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Arsenic Ore Mixture Froth Image Generation with Neural Networks and a Language for Declarative Data Validation

dc.contributor.authorZamayla, Arnel
dc.contributor.authorGreco, Gianluigi
dc.contributor.authorAlviano, Mario
dc.contributor.authorDodaro, Carmine
dc.date.accessioned2024-12-03T10:07:50Z
dc.date.available2024-12-03T10:07:50Z
dc.date.issued2022-04-14
dc.identifier.urihttps://hdl.handle.net/10955/5519
dc.identifier.urihttps://doi.org/10.13126/unical.it/dottorati/5519
dc.descriptionUniversità della Calabria. Dipartimento di Matematica e Informatica Dottorato di Ricerca in Matematica e Informatica. Ciclo XXXIIIen_US
dc.description.abstractComputer vision systems that measure froth flow velocities and stability designed for flotation froth image analysis are well established in industry, as they are used to control material recovery. However flotation systems that has limited data has not been explored in the same fashion bearing the fact that big data tools like deep convolutional neural networks require huge amounts of data. This lead to the motivation of the research reported in the first part of this thesis, which is to generate synthetic images from limited data in order to create a froth image dataset. The image synthesis is possible through the use of generative adversarial network. The performance of human experts in this domain in identifying the original and synthesized froth images were then compared with the performance of the models. The models exhibited better accuracy levels by average on the tests that were performed. The trained classifier was also compared with some of the established neural network models in the literature like the AlexNet, VGG16 ang ResNet34. Transfer learning was used as a method for this purpose. It also showed that these pretrained networks that are readily available have better accuracy by average comapared to trained experts. The second part of this thesis reports on a language designed for data validation in the context of knowledge representation and reasoning. Specifically, the target language is Answer Set Programming (ASP), a logic-based programming language widely adopted for combinatorial search and optimization, which however lacks constructs for data validation. The language presented in this thesis fulfills this gap by introducing specific constructs for common validation criteria, and also supports the integration of consolidated validation libraries written in Python. Moreover, the language is designed so to inject data validation in ordinary ASP programs, so to promote fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid.en_US
dc.language.isoenen_US
dc.publisherUniversità della Calabriaen_US
dc.relation.ispartofseriesINF/01;
dc.subjectArtificial Intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectdata validationen_US
dc.titleArsenic Ore Mixture Froth Image Generation with Neural Networks and a Language for Declarative Data Validationen_US
dc.typeThesisen_US


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