Search results for " algorithms"

showing 10 items of 612 documents

The Windy clustered prize-collecting arc-routing problem

2011

This paper introduces the windy clustered prize-collecting arc-routing problem. It is an arc-routing problem where each demand edge is associated with a profit that is collected once if the edge is serviced, independent of the number of times the edge is traversed. It is further required that if a demand edge is serviced, then all the demand edges of its component are also serviced. A mathematical programming formulation is given and some polyhedral results including several facet-defining and valid inequalities are presented. The separation problem for the different families of inequalities is studied. Numerical results from computational experiments are analyzed. © 2011 INFORMS.

Arc routingMathematical optimizationMathematical programmingTransportation68W AlgorithmsSeparation problemsCutting plane algorithmsArc routing problems:Informàtica::Informàtica teòrica [Àrees temàtiques de la UPC]Prize-collectingPolyhedral modellingNumerical resultsProfitability indexProfitabilityPolyhedral analysisComputational experimentMATEMATICA APLICADAArc routingCutting plane algorithmValid inequalityAlgorithmsCivil and Structural EngineeringSeparation problemMathematicsMathematicsofComputing_DISCRETEMATHEMATICS
researchProduct

A Probabilistic Analysis to Quantify the Effect of March 11, 2004, Attacks in Madrid on the March 14 Elections in Spain: A Dynamic Modelling Approach

2015

[EN] The bomb attacks in Madrid three days before the general elections of March 14, 2004, and their possible influence on the victory of PSOE (Spanish Workers Socialist Party), defeating PP (Popular Party), have been a matter of study from several points of view (i.e., sociological, political, or statistical). In this paper, we present a dynamic model based on a system of differential equations such that it, using data from Spanish CIS (National Center of Sociological Research), describes the evolution of voting intention of the Spanish people over time. Using this model, we conclude that the probability is very low that the PSOE would have won had the attack not happened.Moreover, after t…

Article SubjectOperations researchlcsh:MathematicsApplied Mathematicsmedia_common.quotation_subjectSociological researchVictoryDynamic modellinglcsh:QA1-939PoliticsEconomyVotingGeneral electionProbabilistic analysis of algorithmsCenter (algebra and category theory)MATEMATICA APLICADAAnalysisMathematicsmedia_commonAbstract and Applied Analysis
researchProduct

A Bacterial-Based Algorithm to Simulate Complex Adaptative Systems

2014

Paper presented at the 13th International Conference on Simulation of Adaptive Behavior which took place at Castellón, Spain in 2014, July 22-25. Bacteria have demonstrated an amazing capacity to overcome envi-ronmental changes by collective adaptation through genetic exchanges. Using a distributed communication system and sharing individual strategies, bacteria propagate mutations as innovations that allow them to survive in different envi-ronments. In this paper we present an agent-based model which is inspired by bacterial conjugation of DNA plasmids. In our approach, agents with bounded rationality interact in a common environment guided by local rules, leading to Complex Adaptive Syste…

Artificial SocietyComputer sciencebusiness.industryComplex Adaptive SystemsArtificial societyBacterial conjugationAutonomous agentCASComplexityCommunications systemBounded rationalityBiblioteconomía y DocumentaciónP2P SocietyBacterial-based AlgorithmsCollective adaptationArtificial intelligenceComplex adaptive systemAdaptation (computer science)businessAlgorithm
researchProduct

Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy

2022

AbstractIt is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper differ…

Artificial neural networkOptimizationResponse surface methodologyArtificial IntelligencePredictive modelMetaheuristic algorithmsIndustrial and Manufacturing EngineeringSoftware
researchProduct

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
researchProduct

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
researchProduct

LogDet divergence-based metric learning with triplet constraints and its applications.

2014

How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn…

AutomatedData InterpretationBiometryFeature extractionhigh dimensional datametric learningPattern RecognitionFacial recognition systemSensitivity and SpecificityMatrix decompositionPattern Recognition Automatedcompressed representationComputer-AssistedArtificial Intelligencecompressed representation; high dimensional data; LogDet divergence; metric learning; triplet constraint; Artificial Intelligence; Biometry; Data Interpretation Statistical; Face; Humans; Image Enhancement; Image Interpretation Computer-Assisted; Pattern Recognition Automated; Photography; Reproducibility of Results; Sensitivity and Specificity; Algorithms; Facial Expression; Software; Medicine (all); Computer Graphics and Computer-Aided DesignImage Interpretation Computer-AssistedPhotographyHumansDivergence (statistics)Image retrievalImage InterpretationMathematicsMahalanobis distancebusiness.industryLogDet divergenceMedicine (all)Reproducibility of ResultsPattern recognitionStatisticalImage EnhancementComputer Graphics and Computer-Aided DesignFacial ExpressionComputingMethodologies_PATTERNRECOGNITIONComputer Science::Computer Vision and Pattern RecognitionData Interpretation StatisticalFaceMetric (mathematics)Pattern recognition (psychology)Artificial intelligencetriplet constraintbusinessSoftwareAlgorithmsIEEE transactions on image processing : a publication of the IEEE Signal Processing Society
researchProduct

On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata

2013

Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-013-0424-x There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the second involves discretizing the probability space in which the LA operates. This paper demonstrates how both of these can be simultaneously utilized, and in particular, by using the family of Bayesian estimates that have been proven to have distinct advantages over their maximum likelihood counterparts. The success of LA-…

Bayes estimatorLearning automataDiscretizationbusiness.industryComputer scienceMaximum likelihoodBayesian probabilityestimator algorithmsBayesian reasoningEstimatorlearning automataBayesian inferencediscretized learningVDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425Artificial Intelligenceε-optimalityArtificial intelligencepursuit schemesbusinessAlgorithm
researchProduct

A Collaborative Filtering Approach for Drug Repurposing

2022

A recommendation system is proposed based on the construction of Knowledge Graphs, where physical interaction between proteins and associations between drugs and targets are taken into account. The system suggests new targets for a given drug depending on how proteins are linked each other in the graph. The framework adopted for the implementation of the proposed approach is Apache Spark, useful for loading, managing and manipulating data by means of appropriate Resilient Distributed Datasets (RDD). Moreover, the Alternating Least Square (ALS) machine learning algorithm, a Matrix Factorization algorithm for distributed and parallel computing, is applied. Preliminary obtained results seem to…

Big Data technologiesLatent factorsSettore INF/01 - InformaticaDrugsMachine learning algorithms
researchProduct

“The datafication and commodification of Italian schools during the Covid-19 crisis. Implications for policy and future research”

2022

Big Data and algorithms increasingly inform public policymaking and institutional practices, producing an impact on people’s everyday life. An emerging body of scholarly research—Critical Data Studies—has been working on this role shedding light on how society’s current platformisation is linked to a much longer privatization and reorganization of the public sector. This chapter intends to reflect on how the Covid-19 pandemic has dramatically accelerated these processes focusing on school education in particular. Health Big Data and apps have been crucial to take concrete measures to fight the pandemic, while platforms have helped organize vaccination rounds. Nevertheless, they have also be…

Big Data algorithms Covid-19 school education platform societySettore SPS/08 - Sociologia Dei Processi Culturali E Comunicativi
researchProduct