Search results for "vector [form factor]"

showing 10 items of 770 documents

Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta

2021

The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it re…

Computer sciencePhysiologySample (statistics)Target populationMachine learningcomputer.software_genreData acquisitionVirtual patientPhysiology (medical)digital twinQP1-981support vector machineOriginal Researchbusiness.industrygenerative adversarial networkSampling (statistics)synthetic populationthoracic-aortaSupport vector machineReference samplein-silico trialsCohortArtificial intelligencevirtual cohortbusinesscomputerclinically-driven samplingFrontiers in Physiology
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Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

2021

Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results

Computer scienceProcess (engineering)Geography Planning and DevelopmentAquatic ScienceMachine learningcomputer.software_genreBiochemistrysupport vector regressionTD201-500Uncertainty analysisWater Science and TechnologyEmulationArtificial neural networkFlood mythWater supply for domestic and industrial purposesbusiness.industryDimensionality reductionHydraulic engineeringSupport vector machineemulatorsVDP::Teknologi: 500Sample size determinationerror structureArtificial intelligencetraining set sizebusinessTC1-978computerartificial neural networkWater
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Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification

2019

Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …

Computer scienceSVM02 engineering and technologyConvolutional neural networklcsh:TechnologyIIF image030218 nuclear medicine & medical imaginglcsh:Chemistry03 medical and health sciences0302 clinical medicineClassifier (linguistics)Autoimmune disease0202 electrical engineering electronic engineering information engineeringGeneral Materials Scienceautoimmune diseasesReceiver operating characteristic (ROC) curveInstrumentationlcsh:QH301-705.5AccuracyIIF imagesFluid Flow and Transfer ProcessesIndirect immunofluorescencebusiness.industrylcsh:TProcess Chemistry and TechnologyGeneral EngineeringPattern recognitionIIfGold standard (test)Convolutional Neural Network (CNN)lcsh:QC1-999Computer Science ApplicationsIntensity (physics)Support vector machineFluorescence intensitylcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencebusinesslcsh:Engineering (General). Civil engineering (General)lcsh:Physics
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A multi-process system for HEp-2 cells classification based on SVM

2016

An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we de…

Computer scienceSVM02 engineering and technologyImmunofluorescencecomputer.software_genre030218 nuclear medicine & medical imagingImage (mathematics)03 medical and health sciences0302 clinical medicineArtificial IntelligencePyramid0202 electrical engineering electronic engineering information engineeringmedicinePyramid (image processing)Spatial analysisAccuracy1707Contextual image classificationmedicine.diagnostic_testFeatures reductionIndirect immunofluorescencePipeline (software)Class (biology)Settore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)StainingSupport vector machineHep-2 cells classificationSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionData miningcomputerSoftware
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Automated detection of microaneurysms using robust blob descriptors

2013

International audience; Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fun…

Computer scienceSVMComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyFundus (eye)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0202 electrical engineering electronic engineering information engineeringmedicineComputer visionRetinaRadon transformbusiness.industrySURFHessian[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Diabetic retinopathymedicine.diseaseMicroaneurysmSupport vector machinemedicine.anatomical_structureComputer-aided diagnosis020201 artificial intelligence & image processingArtificial intelligencebusinessSVDRetinopathy
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An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…

Computer scienceSVMKNN02 engineering and technologylcsh:TechnologyIIF imageHough transformlaw.inventionlcsh:Chemistry03 medical and health scienceslawClassifier (linguistics)0202 electrical engineering electronic engineering information engineeringPreprocessorGeneral Materials ScienceSegmentationcell segmentationlcsh:QH301-705.5InstrumentationIIF images030304 developmental biologyFluid Flow and Transfer Processes0303 health sciencesIndirect immunofluorescencelcsh:Tbusiness.industryProcess Chemistry and TechnologyGeneral EngineeringPattern recognitionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)ROC curvelcsh:QC1-999Computer Science ApplicationsSupport vector machineParameter identification problemFluorescence intensityHough transformlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencelcsh:Engineering (General). Civil engineering (General)businesslcsh:Physicsactive contours modelApplied Sciences
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The Sliced COO Format for Sparse Matrix-Vector Multiplication on CUDA-enabled GPUs

2012

Abstract Existing formats for Sparse Matrix-Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an effcient CUDA implementation to perform SpMV on the GPU. While previous work shows experiments on small to medium-sized sparse matrices, we perform evaluations on large sparse matrices. We compared SCOO performance to existing formats of the NVIDIA Cusp library. Our resutls on a Fermi GPU show that SCOO outperforms the COO and CSR format for all tested matrices and the HYB format for all tested unstructured matrices. Furthermore, comparison to a Sandy-Bridge CPU sho…

Computer scienceSparse matrix-vector multiplicationCUDAParallel computingMatrix (mathematics)CUDAFactor (programming language)SpMVGeneral Earth and Planetary SciencesMultiplicationcomputerFermiGeneral Environmental Sciencecomputer.programming_languageSparse matrixProcedia Computer Science
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Proactive Handoff of Secondary User in Cognitive Radio Network Using Machine Learning Techniques

2021

Spectrum management always appears as an essential part of modern communication systems. Handoff is initiated when the signal strength of a current user deteriorates below a certain threshold. In cognitive radio network, the perception of handoff is different due to the presence of two categories of users: certified/primary user and uncertified/secondary user. The reason for the spectrum handoff arises when the primary user (PU) returns to one of its band used by the secondary user. The spectrum handoff is of two types: reactive handoff and proactive handoff. There are certain limitations in reactive handoff, such as it suffers from prolonged handoff latency and interference. In the proacti…

Computer sciencebusiness.industryComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSDecision treeCommunications systemMachine learningcomputer.software_genreSpectrum managementRandom forestSupport vector machineCognitive radioHandoverMultilayer perceptronArtificial intelligencebusinesscomputer
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Localization and Activity Classification of Unmanned Aerial Vehicle Using mmWave FMCW Radars

2021

In this article, we present a novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar. The localization and activity classification for aerial vehicle enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications. In the proposed method, Radar’s antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. The height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival. The aerial vehicle’s activ…

Computer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputerApplications_COMPUTERSINOTHERSYSTEMSConvolutional neural networklaw.inventionSupport vector machinelawActivity classificationChirpRange (statistics)Computer visionGradient boostingArtificial intelligenceElectrical and Electronic EngineeringRadarbusinessInstrumentationEdge computingIEEE Sensors Journal
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Perceptual Image Representations for Support Vector Machine Image Coding

2007

Support-vector-machine image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regressor (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image-coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of t…

Computer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionImage processingPermissionImage (mathematics)Support vector machineAutomatic image annotationDigital image processingComputer visionArtificial intelligenceImage warpingbusinessFeature detection (computer vision)
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