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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    Acquiring experience in pathology predominantly from what you see, not from what you read: the HIPON e-learning platform
    (Informa UK Limited, 2015-06)
    Riccioni, Olga
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    Vrasidas, Charalambos
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    Brcic, Luka
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    Seiwerth, Sven
    It is indisputable that nowadays one of the hardest and most important tasks in medicine and especially in medical education, is the conversion of the extensive amount of available data, into medical experience, after a proper analysis. A project under the title “ICT (Information and Communication Technology) eModules on HistoPathology: a useful online tool for students, researchers and professionals – HIPON”, co-financed by the Lifelong Learning Program of the Education, Audiovisual and Culture Executive Agency (EACEA), The Commission of the European Union, has been launched at the beginning of 2013. HIPON’s purpose is not to provide just another pathology website atlas, but to convey professional experience and thinking in pathology. HIPON has resulted in a well-structured and user-friendly, open resource, multi-language, e-learning platform which, taking advantage of modern image technology, offers medical students, researchers, and professionals a valuable teaching instrument so that they can acquire professional experience in pathology. The mid-term report of HIPON has been favorably evaluated by the EACEA experts who appreciated the potential of our teaching tool in providing the opportunity and the means to acquire medical experience. Through the use of virtual slides, educative videos and microscopic, high resolution, marked images accompanied by relevant questions and answers, HIPON project aims to make end-users able to think as experienced pathologists and become highly efficient in correlating pathologic data with other clinical-laboratory information.
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    Scalable architecture of e-ordering system in cloud
    (IEEE, 2015-05)
    Dimitrievski, Filip
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    Ristov, Sasko
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    Todays dynamic economy enforces the companies to dismiss their minor-business departments and to turn the efforts towards the main business. This is emphasized especially to small and start-up companies, which would like to order the issues as a service from third parties as OPEX (Operational Expenditure), rather than investing in their own departments as CAPEX (Capital Expenditure). Even more, they expect to achieve their orders as faster as possible. Since the number of such companies is floating, as well as the number of their employees, these requirements demand a scalable system for e-ordering. Such a system will allow the consumer companies to order some services from the sellers. In this paper we present scalable architecture in the cloud, which can be offered as a service both to the consumers (buyers) and clients (sellers), along with two business models according to the market in a particular region or state.
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    Scalable system for e-orders as a service in cloud
    (IEEE, 2015-09)
    Ristov, Sasko
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    Dimitrievski, Filip
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    Cloud market grows each year and companies' dilemma is when and where to migrate their services and applications. Companies can migrate the whole application together with its data, or move only data to some application or service that is already prepared in the cloud. Companies have challenges in either way. For the former, the company should redevelop its application to be applicable to the cloud architecture of the cloud service provider, while it should develop some adapter or make some data transformation for the latter. This paper focuses on the migration of the application along with its data and presents the development of scalable and portable system for e-ordering, which can be offered as a service in the cloud. The system orchestrates the resources dynamically and groups buyers and sellers according to the needs, thus reducing the database size and optimizing the cost and performance. Series of experiments are conducted to prove the scalability of the system hosted in the scalable Eucalyptus cloud framework. The results show a phenomenon, that is, superlinear improvement (greater than the scaled resources) of the performance while scaling the resources. The new architecture achieves speedup of up to 20 while scaling the resources only with factor four. Also, superlinearity appears in the number of handled requests.
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    E-Testing question development technologies and strategies
    (IEEE, 2015-03)
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    Ristov, Sasko
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    Advanced learning technologies integrate questions as a very relevant function in building e-Testing systems and especially in intelligent tutoring systems. Therefore, a huge challenge is the generation of a necessary and relevant assessment content. The existing efforts to realize open source learning materials and establishment of massive open online courses introduce another challenge for realization of a sophisticated system and appropriate knowledge database with huge number of questions reflecting all relevant knowledge items (learning objectives). In this paper we present the experience in realization of a real e-Testing system by building two variations: the first with standard graphical interface, and the other with an enhanced media interaction supporting interactive images. We present several question generation strategies for conventional approach using multiple-choice questions and also for the system with interactive images. These techniques can be used to develop a large set of questions and assessment content.
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    Implementation of Experimental Learning in Pathology: Impact of HIPON Project Concept and Attainment
    (Springer Science and Business Media LLC, 2015)
    Lazaris, Andreas C.
