Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Hurloiu, Iulian" wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    Neuromanagement decision making in facial recognition biometric authentication as a mobile payment technology in retail, restaurant, and hotel business models
    Autorzy:
    Dijmărescu, Irina
    Iatagan, Mariana
    Hurloiu, Iulian
    Geamănu, Marinela
    Rusescu, Ciprian
    Dijmărescu, Adrian
    Powiązania:
    https://bibliotekanauki.pl/articles/19322524.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    neuromanagement decision making
    facial recognition
    biometric authentication
    mobile payment technology
    retail, restaurant, and hotel business
    Opis:
    Research background: With growing evidence of biometric identification techniques as authentication, there is a pivotal need for comprehending contactless payments by use of facial recognition algorithms in retail, restaurant, and hotel business models. Purpose of the article: In this research, previous findings were cumulated showing that harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Methods: Throughout March and November 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms including "facial recognition payment technology", "facial recognition payment system", "facial recognition payment application", "face recognition-based payment service", "facial authentication for mobile payment transactions", and "contactless payment through facial recognition algorithms". As the analyzed research was published between 2017 and 2021, only 187 articles satisfied the eligibility criteria. By removing questionable or unclear findings (limited/nonessential data), results unsubstantiated by replication, too general content, or having quite similar titles, 38, mainly empirical, sources were selected. The Systematic Review Data Repository was harnessed, a software program for the gathering, processing, and analysis of data for our systematic review. The quality of the selected scholarly sources was assessed by employing the Mixed Method Appraisal Tool. Findings & value added: Harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Subsequent attention should be directed to whether perceived value and trust shape customers' adoption of biometric recognition payment devices.
    Źródło:
    Oeconomia Copernicana; 2022, 13, 1; 225-250
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Big data management algorithms in artificial Internet of Things-based fintech
    Autorzy:
    Andronie, Mihai
    Iatagan, Mariana
    Uță, Cristian
    Hurloiu, Iulian
    Dijmărescu, Adrian
    Dijmărescu, Irina
    Powiązania:
    https://bibliotekanauki.pl/articles/19902795.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    big data management algorithms
    artificial intelligence
    Internet of Things
    fintech
    banking
    capital markets
    Opis:
    Research background: Fintech companies should optimize banking sector performance in assisting enterprise financing as a result of firm digitalization. Artificial IoT-based fintech-based digital transformation can relevantly reverse credit resource misdistribution brought about by corrupt relationship chains. Purpose of the article: We aim to show that fintech can decrease transaction expenses and consolidates firm stock liquidity, enabling excess leverage decrease and cutting down information asymmetry and transaction expenses across capital markets. AI- and IoT-based fintechs enable immersive and collaborative financial transactions, purchases, and investments in relation to payment tokens and metaverse wallets, managing financial data, infrastructure, and value exchange across shared interactive virtual 3D and simulated digital environments. Methods: AMSTAR is a comprehensive critical measurement tool harnessed in systematic review methodological quality evaluation, DistillerSR is harnessed in producing accurate and transparent evidence-based research through literature review stage automation, MMAT appraises and describes study checklist across systematic mixed studies reviews in terms of content validity and methodological quality predictors, Rayyan is a responsive and intuitive knowledge synthesis tool and cloud-based architecture for article inclusion and exclusion suggestions, and ROBIS appraises systematic review bias risk in relation to relevance and concerns. As a reporting quality assessment tool, the PRISMA checklist and flow diagram, generated by a Shiny App, was used. As bibliometric visualization and construction tools for large datasets and networks, Dimensions and VOSviewer were leveraged. Search terms were “fintech” + “artificial intelligence”, “big data management algorithms”, and “Internet of Things”, search period was June 2023, published research inspected was 2023, and selected sources were 35 out of 188. Findings & value added: The growing volume of financial products and optimized operational performance of financial industries generated by fintech can provide firms with multifarious financing options quickly. Big data-driven fintech innovations are pivotal in banking and capital markets in relation to financial institution operational efficiency. Through data-driven technological and process innovation capabilities, AI system-based businesses can further automated services.
    Źródło:
    Oeconomia Copernicana; 2023, 14, 3; 769-793
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
    Autorzy:
    Lăzăroiu, George
    Gedeon, Tom
    Rogalska, Elżbieta
    Andronie, Mihai
    Frajtova Michalikova, Katarina
    Musova, Zdenka
    Iatagan, Mariana
    Uță, Cristian
    Michalkova, Lucia
    Kovacova, Maria
    Ștefănescu, Roxana
    Hurloiu, Iulian
    Zabojnik, Stanislav
    Stefko, Robert
    Dijmărescu, Adrian
    Dijmărescu, Irina
    Geamănu, Marinela
    Powiązania:
    https://bibliotekanauki.pl/articles/39832736.pdf
    Data publikacji:
    2024
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    deep and machine learning
    COVID 19
    prediction
    detection
    diagnosis
    organizational management
    hospital
    Opis:
    Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.
    Źródło:
    Oeconomia Copernicana; 2024, 15, 1; 27-58
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies