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8th IFIP 12 international conference on Intelligent Information Processing, IIP 2014

8th IFIP 12 International Conference on Intelligent Informat...

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8th IFIP international conference on Intelligent Information Processing, IIP 2014

ISBN: (纸本)9783662449790

the proceedings contain 35 papers. the special focus in this conference is on machine learning, data Mining, Web Mining, Multi-agent Systems, Automatic Reasoning, Decision Algorithms, Multimedia pattern recognition and Information Security. the topics include: Semi-paired probabilistic canonical correlation analysis;using bat algorithm with levy walk to solve directing orbits of chaotic systems;complex proteomes analysis using label-free mass spectrometry- based quantitative approach coupled with biomedical knowledge;online migration solver based on instructions statistics;extracting part-whole relations from online encyclopedia;topic detection and evolution analysis on microblog;adaptive mechanism based on shared learning in multi-agent system;an agent-based autonomous management approach to dynamic services;research and application analysis of feature binding mechanism;verification of branch-time property based on dynamic description logic;dynamic description logic based on DL-Lite;formalizing the matrix inversion based on the adjugate matrix in HOL4;a heuristic approach to acquisition of minimum decision rule sets in decision systems;cooperative decision algorithm for time critical assignment without explicit communication;using PDDL to solve vehicle routing problems;automated localization and accurate segmentation of optic disc based on intensity within a minimum enclosing circle;a traffic camera calibration method based on multi-rectangle;a multi-instance multi-label learning framework of image retrieval;the retrieval of shoeprint images based on the integral histogram of the Gabor transform domain;scene classification using spatial and color features;multipath convolutional-recursive neural networks for object recognition;identification of co-regulated gene network by using path consistency algorithm based on gene ontology and efficient integrity protection for P2P streaming.

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Multi-modal pipeline for comprehensive validation of Mitral Valve geometry and functional computational models

Multi-modal pipeline for comprehensive validation of Mitral ...

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4th international Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013

作者： Neumann, Dominik Grbic, Sasa Mansi, Tommaso Voigt, Ingmar Rabbah, Jean-Pierre Siefert, Andrew W. Saikrishnan, Neelakantan Yoganathan, Ajit P. Yuh, David D. Ionasec, Razvan Ioan Imaging and Computer Vision Siemens Corporate Technology Princeton NJ United States Pattern Recognition Lab. University of Erlangen-Nuremberg Germany Computer Aided Medical Procedures Technical University Munich Germany Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta GA United States Section of Cardiac Surgery Department of Surgery Yale University New Haven CT United States

ISBN: (纸本)9783642542671

Valvular heart disease affects a high number of patients, exhibiting significant mortality and morbidity rates. Mitral Valve (MV) Regurgitation, a disorder in which the MV does not close properly during systole, is among its most common forms. Traditionally, it has been treated with MV replacement. However, recently there is an increased interest in MV repair procedures, providing better long-term survival, better preservation of heart function, lower risk of complications, and usually eliminating the need for long-term use of blood thinners (anticoagulants). these procedures are complex and require an experienced surgeon and elaborate pre-operative planning. Hence, there is a need for efficient tools for training and planning of MV repair interventions. Computational models of valve function have been developed for these purposes. Nevertheless, state-of-the-art models remain approximations of real anatomy with considerable simplifications, since current modalities are limited by image quality. Hence, there is an important need to validate such low-fidelity models against comprehensive ex-vivo data to assess their clinical applicability. As a first step towards this aim, we propose an integrated pipeline for the validation of MV geometry and function models estimated in ex-vivo TEE data with respect to ex-vivo microCT data. We utilize a controlled experimental setup for ex-vivo imaging and employ robust machine learning and optimization techniques to extract reproducible geometrical models from both modalities. Using one exemplary case, we demonstrate the validity of our framework. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Pipelines

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Predicting the imagined contents using brain activation

Predicting the imagined contents using brain activation

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4th National conference on Computer Vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Miyapuram, Krishna Prasad Schultz, Wolfram Tobler, Philippe N. IIT Gandhinagar Cognit Sci Program Ahmadabad 382424 Gujarat India Univ Cambridge Physiol Dev & Neurosci Cambridge England Univ Zurich Dept Econ Zurich Switzerland

ISBN: (纸本)9781479915880

Mental imagery refers to percept-like experiences in the absence of sensory input. Brain imaging studies suggest common, modality-specific, neural correlates imagery and perception. We associated abstract visual stimuli with either visually presented or imagined monetary rewards and scrambled pictures. Brain images for a group of 12 participants were collected using functional magnetic resonance imaging. Statistical analysis showed that human midbrain regions were activated irrespective of the monetary rewards being imagined or visually present. A support vector machine trained on the midbrain activation patterns to the visually presented rewards predicted with 75% accuracy whether the participants imagined the monetary reward or the scrambled picture during imagination trials. Training samples were drawn from visually presented trials and classification accuracy was assessed for imagination trials. these results suggest the use of machine learning technique for classification of underlying cognitive states from brain imaging data.

