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Gene Expression Classification with a Novel Coevolutionary Based Learning Classifier System on Public Clouds

Gene Expression Classification with a Novel Coevolutionary B...

引用

IEEE International Conference on E-Science Workshops

作者： Christian Vecchiola Mani Abedini Michael Kirley Xingchen Chu Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory University of Melbourne Australia Department of Computer Science and Software Engineering University of Melbourne Australia

Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray data. In this paper, we present a coevolutionary learning classifier system based on feature set partitioning to classify gene expression data. A distributed implementation, which leverages Cloud computing technologies, is used to address the inherent computational costs of our model. The development and execution of this application was done using the Aneka middleware on the public Cloud (Amazon EC2) infrastructure. Experiments conducted using gene expression profiles demonstrates that the proposed implementation outperforms other well-known classifiers in terms of accuracy. Preliminary analysis into the impact of different Cloud setups on the performance of the classifier are also reported.

关键词： Cloud computing Gene expression Accuracy Machine learning Containers computer architecture

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Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients

Comparing performances of logistic regression, decision tree...

引用

computer Information systems and Industrial Management Applications (CISIM)

作者： Anchana Khemphila Veera Boonjing Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease. A retrospective analysis was performed in 303 subjects. We compared the performance of logistic regression(LR), decision trees(DTs), and Artificial neural networks (ANNs). The variables were medical profiles are age, Sex, Chest Pain Type, Blood Pressure, Cholesterol, Fasting Blood Sugar, Resting ECG, Maximum Heart Rate, Induced Angina, Ole Peak, Slope, Number Colored Vessels, Thal and Concept Class. We have created the model using logistic regression classifiers, artificial neural networks and decision trees that they are often used for classification problems. Performances of classification techniques were compared using lift chart and error rates. In the result, artificial neural networks have the greatest area between the model curve and the baseline curve. The error rates are 0.22, 0.198, 0.21, respectively for logistic regression, artificial neural networks and decision trees. The neural networks exhibited sensitivity of 81.1%, specificity of 78.7% and accuracy of 80.2%, while the decision tree provided the prediction performance with a sensitivity, specificity and accuracy of 81.7%, 76.0% and 79.3%. And the logistic regression provided the prediction performance with a sensitivity, specificity and accuracy of 81.2%,73.1% and 77.7% Artificial neural networks have the least of error rate and has the highest accuracy, therefore Artificial neural networks is the best technique to classify in this data set.

关键词： Artificial neural networks Heart Logistics Regression tree analysis Data models Data mining

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XML Path Matching for Different Hierarchy Order of Elements in XML Documents

XML Path Matching for Different Hierarchy Order of Elements ...

引用

ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Department of Mathematics and Computer Science Faculty of Science Software Systems Engineering Laboratory King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781424474226;9780769540887

XML documents allow document owners to create their documents in their own formats (element names and structure). The same information might be described in several ways. To find the similarity between XML documents which use different formats, one method is path similarity detection. The recently approach is PathSim. The previous approach can detect the similarity rate between two XML paths in case those two XML paths are in the same hierarchy order of elements that are semantic with each other. If XML documents use different elements hierarchy order, that approach has a low similarity rate. To improve the previous approach, PathMatch is introduced. PathMatch uses the edit distance algorithm to find the semantic similarity rate between element names and a cost matrix model to find the similarity rate between two XML paths. On the result, PathMatch has a higher similarity rate than the previous approach in case those two XML paths contain a different hierarchy order of elements that are semantic with each other. Moreover, in case two XML paths contain the same hierarchy order, PathMatch has the same similarity rate as the previous one.

关键词： XML Costs Change detection algorithms software engineering Artificial intelligence Distributed computing software systems systems engineering and theory Laboratories Mathematics

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A Three-Stage Method to Select Informative Genes from Gene Expression Data in Classifying Cancer Classes

A Three-Stage Method to Select Informative Genes from Gene E...

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International Conference on Intelligent systems, Modelling and Simulation (ISMS)

作者： Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Michifumi Yoshioka Department of Software Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

ISBN: (纸本)9781424459841;9780769539737

The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: (1) pre-selecting genes using a filter method; (2) optimizing the gene subset using a multi-objective hybrid method; (3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.

关键词： Gene expression Cancer Filters Genetic algorithms Support vector machines Support vector machine classification Frequency Intelligent systems Computational modeling computer simulation

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XER: A Recommendation for XML Element

XER: A Recommendation for XML Element

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International Conference on Information Technology: New Generations (ITNG)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

The strong point of the XML is that it allows document owners to describe their documents in their own format (structure and element names). There might be the same information has been describe in various ways. When user wants to search some information from XML documents, user might not retrieve all related results because of the difference of elements (tag names). To retrieve all related results, user needs to have queries for all possible elements. XML Element Recommendation (XER) is an idea to make XML documents easier for searching. It receives XML document as an input and compares that input with XML documents from database. It compares word similarity and semantic of each element. For semantic similarity of each element, it checks those elements to find which element (word) should be used by using word ranking (most common words). The output is a recommendation for each element of input XML document.

