Search results for "Artificial intelligence"

showing 10 items of 6122 documents

Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces

2017

There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine lea…

Training setbusiness.industryFeature vectorSimilarity heuristicPattern recognition02 engineering and technologyMachine learningcomputer.software_genreSemantic similarityArtificial Intelligence020204 information systemsNormalized compression distance0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceJaro–Winkler distancebusinesscomputerClassifier (UML)SoftwareSimilarity learningMathematicsInternational Journal of Pattern Recognition and Artificial Intelligence
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Honeybees can recognise images of complex natural scenes for use as potential landmarks

2008

SUMMARY The ability to navigate long distances to find rewarding flowers and return home is a key factor in the survival of honeybees (Apis mellifera). To reliably perform this task, bees combine both odometric and landmark cues,which potentially creates a dilemma since environments rich in odometric cues might be poor in salient landmark cues, and vice versa. In the present study, honeybees were provided with differential conditioning to images of complex natural scenes, in order to determine if they could reliably learn to discriminate between very similar scenes, and to recognise a learnt scene from a novel distractor scene. Choices made by individual bees were modelled with signal detec…

Transfer testSpatial visionPhysiologyComputer scienceDecision MakingVideo RecordingAquatic ScienceDiscrimination LearningVisual processingAnimalsNatural (music)Computer visionMolecular BiologyEcology Evolution Behavior and SystematicsCommunicationLandmarkbusiness.industryBeesPattern Recognition VisualSalientInsect ScienceConditioning OperantAnimal Science and ZoologyDifferential conditioningArtificial intelligenceCuesbusinessPhotic StimulationJournal of Experimental Biology
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Texture analysis with statistical methods for wheat ear extraction

2007

In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are…

Transform theoryComputer sciencebusiness.industryFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionMobile robotImage processingImage segmentationField (computer science)Image (mathematics)Component (UML)Computer visionArtificial intelligencebusinessEighth International Conference on Quality Control by Artificial Vision
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Transformation of the Software Testing Glossary into a Browsable Concept Map

2014

Authors propose a transformation method of the glossary “Standard glossary of terms used in Software Testing” created by ISTQB document into a basic concept map. By applying natural language processing techniques and analyzing the discovered relations between concepts the most essential aspects of the software testing domain are elicited and integrated. As the result a browsable concept map is created. Browsable concept map can be used as a learning support tool.

Transformation (function)GlossarySoftware testingComputer scienceProgramming languagebusiness.industryConcept mapLearning supportArtificial intelligencecomputer.software_genrebusinesscomputerDomain (software engineering)
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Indexing Method for Transitive Relationships of Product Information

2008

To successfully use a relational database management system (RDBMS) as a repository for product information, the RDBMS must efficiently process and properly answer ontological queries. The key to processing the ontological queries is whether the various semantic relationships among the concepts of the product ontology are likewise well-processed. In particular, the transitive relationships (e.g., is-a, component-of relationships) such as ancestors-descendents, parents-children, and taxonomy of products must be processed successfully. We propose an efficient index using a numbering scheme (labeling scheme) to process queries over transitive relationships. (This paper is an extended version o…

Transitive relationInformation retrievalComputer Networks and CommunicationsRelational databasecomputer.internet_protocolComputer scienceSearch engine indexingInformationSystems_DATABASEMANAGEMENTOntology (information science)computer.software_genreNumberingDatabase indexNumbering schemeIndex (publishing)Relational database management systemArtificial IntelligenceTaxonomy (general)Product (mathematics)OntologycomputerSoftwareXML2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
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Projective unification in transitive modal logics

2018

We show that a transitive normal modal logic L enjoys projective unification (i.e. each unifiable formula is projective) if and only if L contains K4D1 ( D1 : ( x → y ) ∨ ( y → x ) ). It means, in particular, that K4D1 (and any of its extensions) is almost structurally complete, i.e. the logic is complete with respect to all non-passive admissible rules. We also characterize non-unifiable formulas and provide an explicit form of the basis for all passive rules over K4G + ( x → x )

