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Toward Heterogeneous Computing to Facilitate Sequential OLAP Real-Time Applications 6

Toward Heterogeneous Computing to Facilitate Sequential OLAP...

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6th international conference on Computer and Communication Engineering (ICCCE)

作者： Hameed, Shihab A. Habaebi, Mohamed H. Alzeini, Haytham I. M. IIUM Univ Dept Elect & Comp Engn Kuala Lumpur Malaysia

ISBN: (纸本)9781509024278

Over the last decade, due to the need of analyzing and studying the logical order that data exhibit in various industries, sequential data storage and processing field has attracted a significant number of researchers. Recently, sequential OLAP has emerged as one of sequential data sub fields whereby traditional OLAP - which mainly utilizes a set data-based analysis, do not satisfy the hunger of performing pattern-based operations and time-based analysis. Such analyses can provide an insightful perspective and reveal hidden correlations among events patterns through time. therefore, extended query languages, new OLAP cube models and optimized algorithms and infrastructures have been introduced. However, the ever grown data size has always been deemed a major hurdle in the way of fully taking advantage of this data. In this context, and based on our proposed optimized heterogeneous Rabin-Karp algorithm earlier, we suggest a high performance sequential pattern detection approach that works in harmony Sequential OLAP processing requirements. the optimized algorithm is dedicated to detect patterns over parallel data streams in Real-Time. the empirical results have shown more than ten times speedup over the multi-core version.

关键词： S-OLAP heterogeneous computing Rabin-Karp data mining pattern recognition

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Features Selection For Building An Early Diagnosis Machine Learning Model For Parkinson's Disease 3

Features Selection For Building An Early Diagnosis Machine L...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Soliman, Abu Bakr Fares, Mohamed Elhefnawi, Mohamed M. Al-Hefnawy, Mahmoud Nile Univ Commun & Informat Technol Dept 6th Of October Egypt Natl Res Ctr Vet Res Div Giza Egypt Modern Univ Fac Engn Commun & Comp Dept Cairo Egypt Natl Res Ctr Bioinformat & Chemoinformat Res Grp Giza Egypt

ISBN: (纸本)9781467391870

In this work, different approaches were evaluated to optimize building machine learning classification models for the early diagnosis of the Parkinson disease. the goal was to sort the medical measurements and select the most relevant parameters to build a faster and more accurate model using feature selection techniques. Decreasing the number of features to build a model could lead to more efficient machine learning algorithm and help doctors to focus on what are the most important measurements to take into account. For feature selection we compared the Filter and Wrapper techniques. then we selected a good machine learning algorithm to detect which technique could help us by calculate the crossover scores for each technique. this research is based on a dataset which was created by Athanasius Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation. this target of these medical measurements is to find the Unified Parkinson's disease rating scale (UPDRS) which is the most commonly used scale for clinical studies of Parkinson's disease.

关键词： Parkinson Features Selection UPDRS

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15th Workshop on Computational Models of Natural Argument, CMNA XV 2015 and 6th international Workshop on Empathic Computing, IWEC-15

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15th Workshop on Computational Models of Natural Argument, CMNA XV 2015 and 6th international Workshop on Empathic Computing, IWEC-15

ISBN: (纸本)9783319462172

the proceedings contain 13 papers. the special focus in this conference is on Computational Models of Natural Argument and Empathic Computing. the topics include: A cognitive approach to relevant argument generation;argumentation mining in parliamentary discourse;automatically detecting fallacies in system safety arguments;modeling user music preference through usage scoring and user listening behavior for generating preferred playlists;comparing affect recognition in peaks and onset of laughter;modeling work stress using heart rate and stress coping profiles;wizard-of-oz support using a portable dialogue corpus;identifying significant task-based predictors of emotion in learning;design of populations in symbiotic evolution to generate chord progression in consideration of the entire music structure and item-based learning for music emotion prediction using EEG data.

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On Analysis and Evaluation of Cocktail Party Effect on Applied Educational Practice theory Using Neural Networks Modeling

On Analysis and Evaluation of Cocktail Party Effect on Appli...

