Search results for "Ranking"

showing 10 items of 212 documents

Application of the Morris method for screening the influential parameters of fuzzy controllers applied to wastewater treatment plants

2011

In this paper,we evaluate the application of a sensitivity analysis to help fine-tuning a fuzzy controller for a biological nitrogen and phosphorus removal (BNPR) plant. TheMorris Screeningmethod is proposed and evaluated as a prior step to obtain the parameter significance ranking. First, an iterative procedure has been performed in order to find out the proper repetition number of the elementary effects (r) of the method. The optimal repetition number found in this study (r = 60) is in direct contrast to previous applications of the Morris method, which usually use low repetition number, e.g. r = 10 ~ 20. Working with a non-proper repetition number (r) could lead to Type I error (identify…

EngineeringParameterFuzzy controllersWastewater treatmentWastewaterScreening methodChemicals removal (water treatment)Parameter significance rankingWaste ManagementWastewater treatment plantsStatisticsWater treatmentFalse positiveControl systemWater Science and TechnologyControllersPhosphorusMorris methodFine-tuningError analysisPollutant removalFuzzy mathematicsCalibrationFalse negativesScreeningSensitivity analysisType I and type II errorsOptimizationWastewater treatment plant (WWTP)Environmental EngineeringWaste water treatment plantNitrogenIterative proceduresNumerical methodRepetition NumberFuzzy logicSewage pumping plantsArticleFalse positive resultFuzzy LogicControl theoryMorris methodSensitivity (control systems)Water treatment plantsBiological water treatmentFalse negative resultTECNOLOGIA DEL MEDIO AMBIENTEBiological nitrogen and phosphorus removalType II errorToxicitybusiness.industryNitrogen removalFuzzy mathematicsRankingFuzzy controllerType-I errorbusiness
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A combined multi-criteria approach to support FMECA analyses: A real-world case

2018

[EN] The paper proposes an approach that combines reliability analyses and multi-criteria decision methods to optimize maintenance activities of complex systems. A failure mode, effects, and criticality analysts (FMECA) is initially performed and the fuzzy TOPSIS (FTOPSIS) method is then applied to rank previously identified failure modes. For prioritization, failure modes are assessed against three evaluation criteria that differ from those traditionally involved in risk priority number (RPN) computation (i.e. severity, occurrence and detection). Two criteria refer to the maintenance management reflecting the operational time taken by the maintenance activity performed after the occurrence…

EngineeringSafety-critical analysisSafety-critical analysiAHP0211 other engineering and technologiesAnalytic hierarchy process02 engineering and technologyFault (power engineering)Industrial and Manufacturing EngineeringSettore ING-IND/17 - Impianti Industriali Meccanici0202 electrical engineering electronic engineering information engineeringFTOPSISSensitivity (control systems)Safety Risk Reliability and QualityReliability (statistics)021103 operations researchbusiness.industryRank (computer programming)Reliability engineeringFailure mode effects and criticality analysisRanking020201 artificial intelligence & image processingMATEMATICA APLICADAbusinessFailure mode and effects analysisFMECAReliability Engineering & System Safety
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Decision making in energy planning: The electre multicriteria analysis approach compared to a fuzzy-sets methodology

1998

Every planning activity generally requires to make some choices. After a preliminary analysis of the sector under examination, a forecast of trends of input-output items, the planner must define an action plan voted to arrange all the strategies and specific interventions able to fit demand and supply during the planned time. The redaction of an action plan implies a strong effort in order to synthesize either suggestions coming from the analysis phases either all the constraints linked to technical choices. In the same time, a large number of "external" variables plays a role in orienting decision making. Some of these can be handled by numerical models (economic cost-benefit analyses, mar…

Engineeringdecision making energy planningSettore ING-IND/11 - Fisica Tecnica AmbientaleOperations researchRenewable Energy Sustainability and the Environmentbusiness.industryFuzzy setJudgementEnergy Engineering and Power TechnologyEnergy planningFuzzy logicFuel TechnologyNuclear Energy and EngineeringRankingOrder (exchange)Action planELECTREbusiness
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Ensemble methods for item-weighted label ranking: a comparison

2022

Label Ranking (LR), an emerging non-standard supervised classification problem, aims at training preference models that order a finite set of labels based on a set of predictor features. Traditional LR models regard all labels as equally important. However, in many cases, failing to predict the ranking position of a highly relevant label can be considered more severe than failing to predict a trivial one. Moreover, an efficient LR classifier should be able to take into account the similarity between the items to be ranked. Indeed, swapping two similar elements should be less penalized than swapping two dissimilar ones. The contribution of the present paper is to formulate more flexible item…

Ensemble methodsRanking dataLabel rankingSettore SECS-S/01 - Statistica
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WORDY: a Semi-automatic Methodology aimed at the Creation of Neologisms based on a Semantic Network and Blending Devices

2017

In this paper, we propose a semi-automatic tool, named WORDY, that implements a methodology aimed at speeding-up the pro- cess of creation of neologisms. The approach exploits a semantic network, which is explored through the spreading activation methodology and ex- ploits three blending linguistic techniques together with a proper ranking function in order to support companies in the creation of neologisms ca- pable of evoking semantic meaningful associations to customers.

