The proceedings contain 38 papers. The topics discussed include: context aware computing and its utilization in event-based systems;logic-based representation, reasoning and machine learning for event recognition;an e...
ISBN:
(纸本)9781605589275
The proceedings contain 38 papers. The topics discussed include: context aware computing and its utilization in event-based systems;logic-based representation, reasoning and machine learning for event recognition;an event view model and DSL for engineering an event-based SOA monitoring infrastructure;an RFID architecture based on an event-oriented component model;content-based rendezvous with upgraph combination;event semantics in event dissemination architectures for massive multiuser virtual environments;complex event processing synergies with predictive analytics;event processing for large-scale distributed games;reliable fault-tolerant sensors for distributed systems;business-oriented development methodology for complex event processing: demonstration of an integrated approach for process monitoring;placement of replicated tasks for distributed stream processing systems;and an approach for iterative event pattern recommendation.
The proceedings contain 115 papers. The topics discussed include: bioinformatics - transition from algorithmic to data intensive science;personalized medicine - challenges and opportunities for informatics research;ne...
ISBN:
(纸本)9781450304382
The proceedings contain 115 papers. The topics discussed include: bioinformatics - transition from algorithmic to data intensive science;personalized medicine - challenges and opportunities for informatics research;next generation sequencing data analysis;intelligent patternrecognition and applications;relationship preserving feature selection for unlabelled clinical trials time-series;comparison of virus interactions with human signal transduction pathways;relational operators for prioritizing candidate biomarkers in high-throughput differential expression data;alignment-based versus variation-based transformation methods for clustering;parameter estimation approach for non-linear systems biology models using spline approximation;random forest-based prediction of protein sumoylation sites from sequence features;SplittingHeirs: inferring haplotypes by optimizing resultant dense graphs;and automatic selection of near-native protein-ligand conformations using a hierarchical clustering and volunteer computing.
The use of certain machine beaming and patternrecognition tools for automated pharmacological drug design has been recently introduced Different, families of kat mug algorithms and Support Vector Machines in particul...
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ISBN:
(纸本)9783642130243
The use of certain machine beaming and patternrecognition tools for automated pharmacological drug design has been recently introduced Different, families of kat mug algorithms and Support Vector Machines in particular have been applied to the task of associating observed chemical propel Lies and pharmacological activities to certain kinds of representations of the candidate compounds The put pose of this work: is to select an appropriate feature (Adoring front a large set of molecular descriptors usually used in the domain of Ding Activity Characterization To tins end, a new input pinning method is introduced and assessed with respect to commonly used (entitle ranking algorithms
Interval type-2 fuzzy logic can be applied to perform image processing and patternrecognition. In this work a new type-2 fuzzy logic method is applied for edge detection in images and the results are compared with th...
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Interval type-2 fuzzy logic can be applied to perform image processing and patternrecognition. In this work a new type-2 fuzzy logic method is applied for edge detection in images and the results are compared with three different traditional techniques for the same goal with the type-2 edge detection outperforming the other techniques.
Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective fea...
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ISBN:
(纸本)9781450304603
Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories;ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis. Copyright 2010 ACM.
This paper presents a novel hybrid approach based on clustering technique (CT) and least square support vector machine (LS-SVM) denoted as CT-LS-SVM for classifying two-class EEG signals. The study aims to extract rep...
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This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two-polarity trend. According to the observation that the...
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There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are m...
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Traditional patternrecognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced,...
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ISBN:
(纸本)9781424475421
Traditional patternrecognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain.
This paper addresses the problem of traffic sign recognition in real-time conditions. The algorithm presented in this paper is based on detecting traffic signs in life images and videos using pattern matching of the u...
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This paper addresses the problem of traffic sign recognition in real-time conditions. The algorithm presented in this paper is based on detecting traffic signs in life images and videos using pattern matching of the unknown sign's shape with standard shapes of the traffic signs. The pattern matching algorithm works with shape vertices rather than the whole image. This reduces the computation time which is a crucial factor to fit real-time demands. The algorithm is translation and scaling invariant. It shows high robustness as it is tested with 500 images and several videos and a recognition rate of 97% is achieved.
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