关键词： machine learning brain imaging support vector machine brain reading mental imagery

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Polynomial order prediction using a classifier trained on meta-measurements

Polynomial order prediction using a classifier trained on me...

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4th international conference on Emerging Security Technologies (EST)

作者： Gherman, Bogdan G. Sirlantzis, Konstantinos Univ Kent Sch Engn & Digital Arts Canterbury CT2 7NT Kent England

ISBN: (纸本)9780769550770

Polynomial regression is still widely used in engineering and economics where polynomials of low order (usually less than tenth order) are being fitted to experimental data. However, the fundamental problem of selecting the optimal order of the polynomial to be fitted to experimental data is not a straightforward problem. this paper investigates the performance of automated methods for predicting the order of the polynomial that can be fitted on the decision boundary formed between two classes in a pattern recognition problem. We have investigated statistical methods and proposed a method of predicting the order of the polynomial. Our proposed machine learning method is computing a number of measurements on the input data which are used by a classifier trained offline to predict the order of the polynomial that should be fitted to the decision boundary. We have considered two matching scenarios. One scenario is where we have counted only the exact matches as being correct and another scenario in which we count as correct an exact match and higher polynomial orders. Experimental results on synthetic data show that our proposed method predicts the exact order of the polynomial with 31.90% accuracy as opposed to 13.22% of the best statistical method, but it also under-estimates the true order almost twice as often when compared to statistical methods of predicting the order of the polynomial to be fitted to the same data points.

关键词： model selection polynomial regression meta-measurements pattern recognition decision boundary

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A study on mammography computer aided diagnosis system using machine learning methods 4

A study on mammography computer aided diagnosis system using...

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IET Chennai 4th international conference on Sustainable Energy and Intelligent Systems, SEISCON 2013

作者： Dinakaran, K. Sivakrithika, V. Department of CSE RMD Engineering College India Department of CSE TRP Engineering College India

ISBN: (纸本)9781785610301

Early diagnosis is an important aspect of successful treatment for breast cancer. Mammogram is the most reliable imaging technique available. It is a challenging task for radiologists to detect the abnormalities in the mammograms. Computing helps the radiologists in diagnosing the abnormalities in the mammogram. Computer Aided Diagnosis System involves computerized biomedical image analysis to classify the mammography into benign or malign. In a decade of research work number of algorithms had been proposed to classify the image that employ data mining techniques, image processing methods, machine learning methods and pattern recognition. In this paper such algorithms in previous research work is studied and their performance is discussed.

关键词： Computer aided diagnosis

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machine learning based biomedical named entity recognition 4

Machine learning based biomedical named entity recognition

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IET Chennai 4th international conference on Sustainable Energy and Intelligent Systems, SEISCON 2013

作者： Kanya, N. Ravi, T. Manonmanium Sundaranar University TN India Srinivasa Institute of Engineering and Technology TN India

ISBN: (纸本)9781785610301

the biomedical society makes wide use of text mining technology. Named Entity (NE) extraction is one of the most primary and significant tasks in biomedical information extraction of text mining technology. Named Entity recognition (NER) involves processing structured and unstructured documents to recognize the definite kinds of entities and categorization of them into some predefined classes. Several Named Entity recognition systems have been developed for the Biomedical Domain based on the Rule-Based, Dictionary based and machine learning based techniques. Implementing the best approach is not possible in all domains. machine learning based approaches have many advantages than other approaches. In this paper we are proposing a machine learning based framework for recognizing named entities from biomedical abstracts. For this study we used benchmarked datasets such as GENETAG and JNLPBA.

关键词： data mining

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Predicting Aquaplaning Performance from Tyre Profile Images with machine learning

Predicting Aquaplaning Performance from Tyre Profile Images ...

