Data mining concerns theories,methodologies,and in particular,computer systems for knowledge extraction or mining from large amounts of *** rule mining is a general purpose rule discovery *** has been widely used for ...
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ISBN:
(纸本)9781612848396
Data mining concerns theories,methodologies,and in particular,computer systems for knowledge extraction or mining from large amounts of *** rule mining is a general purpose rule discovery *** has been widely used for discovering rules in medical *** diagnosis of diseases is a significant and tedious task in *** detection of heart disease from various factors or symptoms is an issue which is not free from false presumptions often accompanied by unpredictable *** the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable *** this paper,we presented an efficient approach for the prediction of heart attack risk levels from the heart disease ***, the heart disease database is clustered using the K-means clustering algorithm,which will extract the data relevant to heart attack from the *** approach allows mastering the number of fragments through its k parameter. Subsequently the frequent patterns are mined from the extracted data,relevant to heart disease,using the MAFIA (Maximal Frequent Itemset Algorithm)*** machinelearning algorithm is trained with the selected significant patterns for the effective prediction of heart attack. We have employed the ID3 algorithm as the training algorithm to show level of heart attack with the decision *** results showed that the designed prediction system is capable of predicting the heart attack effectively.
uCancer survival forecasting may be attempted using models constructed through predictive techniques of various kinds, including statistical multivariate regression and machinelearning. However, no single such techni...
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ISBN:
(纸本)9783642184710
uCancer survival forecasting may be attempted using models constructed through predictive techniques of various kinds, including statistical multivariate regression and machinelearning. However, no single such technique provides the best predictive performance in all cases. We present an automated meta-learning approach that learns to predict the best performing technique for each individual patient. The individually selected technique is then used to forecast survival for the given patient. We evaluate the proposed approach over a database of retrospective records of pancreatic cancer surgical resections.
Reconstructing the 3D facial model of an unidentified individual from his skull contributes considerable benefits to terms of archaeology, anthropology and forensic investigation but it is still significantly complica...
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Kernel methods are effective machinelearning techniques for many image based pattern recognition problems. Incorporating 3D information is useful in such applications. The optical profilometries and interforometric t...
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ISBN:
(纸本)9780819485830
Kernel methods are effective machinelearning techniques for many image based pattern recognition problems. Incorporating 3D information is useful in such applications. The optical profilometries and interforometric techniques provide 3D information in an implicit form. Typically phase unwrapping process, which is often hindered by the presence of noises, spots of low intensity modulation, and instability of the solutions, is applied to retrieve the proper depth information. In certain applications such as pattern recognition problems, the goal is to classify the 3D objects in the image, rather than to simply display or reconstruct them. In this paper we present a technique for constructing kernels on the measured data directly without explicit phase unwrapping. Such a kernel will naturally incorporate the 3D depth information and can be used to improve the systems involving 3D object analysis and classification.
Production scheduling and transportation strategies in manufacturing systems are two important topics in the fields of production or supply chain management. These two decisions are usually separately discussed in pre...
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ISBN:
(纸本)9783642200397
Production scheduling and transportation strategies in manufacturing systems are two important topics in the fields of production or supply chain management. These two decisions are usually separately discussed in previous works. However, since a business may simultaneously face these two problems, it needs to develop a model to simultaneously deal with these two problems so as to minimize the total costs. This paper deals with a production scheduling and air-transportation problem with a single-machine and multi-delivery destinations. A heuristic approach is developed to deal with this problem. Computational results show that the heuristic approach can produce nearly optimal solutions for small-scale problem and can produce feasible solutions that commercial optimization software, Lingo, can not solve within a reasonable amount of time.
Computer Go presents a challenging problem for machinelearning agents. With the number of possible board states estimated to be larger than the number of hydrogen atoms in the universe, learning effective policies or...
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The proceedings contain 30 papers. The special focus in this conference is on Tools for Teaching Logic. The topics include: Panda: A Proof Assistant in Natural Deduction for All. A Gentzen Style Proof Assistant for Un...
