Search results for "NEURAL NETWORKS"

showing 10 items of 599 documents

Modeling anti-allergic natural compounds by molecular topology.

2013

Molecular topology has been applied to the search of QSAR models able to identify the anti-allergic activity of a wide group of heterogeneous compounds. Through the linear discriminant analysis and artificial neural networks, correct classification percentages above 85% for both the training set and the test set have been obtained. After carrying out a virtual screening with a natural product library, about thirty compounds with theoretical anti-allergic activity have been selected. Among them, hesperidin, naringin, salinomycin, sorbitol, curcumol, myricitrin, diosmin and kinetin stand out. Some of these compounds have already been referenced as having anti-allergic activity.

Virtual screeningQuantitative structure–activity relationshipStereochemistryOrganic ChemistryDiosminDiscriminant AnalysisQuantitative Structure-Activity RelationshipGeneral MedicineComputational biologyLinear discriminant analysisModels BiologicalComputer Science Applicationschemistry.chemical_compoundHesperidinchemistryArtificial IntelligenceTest setDrug DiscoveryAnti-Allergic AgentsmedicineHumansNeural Networks ComputerMyricitrinNaringinmedicine.drugCombinatorial chemistryhigh throughput screening
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Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

2019

Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…

Volumetric imagingComputer scienceProfundo InterpretabilidadConvolutional neural network030218 nuclear medicine & medical imagingPattern Recognition Automatedchemistry.chemical_compoundMacular Degeneration[SPI]Engineering Sciences [physics]0302 clinical medicineDeep learning modelsInterpretabilityModelos de aprendizajeAged 80 and overArtificial neural networkmedicine.diagnostic_testMedical findings KeyWords Plus:MACULAR DEGENERATIONAngiographyMiddle AgedRetinal diseases3. Good healthComputer Science ApplicationsArea Under CurveTomographyMedical findingsAlgorithmsTomography Optical CoherenceAprendizaje - ModelosDiabetic macular edemaHealth InformaticsHallazgos médicosMacular Edema03 medical and health sciencesDeep LearningOptical coherence tomographymedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingDeep InterpretabilityHumans[INFO]Computer Science [cs]Enfermedades de la retinaRetinopathyAgedDiabetic RetinopathyOptical coherence tomographybusiness.industryDeep learningReproducibility of ResultsRetinalPattern recognitionMacular degenerationmedicine.diseasechemistryArtificial intelligenceNeural Networks ComputerLa tomografía de coherencia ópticabusinessClassifier (UML)030217 neurology & neurosurgerySoftware
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Aging Effects in a Lennard-Jones Glass

1997

Using molecular dynamics simulations we study the out of equilibrium dynamic correlations in a model glass-forming liquid. The system is quenched from a high temperature to a temperature below its glass transition temperature and the decay of the two-time intermediate scattering function C(t_w,t+t_w) is monitored for several values of the waiting time t_w after the quench. We find that C(t_w,t+t_w) shows a strong dependence on the waiting time, i.e. aging, depends on the temperature before the quench and, similar to the case of spin glasses, can be scaled onto a master curve.

Waiting timeScattering functionMaterials scienceSpin glassCondensed matter physicsStatistical Mechanics (cond-mat.stat-mech)General Physics and AstronomyThermodynamicsFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksCondensed Matter::Disordered Systems and Neural NetworksCondensed Matter::Soft Condensed MatterMolecular dynamicsCondensed Matter::Statistical MechanicsGlass transitionCondensed Matter - Statistical Mechanics
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A multiscale method for gamma/h discrimination in extensive air showers

2011

We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. The separation technique is particularly suited for being applied when the topology of the particle distribution in the shower front is as largely detailed as possible. Here, our method is discussed and applied to a set of fully simulated vertical showers in the experimental framework of ARGO-YBJ, taking advantage of both the space and time distribution of the detected sec…

Wavelet MethodNeural NetworksCosmic Rays; Extensive Air Showers; Multiscale Analysis; Wavelet Methods; Neural NetworksMultiscale AnalysiSettore FIS/01 - Fisica SperimentaleExtensive Air ShowerCosmic Ray
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Online Web Bot Detection Using a Sequential Classification Approach

2019

A significant problem nowadays is detection of Web traffic generated by automatic software agents (Web bots). Some studies have dealt with this task by proposing various approaches to Web traffic classification in order to distinguish the traffic stemming from human users' visits from that generated by bots. Most of previous works addressed the problem of offline bot recognition, based on available information on user sessions completed on a Web server. Very few approaches, however, have been proposed to recognize bots online, before the session completes. This paper proposes a novel approach to binary classification of a multivariate data stream incoming on a Web server, in order to recogn…

Web serverHTTP request analysis; Internet security; Machine learning; Neural networks; Sequential classification; Web bot detectionSettore INF/01 - InformaticaWeb bot detectionComputer sciencebusiness.industrySequential classification020206 networking & telecommunications02 engineering and technologyMachine learningcomputer.software_genreInternet securitySession (web analytics)Task (computing)Web trafficMachine learning0202 electrical engineering electronic engineering information engineeringHTTP request analysis020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerNeural networksInternet security2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
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Wittgenstein, Turing, and Neural Networks