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the 6th international conference on Advances in Science, Engineering and Technology (ICASET-17)

作者： Hassan M.H.Mustafa Fadhel Ben Tourkia Comp.Eng.Dept. Al-Baha Private College of Sciences

this piece of research deals with an interestingly observed interdisciplinary challenging educational issue associated with children's learning performance phenomenon in *** precisely,it addresses an answer for the critically challenging educational question : how any of the students could focus on teachers' interactive speaking in noisy environmentally overcrowd class?.more *** specifically,it deals with the critical educational issue of What is the effect of the school environment on students considering Cocktail Party Effect(CPE) on learning achievement and its relation to the Educational Practice ***,this work presents a systematic approach for evaluation of an interdisciplinary phenomenal problem of human's selectivity auditory scene ***,in the educational field practice this Cocktail Party Effect results in the Cocktail Party Problem(CPP) dealing with an auditory perception ***,it is noticed that introduced proposal for active audition modeling is motivated by analogous active vision processes,such as that observed during Optical Character recognition(OCR).In nature,observed OCR as well as pattern recognition processes have to be carried out under non ideal environmental learning condition(under effect of noisy data).More particularly,CPP is motivated to be solved by adopting selective auditory attention or equivalently selective hearing which is a type of selective attention and involves the auditory system of the nervous ***,children's Selective hearing is characterized as the action in which considerable children's ability to focus their attention inside classroom on a specific source of teacher's spoken wording ***,this process experienced as following one speaker(teachers' speech) in the presence of another noisy overcrowded signals resulting in CPE at *** the end of this paper,some interesting simulation results presented after taking into accou

关键词： Artificial neural network modeling noisy crowded learning environment signal to noise ratio cocktail party problem

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IFIP WG 13.2/13.5 Joint 6th international conference on Human-Centered Software Engineering, HCSE 2016 and 8th international conference on Human Error, Safety, and System Development, HESSD 2016

IFIP WG 13.2/13.5 Joint 6th International Conference on Huma...

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IFIP WG 13.2/13.5 Joint 6th international conference on Human-Centered Software Engineering, HCSE 2016 and 8th international conference on Human Error, Safety, and System Development, HESSD 2016

ISBN: (纸本)9783319449012

the proceedings contain 25 papers. the special focus in this conference is on Agile, Human-Centered Software Engineering, Usability Evaluation, Testing, Socio-Technical, Ethical Considerations, Human Error, Safety-Critical Systems, User, Developer Experience, Models, Methods, Adopting Tools, Demos and Posters. the topics include: Supporting the HCI aspect of agile software development by tool support for UI-pattern transformations;usability problems experienced by different groups of skilled internet users;user-test results injection into task-based design process for the assessment and improvement of both usability and user experience;framework for relative web usability evaluation on usability features in MDD;testing prototypes and final user interfaces through an ontological perspective for behavior-driven development;complementary tools and techniques for supporting fitness-for-purpose of interactive critical systems;a conceptual ux-aware model of requirements;audio guidance for runner training;enterprise model-driven agile human-centered software engineering;collaborative task modelling on the web and declarative prototyping with data;a first prototype integrated in HAMSTERS.

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Dermoscopic Feature Analysis for Melanoma recognition and Prevention 6

Dermoscopic Feature Analysis for Melanoma Recognition and Pr...

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6th international conference on Innovative Computing Technology (INTECH)

作者： Jamil, Uzma Khalid, Shehzad Akram, M. Usman Bahria Univ Dept Comp Engn Islamabad Pakistan Natl Univ Sci & Technol Islamabad Pakistan

ISBN: (纸本)9781509020003

Computer-aided diagnosis system automatically analyze skin lesions, and reduces the amount of repetitive and boring tasks carried out by the doctor. the full model of an automated system includes three important stages in order to comply with the lesion analysis : segmentation, feature extraction and classification. the data-set contains images and annotations provided by physicians. Segmentation is an imperative preprocessing step for CAD system of skin lesions. Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Dermatologists take keen interest in examining a specific clinically significant part in a lesion. that part is projected to have lesion information in the form of texture that can be relevant for detection. In case of detection of melanoma various local features for example pigment network and streaks usually occur in peripheral region of the lesion. this led to the extraction of peripheral part for feature extraction instead of whole lesion processing. In this article detailed information regarding Feature extraction and selection techniques for dermoscopic images is presented.

关键词： skin cancer dermoscopy features image processing pattern recognition

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Reconstruction of retinal spectra from RGB data using a RBF network 6

Reconstruction of retinal spectra from RGB data using a RBF ...