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    Riccioni, Olga
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    Solomou, Maria
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    Nikolakopoulos, Ilias
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    Vemmou, Evangelia
    Background: In pathology training, one of the current, hardest and most important tasks is the conversion of the extensive amount of available data into medical experience. This challenge is linked with an innovative project entitled “ICT emodules on HistoPathology: a valuable online tool for students, researchers and professionals-HIPON”. Aim & Objectives: The project has resulted in a multi-language elearning platform which aims to imprint professional experience in a way that medical students, researchers and professionals can develop their own necessary practical dexterities in the huge field of modern Pathology. Methods: The basic concept underling HIPON’s methodology is the introduction of experiential learning based on real cases. Experiential learning is a process through which students develop knowledge, skills, and values from direct experiences; the key element is the student, and knowledge is gained as a result of being personally involved in the pedagogical approach. Results: By implementing experiential learning, there is a move to a more student-centered view of learning. The educator’s most important responsibility becomes to search out and construct meaningful educational experiences that allow students to solve real-world problems; the result is that any abstract, inert knowledge that students used to memorize from dusty textbooks comes alive as they participate in the practical application of knowledge.
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    FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework With Early Warning Systems for Mitigating Online Financial Fraud: A Case Study From North Macedonia
    (Institute of Electrical and Electronics Engineers (IEEE), 2025)
    Fetaji, Bekim
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    Fetaji, Majlinda
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    Hasan, Affan
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    Rexhepi, Shpetim
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    Online financial fraud remains a pervasive threat, incurring billions of dollars in global losses annually. Mid-sized markets, such as North Macedonia, face acute challenges as digital adoption in the Banking, Financial Services, and Insurance (BFSI) sector outpaces the establishment of robust, multi-layered security systems. This paper introduces FRAUD-X, a unified framework merging artificial intelligence (AI)–based anomaly detection, blockchain-driven transaction verification, cybersecurity intrusion detection, and real-time early warning mechanisms into a single pipeline. Drawing upon three datasets—a Credit Card Fraud dataset (Kaggle), the PaySim Mobile Money dataset, and collected 50,000 anonymized local BFSI transactions from North Macedonia—FRAUD-X demonstrates a ~2–4% improvement in F1 compared to single-plane AI approaches, with ~90% recall for zero-day threats. Key enhancements include: 1) a permissioned blockchain for tamper-proof ledger entries, 2) synergistic AI-cybersecurity integration for dynamic risk scoring, and 3) real-time alerts that reduce reaction windows from hours to mere minutes. The framework runs at ~15–16 ms per transaction (~33% CPU usage), supporting near-real-time BFSI operations. Ablation studies confirm that each synergy layer (blockchain, cybersecurity, and early warning) significantly contributes to overall performance. A security analysis illustrates how FRAUD-X mitigates node compromise, collusion attempts, and advanced persistent threats (APT). By providing a replicable roadmap that balances high detection accuracy with operational feasibility, FRAUD-X offers practical value to BFSI entities in North Macedonia and comparable mid-scale markets.
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    Assessing Personalized Engineering Learning Experience with a Multi-Modal AI Tutoring Framework
    (IEEE, 2025-06-02)
    Fetaji, Bekim
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    Fetaji, Majlinda
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    Fetaji, Fjolla
    This paper presents an innovative and novel multimodal AI tutoring framework in the effort to increase the effectiveness of personalized engineering learning experiences. Filling the gaps in adaptive tutoring systems and considering people's emotional engagement, the framework combines the cognitive load theory, indicators of emotional intelligence, and adaptive learning algorithms to develop an overarching, context-specific instructional landscape. The study makes use of a mixed-methods design, using machine learning driven tutoring interfaces, state of the art learning analytics, and sentiment analysis on a long-term study with 68 engineering students. Incorporating powerful affective computing techniques and intelligent intervention methods, this study presents an encompassing approach that can tune the scaffolding support to learners' abilities and adjust to everchanging competencies of learners while being proactive about detecting the indicators of cognitive overload. The uniqueness aspect of this research lies in its synergistic combination of various data streams (multimodal) - text, visual, and biometric data - combined with dynamic AI-based recommendation model, which maximizes personalized feedback loops. Theoretically, the study expands the insights into how the cognitive load and the emotional dynamics form learning outcomes. Practically, it provides a scalable, flexible approach to the integration of multimodal AI tutors in various educational environments. This work helps to bridge the gap between the cognitive science principles and educational technology solutions and offers new findings regarding designing effective, user-centric intelligent tutoring systems that increase knowledge retention, create motivation, and increase the engineering education outcomes.