Transitive relationPure mathematicsUnificationunificationLogic010102 general mathematics02 engineering and technology01 natural sciencescanonical modelModal0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingprajective unifier0101 mathematicsProjective testMathematicsmodal logicLogic Journal of the IGPL
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Probabilistic semantics for categorical syllogisms of Figure II

2018

A coherence-based probability semantics for categorical syllogisms of Figure I, which have transitive structures, has been proposed recently (Gilio, Pfeifer, & Sanfilippo [15]). We extend this work by studying Figure II under coherence. Camestres is an example of a Figure II syllogism: from Every P is M and No S is M infer No S is P. We interpret these sentences by suitable conditional probability assessments. Since the probabilistic inference of \(\bar{P}|S\) from the premise set \(\{M|P,\bar{M}|S\}\) is not informative, we add \(p(S|(S \vee P))>0\) as a probabilistic constraint (i.e., an “existential import assumption”) to obtain probabilistic informativeness. We show how to propagate the…

Transitive relationSequenceSettore MAT/06 - Probabilita' E Statistica MatematicaProbabilistic logicSyllogismConditional probability02 engineering and technologyCoherence (philosophical gambling strategy)Imprecise probabilityCombinatoricscoherence conditional events defaults generalized quantifiers imprecise probability.020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingCategorical variableMathematics
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Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care

2020

Personalized medicine is a new paradigm of healthcare in which interventions are based on individual patient characteristics rather than on “one-size-fits-all” guidelines. As epidemiological datasets continue to burgeon in size and complexity, powerful methods such as statistical machine learning and artificial intelligence (AI) become necessary to interpret and develop prognostic models from underlying data. Through such analysis, machine learning can be used to facilitate personalized medicine via its precise predictions. Additionally, other AI tools, such as natural language processing and computer vision, can play an instrumental part in personalizing the care provided to patients with …

Traumatic spinal cord injuryPrognosiPsychological interventionPatient characteristicsDiseaseSpinal cord injuryMachine learningcomputer.software_genreSpinal DiseaseMachine Learning03 medical and health sciences0302 clinical medicineArtificial IntelligenceHealth careFunctional StatuMedicineHumansSpine carePrecision MedicineDegenerative cervical myelopathyPrognostic modelsSpinal Cord InjuriesNatural Language ProcessingSpinal Cord Injuriebusiness.industryPrognosisPersonalized medicineFunctional Status030220 oncology & carcinogenesisSurgeryFunctional statusSpinal DiseasesNeurology (clinical)Personalized medicineArtificial intelligenceSpondylosisbusinesscomputerSpinal Cord Compression030217 neurology & neurosurgeryHuman
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Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies

2018

Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …

Treatment responsepositron emission tomographyK-nearest neighborKernel support vector machineComputer scienceNormal tissueK-Fold cross-validation030218 nuclear medicine & medical imagingk-nearest neighbors algorithmLesion03 medical and health sciences0302 clinical medicinetissue classificationmedicineRadiation treatment planningFuzzy C-Mean1707Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPattern recognitionComputer Graphics and Computer-Aided DesignPredictive valueSupport vector machineFuzzy C-MeansPositron emission tomography030220 oncology & carcinogenesisComputer Vision and Pattern RecognitionArtificial intelligencemedicine.symptombusinessPattern Recognition and Image Analysis
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Segmentation and virtual exploration of tracheobronchial trees

2003

Abstract The tracheobronchial tree as part of the lung is part of one of the most important organs of the human body. Inhaled air is distributed to the alveolus where oxygen and carbon dioxide exchange between air and blood takes place. In this paper, we introduce the virtual endoscopy system VIVENDI to perform virtual inspections of tracheobronchial trees based on their segmentation and of the complementing blood vessels. It is based on a hybrid segmentation pipeline which enables the segmentation of vascular and tracheobronchial structures down to the seventh generation of the bronchi.

Tree (data structure)business.industryComputer scienceComputer visionSegmentationGeneral MedicineArtificial intelligenceAnatomyrespiratory systemInhaled airVirtual endoscopybusinessInternational Congress Series
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