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6th international conference on Image Processing theory, Tools and Applications, IPTA 2016

作者： Nguyen, Uyen Laaksonen, Lauri Uusitalo, Hannu Lensu, Lasse Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Department of Ophthalmology SILK University of Tampere Finland Tauh Eye Center Tampere University Hospital Finland

ISBN: (纸本)9781467389105

In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. the approaches make use of fuzzy c-means clustering to perform quantization of the image data, and the radial basis function network to learn the mapping from the three-component color representation to the spectral space. the dissimilarities between the reconstructed spectral images and the original ones are evaluated on a retinal image set with spectral and RGB images, and by using a standard spectral quality metric. the experimental results show that the proposed approaches are able to reconstruct spectral retinal images with a relatively high accuracy. © 2016 IEEE.

关键词： Radial basis function networks

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Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI 7th

Super-Resolved Enhancement of a Single Image and Its Applica...

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7th international conference on Image and Signal Processing (ICISP)

作者： Yang, Guang Ye, Xujiong Slabaugh, Greg Keegan, Jennifer Mohiaddin, Raad Firmin, David Royal Brompton Hosp Cardiovasc MR Unit London SW3 6NP England Univ London Imperial Coll Sci Technol & Med Natl Heart & Lung Inst London SW7 2AZ England Lincoln Univ Sch Comp Sci Lincoln LN6 7TS England City Univ London Dept Comp Sci London EC1V 0HB England

ISBN: (纸本)9783319336183;9783319336176

Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learning from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform super-resolution (SR) from a single LR input. the mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information.

关键词： Image enhancement

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6th international Workshop on Computational Topology in Image Context, CTIC 2016

6th International Workshop on Computational Topology in Imag...

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6th international Workshop on Computational Topology in Image Context, CTIC 2016

ISBN: (纸本)9783319394404

the proceedings contain 26 papers. the special focus in this conference is on Computational Topology in Image Context. the topics include: Progress in persistence for shape analysis;homology computation during an incremental construction process;persistence-based pooling for shape pose recognition;bijectivity certification of 3d digitized rotations;parallel homology computation of meshes;topological descriptors for 3d surface analysis;towards a topological fingerprint of music;topological comparisons of fluvial reservoir rock volumes using betti numbers;topological analysis of amplicon structure in comparative genomic hybridization data;fast, simple and separable computation of betti numbers on three-dimensional cubical complexes;computation of cubical steenrod squares;on homotopy continuation for speech restoration;finding largest rectangle inside a digital object;shape matching of 3d topologically segmented objects;construction of an approximate 3d orthogonal convex skull;designing a topological algorithm for 3d activity recognition;robust computations of reeb graphs in 2-d binary images;the coherent matching distance in 2d persistent homology;persistent homology on grassmann manifolds for analysis of hyperspectral movies;persistence based on LBP scale space;on some local topological properties of naive discrete sphere;discrete ISO-contour geodesics for topological analysis of voxelized objects;solving distance geometry problem with inexact distances in integer plane and segmentation and classification of geoenvironmental zones of interest in aerial images using the bounded irregular pyramid.

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Structural health monitoring from discrete binary data through pattern recognition 6th

Structural health monitoring from discrete binary data throu...

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6th international conference on Structural Engineering, Mechanics and Computation (SEMC)

作者： Salehi, H. Burgueno, R. Das, S. Biswas, S. Chakrabartty, S. Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Washington Univ Dept Comp Sci & Engn St Louis MO 63110 USA

ISBN: (纸本)9781315641645;9781138029279

A continuing challenge in structural health monitoring is power availability for sensors to collect and communicate data. While self-powered sensors are helping address this concern, the harvested power with current technology is limited and improving the network efficiency requires reducing the power budget. A way to minimize the communication power demand is to transmit the minimum amount of information, namely one bit. the binary signal can be generated at a sensor node according to a local rule based on physical measurements, but interpretation at the global level requires dealing with discrete binary (1 or 0) data. this study presents an investigation on pattern recognition (PR) methods adapted from image data analysis techniques for the interpretation of binary data for use in structural health monitoring. the ability of the PR methods to identify service demands and localized material degradation was evaluated through finite element simulations and experiments on simple plates. Results indicates that PR techniques are able to use binary data to discern structural response and detect the presence and location of damage.

关键词： Structural health monitoring

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