ExploitNeologismsComputer scienceProcess (engineering)media_common.quotation_subject02 engineering and technologySemantic networkscomputer.software_genre050105 experimental psychologySemantic networkRanking (information retrieval)Creativity0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesFunction (engineering)Neologismmedia_commonbusiness.industry05 social sciencesCreativityBlending020201 artificial intelligence & image processingArtificial intelligenceSemi automaticbusinesscomputerNatural language processing
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Distributed and proximity-constrained C-means for discrete coverage control

2018

In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks. The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations. To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs. The proposed co…

FOS: Computer and information sciences0209 industrial biotechnologyControl and OptimizationComputer scienceDistributed computing02 engineering and technologyIndustrial and Manufacturing EngineeringSet (abstract data type)Disaster reliefComputer Science - Robotics020901 industrial engineering & automation0202 electrical engineering electronic engineering information engineeringDecision Sciences (miscellaneous)Cluster analysisData fusion processPoints of interest(poi)Sensing rangesNon-exclusive clusteringData fusionDisaster preventionSensor fusionEuclidean distanceCoverage controlIdentification (information)Range (mathematics)Information concerningRanking020201 artificial intelligence & image processingMobile agentsRobotics (cs.RO)Cluster centroids
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Mislabel Detection of Finnish Publication Ranks

2019

The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM). The purpose is to introduce and test recently proposed ELM-based mislabel detection approach with a rich set of features characterizing a publication channel. We will compare the architecture, accuracy, and, especially, the set of detected mislabels of the ELM-based approach to the corresponding reference results on the reference paper.

FOS: Computer and information sciencesComputer Science - Machine LearningComputer sciencerankinglistatMachine Learning (stat.ML)computer.software_genreMachine Learning (cs.LG)Set (abstract data type)Statistics - Machine LearningDigital Libraries (cs.DL)julkaisukanavatvirheanalyysimislabel detectionExtreme learning machineExtreme Learning Machine (ELM)publication channelsComputer Science - Digital LibrariesData setkoneoppiminendataData miningrankingsarviointicomputertieteellinen julkaisutoimintaCommunication channel
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Elites, communities and the limited benefits of mentorship in electronic music

2020

AbstractWhile the emergence of success in creative professions, such as music, has been studied extensively, the link between individual success and collaboration is not yet fully uncovered. Here we aim to fill this gap by analyzing longitudinal data on the co-releasing and mentoring patterns of popular electronic music artists appearing in the annual Top 100 ranking of DJ Magazine. We find that while this ranking list of popularity publishes 100 names, only the top 20 is stable over time, showcasing a lock-in effect on the electronic music elite. Based on the temporal co-release network of top musicians, we extract a diverse community structure characterizing the electronic music industry.…

FOS: Computer and information sciencesPhysics - Physics and SocietyLongitudinal dataFOS: Physical scienceslcsh:MedicinePhysics and Society (physics.soc-ph)Musical01 natural sciencesArticle010305 fluids & plasmasMentorshipElectronic music0103 physical sciencesSociology010306 general physicslcsh:ScienceSocial and Information Networks (cs.SI)Multidisciplinarysocial physics complex networksComputational sciencelcsh:RMedia studiesScientific dataComputer Science - Social and Information NetworksPopularitySettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Applied physicsRankingElitelcsh:QScientific Reports
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Aggregation of the web performance of internal university units as a method of quantitative analysis of a university system: the case of Spain

2013

The aggregation of web performance data (page count and visibility) of internal university units could constitute a more precise indicator than the overall web performance of the universities and, therefore, be of use in the design of university web rankings. In order to test this hypothesis, a longitudinal analysis of the internal units of the Spanish university system was conducted over the course of 2010. For the 13,800 URLs identified, page count and visibility were calculated using the Yahoo! API. The internal values obtained were aggregated by university and compared with the values obtained from the analysis of the universities' general URLs. The results indicate that, although the c…

FOS: Computer and information sciencesURLCOMUNICACION AUDIOVISUAL Y PUBLICIDADBIBLIOTECONOMIA Y DOCUMENTACIONComputer Science - Digital LibrariesWebometricsDigital Libraries (cs.DL)Ranking
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Fair Pairwise Learning to Rank

2020

Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy, it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.In this work we present a family of fair pairwise learning to rank approaches based on Neur…

FairnessArtificial neural networkNeural Networksbusiness.industryComputer science05 social sciencesRank (computer programming)02 engineering and technologyMachine learningcomputer.software_genreFairness Neural Networks RankingOutcome (game theory)Ranking (information retrieval)Correlation020204 information systems0202 electrical engineering electronic engineering information engineeringRelevance (information retrieval)Learning to rankProduct (category theory)Artificial intelligenceRanking0509 other social sciences050904 information & library sciencesbusinesscomputer
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