Examinando por Autor "Psaila, Giuseppe"

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    BlockChain platforms in financial services: current perspective
    (Sciendo, 2020-12-03) García Bringas, Pablo; Pastor López, Iker; Psaila, Giuseppe
    BlockChain technology was invented to support bitcoin, currently the most popular virtual currency. The purpose of this paper is to investigate contemporary BlockChain platforms in financial services. An unstructured literature review has been used. BlockChain in financial services is mostly associated with bitcoin exchange. However, this is a partial view of both BlockChain technology and its possible adoption for financial services: In fact, many BlockChain platforms are now available and many different financial services can be effectively supported by BlockChain platforms, even though they are not based on virtual-money exchange. Furthermore, people are attracted by the concept of smart contract, i.e., a contract that is automatically executed by computer technology, without human intervention. The contribution of this paper is twofold: First of all, we introduce the four BlockChain platforms that are now most popular, discussing how they support the smart contract concept; second, we identify some typical categories of financial services, matching each of them with the platform that provides the best support for each category.
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    Quality assessment methodology based on machine learning with small datasets: industrial castings defects
    (Elsevier B.V., 2021-10-07) Pastor López, Iker; Sanz Urquijo, Borja; Tellaeche Iglesias, Alberto; Psaila, Giuseppe; Gaviria de la Puerta, José; García Bringas, Pablo
    Nowadays there are numerous problems for which use of a multi-objective in image classification would be desirable although, unfortunately, the number of samples is too low. In these situations, higher level classifications could also work (for example, in surface defect detection, it is important to identify the defect, but it could also be useful to detect whether or not the object has a defect). To this end, we present a methodology called BoDoC which allows to improve this classification. To evaluate the methodology, we have created a new dataset, obtained from a foundry, to detect surface errors in casting pieces with 2 different defects: (i) inclusions, (ii) coldlaps and (iii) misruns. We also present a collection of techniques to select featu res from the images. We prove that our methodology improves the direct classification results in real world scenarios, with 91.305% precision.