Search results for "learning"

showing 10 items of 6669 documents

Understanding social behavior evolutions through agent-based modeling

2012

Agent-based social simulation as a computational approach to social simulation has been largely used to explore social phenomena. The purpose of this paper is to describe a theoretical model of transmission and evolution of social behaviors in a network of artificial societies (artificial world) using agent-based modeling technology. In this model, each agent (society) is subdivided into social behaviors where individual and social learning occur. The agent-agent interactions are carried out by their social behaviors; otherwise the agent-environment interactions through consumption of ecological resources by its social behaviors in repression and satisfaction. We distinguish social behavior…

Artificial worldSocial dynamicsGlobalizationManagement scienceComputer sciencebusiness.industryMulti-agent systemArtificial intelligencebusinessSocial learningSocial heuristicsSocial behaviorSocial simulation2012 International Conference on Multimedia Computing and Systems
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The effectiveness of compositional animation design: Evidence from eye tracking

2017

Communication donnée le 1er septembre 2017 lors du symposium : Eye tracking as a method in learning and testing with different representations(session L 8); International audience; Learners have difficulty in decomposing conventionally designed animations to obtain raw material suitable for building high quality mental models. A composition approach to designing animations based on the Animation Processing Model was developed as a principled alternative to prevailing approaches. It provides learners with pre-decomposed material that is structured and sequenced to facilitate the relation building required for effective mental model construction. Study of a compositional animation that presen…

Assessment methods and toolsProblem solvingInstructional design[SCCO.PSYC]Cognitive science/Psychology[SCCO.PSYC] Cognitive science/PsychologyMixed-method research[ SCCO.PSYC ] Cognitive science/PsychologyLearning TechnologiesReading comprehensionComputer-assisted learningComprehension of text and graphicsMathematicsMultimedia learning
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Caracterización del curriculum evaluado en matemática en sexto año básico. Un estudio descriptivo en Valparaíso, Chile

2014

Este artículo pretende dar cuenta de los principales resultados de la investigación denominada Caracterización del curriculum evaluado en sexto año básico en matemática: orientaciones para la formación inicial y continua de profesores y profesoras, cuyo objetivo principal fue describir y analizar lo que se evalúa y cómo se evalúa en matemática en dicho nivel en la región de Valparaíso, Chile. Se analizaron 103 pruebas escritas de matemática conducentes a calificación, pertenecientes a 27 establecimientos educacionales. A dichas pruebas, y a sus respectivas 2516 preguntas, se les aplicó un conjunto de códigos referido tanto a aspectos formales como de contenidos y habilidades matemáticas. S…

Assessment; assessment of learning; mathematics; assessment impact; written tests; gradingEvaluaciónEvaluación; Evaluación del aprendizaje; matemática; impacto de la evaluación; pruebas escritas;calificaciónassessment of learningmathematicswritten testsEvaluación del aprendizajegradingpruebas escritasAssessmentimpacto de la evaluaciónpedagogía; educación; evaluaciónEducationmatemáticacalificaciónpedagogíaeducaciónassessment impact
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3D Matrix-Based Visualization System of Association Rules

2017

With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the …

Association rule learningComputer sciencevisualisointi02 engineering and technologycomputer.software_genreMachine learningassociation rulesvisualisationInformation visualizationData visualization0202 electrical engineering electronic engineering information engineeringZoom3D matrixta113business.industry020207 software engineeringdata miningVisualizationHuman visual system modelScalability020201 artificial intelligence & image processingData miningArtificial intelligencetiedonlouhintabusinesscomputerTransaction data2017 IEEE International Conference on Computer and Information Technology (CIT)
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Discovering representative models in large time series databases

2004

The discovery of frequently occurring patterns in a time series could be important in several application contexts. As an example, the analysis of frequent patterns in biomedical observations could allow to perform diagnosis and/or prognosis. Moreover, the efficient discovery of frequent patterns may play an important role in several data mining tasks such as association rule discovery, clustering and classification. However, in order to identify interesting repetitions, it is necessary to allow errors in the matching patterns; in this context, it is difficult to select one pattern particularly suited to represent the set of similar ones, whereas modelling this set with a single model could…

Association rule learningDiscretizationComputer scienceContext (language use)Correlation and dependencecomputer.software_genreSet (abstract data type)CardinalityKnowledge extractionMotif extraction Pattern discoveryPattern matchingData miningCluster analysisTime complexitycomputer
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Predicting hospital associated disability from imbalanced data using supervised learning.

