Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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Author: search.filters.author.Gouveia, António Jorge

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    Overcoming technical barriers in healthcare with blockchain: A systematic review
    (Elsevier BV, 2026-03)
    Costa, Diana
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    Coelho, Paulo Jorge
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    Albuquerque, Carlos
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    Pires, Ivan Miguel
    The rising digitalization of healthcare has increased reliance on complex information systems, creating the need for better integration and interoperability. Despite technology developments, healthcare organizations still confront technical challenges that restrict data transmission, scalability, and secure information sharing. This systematic review highlights important technological challenges to healthcare information system integration and examines the potential of blockchain technology to address them. Following PRISMA principles, a structured search of PubMed and Scopus revealed 24 peer-reviewed studies published from 2020 to 2024. The investigation suggests that interoperability restrictions, lack of data and language standardization, scalability challenges, cybersecurity hazards, and insufficient technical expertise are the most significant hurdles. Evidence suggests that blockchain technology can increase data integrity, security, and regulated interoperability through decentralized and permissioned systems. However, obstacles persist involving technical complexity, regulatory compliance, energy consumption, and organizational readiness. This paper highlights current knowledge on technical integration hurdles in healthcare and presents evidence-based insights on the potential role of blockchain in allowing interoperable, safe, and sustainable healthcare information systems.
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    Corrigendum to “Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up”[Data in Brief, volume 46 (2023) 108874]
    (Elsevier, 2023-04-01)
    Duarte, Rui Pedro
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    Marinho, Francisco Alexandre
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    Bastos, Eduarda Sofia
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    Pinto, Rui João
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    Silva, Pedro Miguel
    This work is funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020. This work is also funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/00308/2020. Hanna Vitaliyivna Denysyuk is funded by the Portuguese Foundation for Science and Technology under scholarship number 2021.06685. BD
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    Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
    (Elsevier, 2023-02-01)
    Vitaliyivna Denysyuk, Hanna
    ;
    Pinto, Rui Joao
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    Silva, Pedro Miguel
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    Duarte, Rui Pedro
    ;
    Marinho, Francisco Alexandre
    The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.