关键词： XML Mobile communication Mobile ad hoc networks Routing protocols Ad hoc networks Earthquakes Network topology Motion analysis Vehicle dynamics Costs

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XML Document Recommendation by Using Case Based Reasoning

XML Document Recommendation by Using Case Based Reasoning

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781424474226;9780769540887

Case Based Reasoning (CBR) is the process of solving new problem based on the solutions of similar previous problems. XML is a markup language that allows document owners describe their data in their own formats. There might be the same information has been described in various ways. This makes searching those information from different formats difficult. CXDR (CBR for XML Document Recommendation) is an idea to make searching XML documents easier. CXDR applies CBR framework to collect the mostly use XML document and recommend it to its user. CBR in CXDR consists of two parts, cases and knowledge. The case is XML documents and the knowledge is an XML document recommendation. The recommendation from CXDR is the most use XML element names and structure.

关键词： XML Keyword search Information retrieval Markup languages Database languages software engineering Artificial intelligence Distributed computing software systems systems engineering and theory

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Energy-Aware Loop Parallelism Maximization for Multi-core DSP Architectures

Energy-Aware Loop Parallelism Maximization for Multi-core DS...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Meikang Qiu Jian-Wei Niu Laurence T. Yang Xiao Qin Senlin Zhang Bin Wang Department of Electrical and Computer Engineering University of Kentucky KY USA State Key Laboratory of Software Development Environment Beihang University Beijing China Department of Computer Science Saint Francis Xavier University NS Canada Department of Computer Science and Software Engineering Aubum University Auburn AL USA Department of Systems Science and Engineering University of Zhejiang Hangzhou China Department of Computer Science and Engineering Wright State University Dayton OH USA

With the advance of semiconductor, multi-core architecture is inevitable in today's embedded system design. Nested loops are usually the most critical part in multimedia and high performance DSP (Digital Signal Processing) systems. Hence, maximizing loop parallelism is an important issue to improve the performance of a modern compiler. This paper studies how to maximize the system performance with the consideration of energy reduction for applications with multidimensional nested loops on multi-core DSP architectures. An algorithm, EALPM (Energy-Aware Loop Parallelism Maximization), is proposed in this paper. We implemented a two phase strategy. First, the strategy uses retiming and loop transformation to parallelize nested loops. Then the strategy employs a novel voltage assignment algorithm to reduce total energy consumption. The experimental results show that on average, both performance and energy-saving can be significantly improved using the EALPM algorithm.

关键词： Parallel processing Energy consumption Voltage control Optimization Algorithm design and analysis computer architecture Digital signal processing

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Distributed diagnosis of complex systems using timed failure propagation graph models

Distributed diagnosis of complex systems using timed failure...

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International Automatic Testing Conference, AUTOTESTCON

作者： Nagabhushan Mahadevan Sherif Abdelwahed Abhishek Dubey Gabor Karsai Institute for Software Integrated Systems Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Mississippi State University Starkville MS USA

Timed failure propagation graph (TFPG) is a directed graph model that represents temporal progression of failure effects in physical systems. In this paper, a distributed diagnosis approach for complex systems is introduced based on the TFPG model settings. In this approach, the system is partitioned into a set of local subsystems each represented by a subgraph of the global system TFPG model. Information flow between subsystems is achieved through special input and output nodes. A high level diagnoser integrates the diagnosis results of the local subsystems using an abstract high level model to obtain a globally consistent diagnosis of the system.

关键词： Cognition Computational modeling Monitoring Semantics software Switches Synchronization

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A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

A Particle Swarm Optimization-Based Heuristic for Scheduling...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Suraj Pandey Linlin Wu Siddeswara Mayura Guru Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia CSIRO Tasmanian ICT Centre Hobart Australia

ISBN: (纸本)9781424466955;9780769540184

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.

关键词： Particle swarm optimization Processor scheduling Cloud computing Application software Cost function Computational efficiency Dynamic scheduling computer science software engineering Application virtualization

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I-superior approach to study the stability of logistic map

I-superior approach to study the stability of logistic map

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International Conference on Mechanical and Electrical Technology (ICMET)

作者： Mamta Rani Saurabh Goel Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia Research Scholar Department Of Computer Science Galgotias Institute of Management Technology Greater Noida India

Verlhust logistic map f(x) = r x (1 - x) is a widely studied and applicable model in discrete dynamical system. Its behavior has been, extensively, studied in Picard orbit and recently studied in superior orbit. Rani et. al. [22] and [25] has shown that in superior orbit, logistic map is stable for larger values of r than that of Picard orbit. In this paper, we have attempted to make the map stable for further larger values of r via I-superior iterates.

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