ISBN:
(纸本)9783642213496
The proceedings contain 30 papers. The special focus in this conference is on Tools for Teaching Logic. The topics include: Panda: A Proof Assistant in Natural Deduction for All. A Gentzen Style Proof Assistant for Undergraduate Students;the Question of the Question in Critical Thinking?;adding a Dimension to Logic Diagramming;the Many Rewards of Putting Absolutely Everything into Introductory Logic;The SELL Project: A learning Tool for E-learning Logic;ten Years of Computer-Based Tutors for Teaching Logic 2000-2010: Lessons Learned;logic in Action: An Open Logic Courseware Project;a Teaching Tool for Proving Equivalences between Logical Formulae;mhy Bib I Fail Logic? Dyslexia in the Teaching of Logic;e-learning and Semantic Technologies: Tools and Concepts;information-Theoretic Perspective for Teaching Logic;teaching Sound Principles about Invalidity;systematic Errors as an Input for Teaching Logic;The AProS Project: Teaching Logic to Business and Engineering Students;using a Learner- and Teacher-Friendly Environment for Turing machine Programming and Testing;using an Argument Ontology to Develop Pedagogical Tool Suites;visual Tools for Teaching Propositional Logic;logicamente: A Virtual learning Environment for Logic Based on learning Objects;a Framework for Coping with Logically-Minded Arguments in Philosophy;a Logic Teaching Tool Based on Tableaux for Verification and Debugging of Algorithms;CT2.0: A Collaborative Database of Examples for Teaching Informal Logic;designing an Introductory Course to Elementary Symbolic Logic within the Blackboard E-learning Environment;Araucaria-PL: Software for Teaching Argumentation Theory;teaching Logic in Philosophy;ORGANON: learning Management System for Basic Logic Courses;variables in Mathematics Education.
The proceedings contain 37 papers. The topics discussed include: authentication and authorization in web services;integrating access control mechanism with EXEL labeling scheme for XML document updating;an access cont...
ISBN:
(纸本)9783642221842
The proceedings contain 37 papers. The topics discussed include: authentication and authorization in web services;integrating access control mechanism with EXEL labeling scheme for XML document updating;an access control model for supporting XML document updating;accelerated particle swarm optimization and support vector machine for business optimization and applications;Xrd metadata to make digital identity less visible and foster trusted collaborations across networked computing ecosystems;an overview of performance comparison of different TCP variants in IP and MPLS networks;routing in mobile ad-hoc networks as a reinforcement learning task;identifying usability issues in personal calendar tools;adaptive query processing for semantic interoperable information systems;and statistical character-based syntax similarity measurement for detecting biomedical syntax variations through named entity recognition.
The proceedings contain 38 papers. The topics discussed include: digital media and the relational revolution in social science;data mining as a key enabler of computational social science;predicting market movements: ...
ISBN:
(纸本)9783642247033
The proceedings contain 38 papers. The topics discussed include: digital media and the relational revolution in social science;data mining as a key enabler of computational social science;predicting market movements: from breaking news to emerging social media;learning information diffusion models from observation and its application to behavior analysis;analysis of twitter unfollow: how often do people unfollow in twitter and why?;mathematical continuity in dynamic social networks;government 2.0 collects the wisdom of crowds;web searching for health: theoretical foundations for analyzing problematic search engine use;integration and warehousing of social metadata for search and assessment of scientific knowledge;comparing linkage graph and activity graph of online social networks;context-aware nearest neighbor query on social networks;and using tag recommendations to homogenize folksonomies in microblogging environments.
The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heart beats. In this work, automatic inter-patient classification of heart beat...
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ISBN:
(纸本)9783642184710
The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heart beats. In this work, automatic inter-patient classification of heart beats following AAMI guidelines is investigated. The prior of the normal class is by far larger than the other classes, and the classifier obtained by a standard SVM training is likely to act as the trivial acceptor. To avoid this inconvenience, a SVM classifier optimizing a convex approximation of the balanced classification rate rather than the standard accuracy is used. First, the assessment of feature sets previously proposed in the litterature is investigated. Second, the performances of this SVM model is compared to those of previously reported inter-patient classification models. The results show that the choice of the features is of major importance, and that some previously reported feature sets do not serve the classification performances. Also, the weighted SVM model with the best feature set selection achieves results better than previously reported inter-patient models with features extracted only from the R spike annotations.
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