2019

Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algo…

Association rule learningmedicine.medical_treatmentvanhuksetMedicine (miscellaneous)sairaalahoitoOutcome (game theory)Task (project management)03 medical and health sciences0302 clinical medicineArtificial IntelligenceMedicineHumanstoimintarajoitteetDisabled PersonsSet (psychology)Adverse effectFinlandta316030304 developmental biologyAgedta1130303 health sciencesRehabilitationbusiness.industrySupervised learningennusteetta3142medicine.diseaseMedical researchHospitalizationmachine learningkoneoppiminenhospital associated disabilityMedical emergencySupervised Machine Learningtiedonlouhintabusiness030217 neurology & neurosurgeryrandom forestArtificial intelligence in medicine
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Analyzing the Correlation of Classical and Community-aware Centrality Measures in Complex Networks

2021

International audience; Identifying influential nodes in social networks is a fundamental issue. Indeed, it has many applications, such as inhibiting epidemic spreading, accelerating information diffusion, preventing terrorist attacks, and much more. Classically, centrality measures quantify the node's importance based on various topological properties of the network, such as Degree and Betweenness. Nonetheless, these measures are agnostic of the community structure, although it is a ubiquitous characteristic encountered in many real-world networks. To overcome this drawback, there is a growing trend to design so-called community-aware centrality measures. Although several works investigate…

AssortativityTransitivityEfficiency) and nine mesoscopic topological features (MixingAverage Distance[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI]Density[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][INFO] Computer Science [cs][INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Diameter[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]Influential NodesCentrality Measures[INFO]Computer Science [cs]Community StructureComputingMilieux_MISCELLANEOUS
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POSSIBILITIES OF USING DIALECT ATLASES IN THE ACQUISITION OF LATVIAN LANGUAGE

2020

The creation of a modern lesson increasingly uses open data, a variety of digital resources, electronic teaching tools etc. In addition to books, electronic teaching tools, information resources, etc., come into the training process as a way to portray linguistic information and the writers' creative thought. One of the resources that can be successfully exploited in promoting the growth of a fully developed and skilled pupil, as well as in-depth learning of the Latvian language, is dialect atlases. The aim of the research was to understand the functionality and effectiveness of the dialect atlases in lear ning lexicology and dialectology. The study combined the descriptive method and conte…

Atlas of the Latvian Dialects; digital resources; teaching/learning Latvian languageContent analysisLexicologyDialectologylanguageLatvianPhoneticsSociologyLexiconCompetence (human resources)language.human_languageLinguisticsLinguistic competenceSOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference
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A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

2020

The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV li…

Atmospheric ScienceAtmospheric chemistry010504 meteorology & atmospheric sciencesneural networkAnalytical chemistry010501 environmental sciences01 natural sciencesTropospherelcsh:Chemistrychemistry.chemical_compoundMESSyErdsystem-ModellierungMixing ratioTropospheric ozoneIsopreneNOx0105 earth and related environmental sciencesEMAChydroxyl radicalPhotodissociationlcsh:QC1-999Atmospheric chemistry neural networkmachine learningchemistrylcsh:QD1-99913. Climate actionCCMI[SDE]Environmental SciencesHydroxyl radicalWater vaporlcsh:Physicsmethane lifetime
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Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

2022

Atmospheric Scienceprecision farmingradiative transfer modelsApplied Mathematicsplant nitrogen uptake estimationComputers in Earth Sciencesmachine learning regression algorithmsGeneral Environmental ScienceEuropean Journal of Remote Sensing
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