereby enabling a degree of proactive network management that underpins an acceptable quality of service. This paper proposes a framework for achieving congestion avoidance through proactive network management using data mining. It examines the inter-relationships between network element management information base (MIB) attributes, queue parameters (associated with a transmission link) and the level of congestion at a network node and identifies hybrid parameters that have a bearing on congestion. By employing data mining on the data pertaining to these variables, congestion at the network node can be predicted. Results from our initial experimentation with particular data mining models show that the accuracy achieved is as high as 98% in all of the cases thus rendering data mining a viable approach to proactively identity network exception conditions

关键词： data mining Computer network management Neural networks intelligent systems Conference management Quality management Communication networks Performance analysis Analytical models Computational intelligence

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Fast Kernel for Calculating Structural Information Similarities

Fast Kernel for Calculating Structural Information Similarit...

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International ieee Conference on intelligent systems, IS

作者： Jin-Mao Wei Shu-Qin Wang Jing Wang Jun-Ping You Institute of Computational Intelligence Northeast Normal University China Open Symbol Computation and Knowledge Engineering Laboratory of State Education Jilin University Changchun Jilin China School of Mathematics & Statistics Northeast Normal University Changchun Jilin China

Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights of different layer nodes and determining if some tree is a subtree of another tree. By defining a fast kernel and recursively computing SICs, we evaluate the structural information similarities of data trees to pattern trees. In the paper, we present the algorithm for calculating SICs with computation complexity of O(n), and use simple examples to instantiate the performance of the proposed method

关键词： Kernel Chemical compounds Silicon carbide XML Genetics Encoding Clustering algorithms intelligent systems intelligent structures Pattern analysis

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Evaluation of Adaptive computing Concepts for Classical ERP systems and Enterprise Services

Evaluation of Adaptive Computing Concepts for Classical ERP ...

引用

ieee International Conference on e-Commerce Technology (CEC)

作者： M. Wimmer V. Nicolescu D. Gmach M. Mohr A. Kemper H. Krcmar Technische Universität München Munich Germany

To ensure the operability and reliability of large scale enterprise resource planning systems (ERP), a peak-load oriented hardware sizing is often used. Better utilization can be achieved by employing an adaptive infrastructure based on smaller computational units in combination with an intelligent allocation management. The SAP University Competence Center (German SAP HCC) at the Technische Universitat Munchen provides support for 55 ERP training systems. The evaluation of the historical load data revealed that many applications exhibit cyclical resource consumption. In this paper we show the extraction of load patterns and present self-organizing controlling concepts in the context of the SAP HCC

关键词： Enterprise resource planning Hardware Sun data mining Quality of service Blades Large-scale systems Computational intelligence Management training Application software

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Integrative data Mining for Assessing International Conflict Events

Integrative Data Mining for Assessing International Conflict...

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International ieee Conference on intelligent systems, IS

作者： Francisco Azuaje Haiying Wang Huiru Zheng Chang Liu Hui Wang Ruth Rios-Morales Computer Science Research Institute University of Ulster Jordanstown UK School of Computing and Mathematics (SC&M) University of Ulster Jordanstown UK SC&M University of Ulster Jordanstown UK School of Computing and Mathematics University of Ulster Jordanstown UK School of Business Studies Trinity College Dublin Dublin Ireland

State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and the limitations exhibited by previous forecasting analyses led us to integrate different predictive resources and models through statistical analysis and machine learning. Here we demonstrate the predictive ability of unsupervised and supervised learning approaches to detecting meaningful relationships between country cases, encoded by several socio-economic indicators, and the emergence of violent conflicts. Two clustering-based analyses (Kohonen maps and a network-based approach) provided the basis for exploratory analyses that confirmed hypotheses about the relevance of the data and the differences between state failure types. We also illustrate the potential of a novel network-based clustering approach for sub-class discovery in the area of political instability analysis. Furthermore, we show significant relationships between the emergence of violent conflicts and a dataset of quantitative indicators of good governance, which allows the design of effective supervised and unsupervised classifiers. This study contributes to the development of intelligent data analysis techniques for supporting hypothesis generation and testing in international conflict analyses

关键词： data mining Failure analysis Availability Predictive models Statistical analysis Machine learning Supervised learning Self organizing feature maps data analysis Testing

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Applying Ant-based Multi-Agent systems to Query Routing in Distributed Environments

Applying Ant-based Multi-Agent Systems to Query Routing in D...

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International ieee Conference on intelligent systems, IS

作者： Elke Michlmayr Arno Pany Sabine Graf Women's Postgraduate College for Internet Technologies (WIT) Institute for Sof tware Technology and InteractiveSystems University of Technology Vienna Vienna Austria Institute for Sof tware Technology and InteractiveSystems University of Technology Vienna Vienna Austria

This paper presents SemAnt, a novel ant-based multi-agent system designed for distributed query routing. While the ant metaphor has been successfully applied to network routing both in wireless and fixed networks, little is yet known about its applicability to the task of query routing in distributed environments. We point out the similarities and dissimilarities between routing of data packets and routing of queries, and we present the design of SemAnt, which is based on the ant colony optimization meta-heuristic. For experimental evaluation, we deploy the algorithm in a peer-to-peer environment with a real-world application scenario and compare its performance against the well-known k-random walker approach. As we show, the benefits of SemAnt are that the routes for queries are optimized according to their popularity, and that the algorithm is highly suitable for volatile environments

关键词： Multiagent systems Query processing Routing Peer to peer computing Ant colony optimization Artificial intelligence intelligent systems Broadcasting Autonomous agents Algorithm design and analysis

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Web Intelligence, Business Intelligence and Decision Support systems: A Challenge for Fuzzy Logic and Soft computing

Web Intelligence, Business Intelligence and Decision Support...

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International ieee Conference on intelligent systems, IS

作者： Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences Warsaw Poland

Summary form only given. We present first some general remarks on challenges faced by modern information technology, notably when a human being is a relevant factor. These challenges are mainly related to inherent difficulties in solving some "meta-problems", in particular broadly perceived decision making. We assume, on the one hand, business intelligence related perspective, augmented with elements of Web intelligence, to fully use all available tools and resources. On the other hand, we assume a human centric computing perspective in the spirit of, for instance, Dertouzos's ideas. First, we present a brief account of modem approaches to real world decision making, emphasize the concept of a decision making process that involves more factors and aspects like: the use of own and external knowledge, involvement of various ,actors, aspects, etc., individual habitual domains, non-trivial rationality, different paradigms. As an example we mention Checkland's deliberative decision making (which is an important elements of his soft approach to systems analysis). After an analysis of specifics and difficulties encountered in many real world decision-making situations, we strongly advocate the use of computer based decision support systems. First, we briefly review the history of decision support systems, and then present a popular classification, starting from data driven to Web based and inter-organizational. We indicate that decision support systems should incorporate some sort of "intelligence", and we first briefly mention some views of what intelligence may mean in this concept, and then assume some more pragmatic, though limited, view of intelligent decision support systems. We indicate possible advantages of using elements of fuzzy logic and soft computing, notably, Zadeh's computing with words to be able to somehow merge the ideas presented like: human centric computing, decision making processes, intelligent decision support, etc. Finally, we present an example o

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