Search results for "Machine learning"

showing 10 items of 1464 documents

PROTEIN SECONDARY STRUCTURE PREDICTION: HOW TO IMPROVE ACCURACY BY INTEGRATION

2006

In this paper a technique to improve protein secondary structure prediction is proposed. The approach is based on the idea of combining the results of a set of prediction tools, choosing the most correct parts of each prediction. The correctness of the resulting prediction is measured referring to accuracy parameters used in several editions of CASP. Experimental evaluations validating the proposed approach are also reported.

Set (abstract data type)Bioinformatics Protein PredictionCorrectnessComputer sciencebusiness.industryArtificial intelligenceData miningMachine learningcomputer.software_genreProtein secondary structure predictionbusinessCASPcomputerApplied Artificial Intelligence
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Predicting reservoir water volumes in the Mediterranean area by combining a data-driven approach with seasonal forecasts data

Settore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaMachine learning reservoir volumes drought seasonal forecast
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Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

2022

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has …

Settore ICAR/09 - Tecnica Delle CostruzioniGeneral Materials ScienceBuilding and ConstructionArtificial neural networks Compressive strength Concrete Machine learning Metakaolin Mix designCivil and Structural Engineering
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Fake News. Soluzioni design driven per il citizen journalism

2022

Il progetto PO FESR “Fake News” sviluppa una ricerca che mette in campo le metodologie e gli strumenti progettuali del design della comunicazione e dell’informazione con l’obiettivo di elaborare nuove possibilità di rendere più efficaci i meccanismi automatici di valutazione dell’autenticità, originalità e rilevanza dell’informazione prodotta dal giornalismo partecipativo. Nell’ambito interdisciplinare (giornalismo, informatica, sociologia, etc.) interessato dal progetto, il contributo del design ha articolato un’ampia ricognizione sulle molteplici dimensioni del fenomeno e in particolare sulle interazioni tra design dell’informazione e giornalismo etico e partecipativo. Le connessioni tra …

Settore ICAR/13 - Disegno IndustrialeFake news design dell'informazione machine learning giornalismo partecipativo intelligenza artificiale
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Fake News. Progetto di un algoritmo contro le false verità

2022

As part of the Smart Specialization Strategy (RIS3), funded by the European Union, the Sicilian Region has promoted the Fake News project in agreement with the aim of developing information exchange and comparison tools online. The goal is to concentrate European resources in emerging technological sectors that can really develop in the same region by focusing on the construction of local knowledge, rather than on the transfer of high-cost external technological resources. Fake News project has the main objective the experimentation of advanced ICT technologies (artificial intelligence) applied to the phenomenon of disinformation, through the dialogue between the private entrepreneur (a com…

Settore ICAR/13 - Disegno Industrialefake news participatory journalism information design machine learning
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Integration of a structural features-based preclassifier and a man-machine interactive classifier for a fast multi-stroke character recognition

2003

A transputer-based parallel machine for handwritten character recognition is proposed. An algorithm based on structural features and on a tree classifier was used to accomplish the pre-classification of the unknown sample in order to speed up the recognition process. The algorithm for the final classification is based on the description of the strokes through Fourier descriptors. The learning phase is accomplished through a man-machine interactive process. The proposed system can expand its knowledge base. A special representation of this knowledge base is proposed in order to record a great amount of data in a suitable way. A fast multistroke handwritten isolated character recognition syst…

Settore INF/01 - InformaticaComputer scienceIntelligent character recognitionbusiness.industrySketch recognitionPattern recognitionDocument processingIntelligent word recognitionComputingMethodologies_PATTERNRECOGNITIONFeature (machine learning)Artificial intelligencebusinessClassifier (UML)Man machine systems Character recognition Humans Handwriting recognition Pattern recognition Parallel machines System testing Performance evaluation Prototypes Energy management
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A Medium Level Language for Pyramid Architectures

1989

In the paper a Parallel C Languages for pyramid architectures is described. The concept of context is introduced in order to handle concurrence between processes in massive parallel machines. Feature implementation on the PAPIA-machine are given.

Settore INF/01 - InformaticaComputer scienceSpeech recognitionConcurrencyPyramidFeature (machine learning)ConcurrenceContext (language use)Parallel computingParallel languages Concurrency Image Analysis Pyramids.
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Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis

2012

AbstractThe advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of pre…

Settore INF/01 - InformaticaGeneral Computer Sciencebusiness.industryComputer scienceBioinformaticsModel selectionGeneral statisticsMachine learningcomputer.software_genreTheoretical Computer ScienceComputational biologyAnalysis of massive datasetsMachine learningCluster (physics)Algorithms and data structures General statistics Analysis of massive datasets Machine learning Computational biology BioinformaticsAlgorithms and data structuresAlgorithm designArtificial intelligenceCluster analysisbusinessCompleteness (statistics)computerComputer Science(all)Theoretical Computer Science
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An Active Learning Approach for Classifying Explosion Quakes

2022

In this work, an Active Learning approach for improving the classification of passed seismo-volcanic events is proposed. Here we study the specific case of Explosion Quakes from Stromboli Volcano versus other seismo-volcanic events, recorded as seismograms, and the use of Random Forest as a Classification method. In conformity with the active learning paradigm, the approach recalls the human intervention for the annotation of uncertain data. The uncertainty is established by the event probabilities, predicted by a trained random forest classifier. The human intervention consists of editing and relabelling the data into these main three classes: Explosion Quakes, Non-Explosion Quakes or Non-…

Settore INF/01 - InformaticaMachine Learning for seismo-volcanic eventsActive LearningExplosion Quakes
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M-VIF: A machine-vision based on information fusion

2002

The authors describe a new architecture for machine vision, which is based on information fusion approach. Its general design has been developed by using a formal computation model that integrates three main ingredients of the visual computation: the data, the models, and the algorithms. The hardware design and the software environment of M-VIF are also given. The simulation of M-VIF is under development on the HERMIA-machine.

Settore INF/01 - InformaticaMachine visionComputer sciencebusiness.industryComputationMachine learningcomputer.software_genreAbstract machineInformation fusionSoftwareComputer architectureactive vision machine vision image analysis parallel processing.Artificial intelligenceArchitecturebusinesscomputer
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