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This category includes resources on artificial intelligence technologies such as expert systems, fuzzy systems, natural language processing, speech recognition, pattern recognition, computer vision, decision-support systems, knowledge bases, and neural networks." }, "prefLabel" : [ { "@language" : "en", "@value" : "Artificial Intelligence" }, { "@language" : "el", "@value" : "Τεχνητή νοημοσύνη" } ], "relatedMatch" : [ "http://id.loc.gov/authorities/subjects/sh85031234", "http://id.loc.gov/authorities/subjects/sh85079324" ] }, { "@id" : "http://semantics.gr/authorities/openarchives-item-types/metaptyxiakh-ergasia", "@type" : "skos:Concept", "broader" : "http://semantics.gr/authorities/openarchives-item-types/Research-Paper-", "exactMatch" : "http://vocab.getty.edu/aat/300077723", "prefLabel" : [ { "@language" : "en", "@value" : "Master thesis" }, { "@language" : "el", "@value" : "Μεταπτυχιακή εργασία" } ] }, { "@id" : "https://www.openarchives.gr/aggregator-openarchives/edm/aggregation/provider/000004-2159_32135%231", "@type" : "ore:Aggregation", "aggregatedCHO" : "https://www.openarchives.gr/aggregator-openarchives/edm/psepheda/000004-2159_32135", "dataProvider" : "Πανεπιστήμιο Μακεδονίας", "isShownAt" : "https://dspace.lib.uom.gr/handle/2159/32135", "provider" : "Greek Aggregator OpenArchives.gr | National Documentation Centre (EKT)", "edm:rights" : { "@id" : "http://creativecommons.org/licenses/by-sa/4.0/" } }, { "@id" : "https://www.openarchives.gr/aggregator-openarchives/edm/psepheda/000004-2159_32135", "@type" : "edm:ProvidedCHO", "contributor" : { "@language" : "el", "@value" : "Ευαγγελίδης, Γεώργιος" }, "creator" : { "@language" : "el", "@value" : "Μάστορας, Ιωάννης" }, "description" : [ { "@language" : "en", "@value" : "Submitted by ΙΩΑΝΝΗΣ ΜΑΣΤΟΡΑΣ (aid23006@uom.edu.gr) on 2025-02-06T10:38:00Z\nNo. of bitstreams: 2\nlicense_rdf: 1025 bytes, checksum: 84a900c9dd4b2a10095a94649e1ce116 (MD5)\nMastorasIoannisMsc2025.pdf: 1458765 bytes, checksum: 2ca6c537733b0d474ba5a82f76e57f3a (MD5)" }, { "@language" : "en", "@value" : "CAR application is an integrated tool created for this thesis, which aims to help users deal \nwith the conventional process of preprocessing data and then prepare them for \ninterpretation prior visualization in an interactive R Shiny interface. As data processing \nbecomes increasingly automated in a variety of domains (e.g., healthcare, social \nsciences, business sectors etc.), CAR is designed to make advanced statistical methods \nand machine learning models readily accessible for users who lack extensive \nprogramming or knowledge on statistics by democratizing the practice of data analysis. \nCAR provides the following key features: Automated data cleaning and imputation \nmethods, customizable data visualizations and comprehensive suite of statistical & \nmachine learning algorithms. Main topics include abstracting the data analysis \nworkflow, ingesting and cleaning data, analyzing them with multiple statistical tests, \nextracting features from several machine learning models or even creating visualizations \nusing custom animations only after a few lines. CAR helps us build a bridge for different \nprofessions to solve real-time big data quality management and automated analysis \nproblems, improving the efficiency of research work in practice. Through performance \ntesting with a real-world dataset, the thesis evaluates CAR's functionality as well as its \nusability and versatility. Overall, this work has the potential to offer immediate and direct \nbenefits in a way that bridges complex data science methodologies all the way over to \nend-user-friendly data exploration of datasets thus enabling better access & policy \nmaking. The creation of the CAR application is a big advance in disseminating state-of\nthe-art data analysis methods to more people who may not have deep technical or \nstatistical expertise. By automatically preprocessing data and analyzing them to derive \nvaluable and meaningful insights, CAR democratizes the field of data science enabling \nexperts as well as novices with little technical knowledge in deriving key takeaways from \ntheir datasets which can be critical for evidence-based decision making across several \ndomains." }, { "@language" : "en", "@value" : "Approved for entry into archive by ΕΛΙΣΑΒΕΤ ΧΑΝΤΑΒΑΡΙΔΟΥ (elisach@uom.edu.gr) on 2025-02-06T10:59:46Z (GMT) No. of bitstreams: 2\nlicense_rdf: 1025 bytes, checksum: 84a900c9dd4b2a10095a94649e1ce116 (MD5)\nMastorasIoannisMsc2025.pdf: 1458765 bytes, checksum: 2ca6c537733b0d474ba5a82f76e57f3a (MD5)" }, { "@language" : "en", "@value" : "Made available in DSpace on 2025-02-06T10:59:46Z (GMT). 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