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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    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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    Combining virtual learning environment and integrated development environment to enhance e-learning
    (IEEE, 2007-06-25)
    Fetaji, Majlinda
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    ;
    Fetaji, Bekim
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    Ebibi, Mirlinda
    The research was undertaken having in consideration two hypothesis. The first hypothesis is that integration of virtual learning environment (VLE) and integrated developing environment (IDE) for programming in Java language will contribute in improving the efficiency and quality in learning because of the enhanced graphical user interface and the “hands on approach”. 7KHVHFRQGK\SRWKHVLVLVWKDWWKHGHVLJQHG graphical user interface of the virtual learning environment will contribute in facilitating its use by improving the results of the learning process, increasing the user-satisfaction and attention during learning that implicates improving the overal efficiency of learning programming in Java. The aim of this paper was to discuss difficulties and disadvantages of learning programming in traditional method, and to investigate for new e-learning strategies by combining e-learning and developing environment whereas the importance of the pedagogical approach is discussed and one is adopted in the design. The usability of the created virtual environment was reviewed, in order to assess and propose solutions to the identified issues.