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    Technologies for Interactive Learning and Assessment Content Development
    (IGI Global, 2016-01-01)
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    Ristov, Sasko
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    <p>Recent technology trends evolved the student assessment from traditional ones (“pen-and-paper” and “face-to-face”) to modern e-Assessment system. These modern approaches allow the teachers to conduct and evaluate an exam with huge number of students in a short period of time. Even more important, both the teacher and the students achieve the evaluation results immediately after the assessment has finished. Although the e-Assessment system speeds up the evaluation, teachers face a huge challenge to prepare, organize and generate a huge set of questions. The questions must cover all learning objectives and their number should be as large as possible to prevent cheating by guessing or memorization of correct answers from previous exams. This paper presents several technologies that can efficiently realize strategies to develop a huge question database with minimal teacher efforts. It also describes the methodologies and strategies based on a specific technology. The technologies are categorized in two classes of e-Assessment systems that are used at the authors' faculty: the traditional e-Assessment system with usual multiple-choice answers and the newest e-Assessment system with interactive images. The question generation is based on defining the questions and answers as XML files (for more advanced users) and MS Word-based files (for users with basic IT background). The question database can be used both for efficient and effective e-Assessment and e-Learning.</p>
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    Interoperability of ECG standards
    (IEEE, 2018-05)
    Stamenov, Dejan
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    The increased demand for interoperability of electronic health records leads to the need for establishing reliable standards for storing and retrieving electrocardiogram data. Multiple medical record data standards were created in response to medical organization initiatives, based on binary and XML formats. These standards raise the need to maximize interoperability between systems that share ECG datasets. HL7 aECG, SCP-ECG, DICOM, and ISHNE are among the most popular ECG standards, which are compared within this paper. We have implemented an adapter system - ECGConvert, which provides interoperability on raw ECG to HL7 aECG and SCP-ECG, while also supporting ISHNE format to HL7 aECG conversion. We provide a discussion for the interoperability of these standards in the healthcare, the problems we have faced and solution to improve the process of sharing ECG datasets between organizations. Our interoperability platform can support the wearable ECG devices that provide single-channel ECG streaming data. The metadata raw ECG record can be converted to any of currently used ECG standards by the built prototype.
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    A meta-heuristic approach for RLE compression in a column store table
    (Springer Science and Business Media LLC, 2018-02-17)
    Jovanovski, Jane
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    Arsov, Nino
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    Stevanoska, Evgenija
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    Siljanoska Simons, Maja
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    Structured data are one of the most important segments in the realm of big data analysis that have undeniably prevailed over the years. In recent years, column-oriented design has become a frequent practice to organize structured data in analytical systems. The storage systems that organize data in a column-wise manner are often referred to as column stores. Column-oriented databases or warehouses and spreadsheet applications in particular have recently become a popular and a convenient tool for column-wise data processing and analysis. At the same time, the volume of data is increasing at an extreme rate, which despite the decrease in pricing of storage systems stresses the importance of data compression. Apart from resounding performance gain in large read-mostly data repositories, column-oriented data are easily compressible, which enables efficient query processing and pushes the peak of the overall performance. Many compression algorithms, including the Run Length Encoding (RLE), exploit the similarity among the column values, where repetitions of the same value form columnar runs that can be found in most database systems. This paper presents a comprehensive analysis and comparison of common and well-known meta-heuristics for columnar run minimization, based on standard implementations by using real datasets. We have analyzed genetic algorithms, simulated annealing, cuckoo search, particle swarm optimization, Tabu search, and the bat algorithm. The first three being the most efficient have undergone sensitivity analysis on synthetic datasets to fine-tune their parameters. These meta-heuristics were then tested on real datasets. The experiments show that the algorithms perform consistently well on both synthetic and real data, demonstrating higher run-reduction efficiency compared to existing approaches. Moreover, the results show that the applied meta-heuristics exhibit quick convergence to nearly optimal solutions, accompanied by an insignificant overhead. In addition, we provide comprehensive implementations of the heuristic RLE compression approaches based on common optimization methods. They are effective at physical compression to an extent that makes them suitable as everyday appliances. The experiments on real datasets also indicate that our implementations are able overcome the expected on-disk file compression ratio, in most cases being better than the respective reduction in runs.