2018

The main task of this paper is grounding the socio-anthropological “naturalization” of meaning operated by the later Wittgenstein in his remarks on rule-following in the Philosophical Investigations in considerations relating to models of low-level (biological) processes of imitation, training, and learning. If the operation suggested above is successful, two of its immediate consequences are that the social aspect of language can no longer be considered as a primitive notion, but needs to be placed upon, if not reduced to, a biological foundation; and that the study of thought, and, actually, of certain brain processes, becomes prior in the order of explanation to the study of language. Th…

Wittgenstein Turing Neural Networks Cognitive Science Artificial Intelligence Naturalism learning trainingSettore M-FIL/02 - Logica E Filosofia Della Scienza
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Effect of disorder on Majorana localization in topological superconductors: a quasiclassical approach

2020

Two-dimensional (2D) topological superconductors (TS) host chiral Majorana modes (MMs) localized at the boundaries. In this work, we study the effect of disorder on the localization length of MMs in two-dimensional spin-orbit (SO) coupled superconductors within quasiclassical approximation. We find nonmonotonic behavior of the Majorana localization length as a function of disorder strength. At weak disorder, the Majorana localization length decreases with an increasing disorder strength. Decreasing the disorder scattering time below a crossover value ${\ensuremath{\tau}}_{c}$, the Majorana localization length starts to increase. The crossover scattering time depends on the relative magnitud…

Work (thermodynamics)suprajohtavuusField (physics)CrossoverFOS: Physical sciencessuperconductorsTopology01 natural sciencessuprajohteet010305 fluids & plasmasSuperconductivity (cond-mat.supr-con)disordered systems0103 physical sciencesMesoscale and Nanoscale Physics (cond-mat.mes-hall)010306 general physicsSuperconductivityPhysicsCondensed Matter - Mesoscale and Nanoscale Physicsmajorana fermionsScatteringCondensed Matter - SuperconductivityFunction (mathematics)Disordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksCoupling (probability)kvasihiukkasetMAJORANA
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A nonlinear oscillators network devoted to image processing

2004

A contrast enhancement and image inverting tool using a lattice of uncoupled nonlinear oscillators is proposed. We show theoretically and numerically that the gray scale picture contrast is strongly enhanced even if this one is initially very small. An image inversion can be also obtained in real time with the same Cellular Nonlinear Network (CNN) without reconfiguration of the network. A possible electronic implementation of this CNN is finally discussed.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[ PHYS.COND.CM-DS-NN ] Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]Image processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingCellular nonlinear networksTopology01 natural sciencesGrayscale010305 fluids & plasmasNonlinear oscillators[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingControl theoryLattice (order)0103 physical sciences[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]010306 general physicsEngineering (miscellaneous)ComputingMilieux_MISCELLANEOUSArtificial neural networkApplied MathematicsControl reconfigurationInversion (meteorology)neural networks[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsNonlinear systemComputer Science::Computer Vision and Pattern RecognitionModeling and SimulationNonlinear dynamics[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system.

2011

International audience; The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using heteroassociative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceMultispectral imageHealth InformaticsDermoscopy[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesSensitivity and SpecificitySkin DiseasesMultispectral pattern recognition010309 opticsImaging systemSoftware[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingInterference (communication)0103 physical sciencesImage Interpretation Computer-AssistedSkin cancerHumansRadiology Nuclear Medicine and imagingComputer visionSpatial analysis[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingSpectral reflectanceRadiological and Ultrasound TechnologyArtificial neural networkbusiness.industryMultispectral images010401 analytical chemistryHyperspectral imagingReproducibility of ResultsEquipment DesignComputer Graphics and Computer-Aided Design0104 chemical sciencesEquipment Failure AnalysisHyperspectral cube reconstructionColorimetryComputer Vision and Pattern RecognitionArtificial intelligenceNeural Networks Computerbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingPreclinical imagingNeural networksFiltrationComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
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Experimental and numerical enhancement of Vibrational Resonance in a neural circuit

2012

International audience; A neural circuit exactly ruled by the FitzHugh-Nagumo equations is excited by a biharmonic signal of frequencies f and F with respective amplitudes A and B. The magnitude spectrum of the circuit response is estimated at the low frequency driving f and presents a resonant behaviour versus the amplitude B of the high frequency. For the first time, it is shown experimentally that this Vibrational Resonance effect is much more pronounced when the two frequencies are multiple. This novel enhancement is also confirmed by numerical predictions. Applications of this nonlinear effect to the detection of weak stimuli are finally discussed.

[ PHYS.COND.CM-DS-NN ] Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]02 engineering and technologyLow frequency01 natural sciencesSignalVibrational ResonanceNuclear magnetic resonance[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0103 physical sciences0202 electrical engineering electronic engineering information engineeringVibrational resonance[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]Electrical and Electronic Engineering010306 general physicsMathematicsQuantitative Biology::Neurons and Cognition020208 electrical & electronic engineering[SPI.TRON]Engineering Sciences [physics]/ElectronicsComputational physics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsNonlinear systemAmplitudeExcited stateNonlinear resonanceBiharmonic equationNonlinear dynamical systemsFitzHugh-Nagumo
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