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10th international conference on Image Analysis and recognition (ICIAR)

作者： Weyde, Tillman Slabaugh, Gregory Fontaine, Gauthier Bederna, Christoph City Univ London London EC1V 0HB England ENSTA Paristech Palaiseau France Continental AG Hannover Germany

ISBN: (纸本)9783642390944;9783642390937

the tread of a tyre consists of a profile (pattern of grooves, sipes, and blocks) mainly designed to improve wet performance and inhibit aquaplaning by providing a conduit for water to be expelled underneath the tyre as it makes contact with the road surface. Testing different tread profile designs is time consuming, as it requires fabrication and physical measurement of tyres. We propose a supervised machine learning method to predict tyres' aquaplaning performance based on the tread profile described in geometry and rubber stiffness. Our method provides a regressor from the space of profile geometry, reduced to images, to aquaplaning performance. Experimental results demonstrate that image analysis and machine learning combined with other methods can yield improved prediction of aquaplaning performance, even using non-normalised data. therefore this method has can potentially save substantial cost and time in tyre development. this investigation is based on data provided by Continental Reifen Deutschland GmbH.

关键词： Image analysis supervised machine learning non-normalised data tyre profile regression

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Dynamic programming for Bayesian logistic regression learning under concept drift

Dynamic programming for Bayesian logistic regression learnin...

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5th international conference on pattern recognition and machine Intelligence, PReMI 2013

作者： Turkov, Pavel Krasotkina, Olga Mottl, Vadim Tula State University Tula Russia Computing Center of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783642450617

A data stream is an ordered sequence of training instances arriving at a rate that does not permit to permanently store them in memory and leads to the necessity of online learning methods when trying to predict some hidden target variable. In addition, concept drift often occurs, what means that the statistical properties of the target variable may change over time. In this paper, we present a framework of solving the online pattern recognition problem in data streams under concept drift. the framework is based on the application of the Bayesian approach to the probabilistic pattern recognition model in terms of logistic regression, hidden Markov model and dynamic programming. © Springer-Verlag 2013.

关键词： Hidden Markov models

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recognition of Sleep Dependent Memory Consolidation with Multi-modal Sensor data

Recognition of Sleep Dependent Memory Consolidation with Mul...

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IEEE international conference on Body Sensor Networks

作者： Sano, Akane Picard, Rosalind W. MIT Media Lab Cambridge MA 02139 USA

ISBN: (纸本)9781479903306;9781479903313

this paper presents the possibility of recognizing sleep dependent memory consolidation using multi-modal sensor data. We collected visual discrimination task (VDT) performance before and after sleep at laboratory, hospital and home for N=24 participants while recording EEG (electroencepharogram), EDA (electrodermal activity) and ACC (accelerometer) or actigraphy data during sleep. We extracted features and applied machine learning techniques (discriminant analysis, support vector machine and k-nearest neighbor) from the sleep data to classify whether the participants showed improvement in the memory task. Our results showed 60-70% accuracy in a binary classification of task performance using EDA or EDA+ACC features, which provided an improvement over the more traditional use of sleep stages (the percentages of slow wave sleep (SWS) in the 1st quarter and rapid eye movement (REM) in the 4th quarter of the night) to predict VDT improvement.

关键词： memory consolidation visual discrimination task (VDT) EEG EDA actigraphy classification galvanic skin response GSR

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A method for hand detection using internal features and active boosting-based learning

A method for hand detection using internal features and acti...

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4th Symposium on Information and Communication Technology, SoICT 2013

作者： Nguyen, Van-Toi Nguyen, thuy thi Mullot, Remy Tran, thi-thanh-Hai Le, Hung International Research Institute MICA HUST - CNRS/UMI-2954 - Grenoble INP Hanoi University of Science and Technology Viet Nam L3i Laboratory La Rochelle University France Hanoi University of Agriculture Viet Nam Hanoi National University of Education Viet Nam

ISBN: (纸本)9781450324540

Hand posture recognition has important applications in sign language, human machine interface, etc. In most such systems, the first and important step is hand detection. this paper presents a hand detection method based on internal features in an active boosting-based learning framework. the use of efficient Haar-like, local binary pattern and local orientation histogram as internal features allows fast computation of informative hand features for dealing with a great variety of hand appearances without background interference. Interactive boosting-based on-line learning allows efficiently training and improvement for the detector. Experimental results show that the proposed method outperforms the conventional methods on video data with complex background while using a smaller number of training samples. the proposed method is reliable for hand detection in the hand posture recognition system. Copyright © 2013 ACM.

关键词： Local binary pattern

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