This study presents the development and validation of a real-time optical character recognition (OCR) system for identity verification in residential access control scenarios, specifically evaluating the effects of environmental variability and user interaction on system performance. The proposed methodology integrates a U-Net architecture with a ResNet-34 encoder for semantic document segmentation and utilizes Tesseract OCR for digit recognition, deployed on an embedded platform. Experimental evaluations were performed under diverse lighting conditions, varying camera-to-document distances, and differing document physical states, followed by field validation within a gated community. The system demonstrated optimal segmentation performance with a mean Intersection over Union (IoU) of 0.82 and a digit recognition accuracy of 87.5% under high-illumination conditions at distances ranging from 7 to 30 cm. Furthermore, system usability and user workload were assessed through standardized psychometric instruments, including the System Usability Scale (SUS), the User Experience Questionnaire (UEQ), task-efficiency metrics, and the NASA Task Load Index (NASA-TLX). Results indicated high perceived usability (SUS = 86.5) coupled with moderate cognitive demand. The findings demonstrate that while environmental factors significantly influence algorithmic performance, user interaction critically impacts overall system reliability. The novelty of this research lies in integrating technical performance metrics with human-centered analysis in a real-world deployment.
The rapid rise of phishing attacks on social media platforms highlights an urgent and growing cybersecurity challenge in today’s digitally connected world. As millions of users rely on these platforms for communication, business, and information sharing, cybercriminals increasingly exploit them to distribute deceptive links, fake advertisements, and fraudulent messages designed to steal sensitive data such as login credentials, financial information, and personal identities. The sophistication of these attacks—often leveraging social engineering techniques, impersonation of trusted entities, and AI-generated content—makes them difficult for users to recognize and avoid. Consequently, there is a critical need for robust and intelligent detection systems capable of reliably identifying malicious content in real time. Such systems should incorporate advanced machine learning models, behavioral analysis, and continuous adaptation to emerging attack patterns in order to effectively mitigate risks, protect users, and maintain trust in social media ecosystems. This paper proposes a multimodal phishing detection framework that integrates visual indicators (e.g., logos and page imagery) with textual signals (e.g., URL structure and phishing-related keywords). Our contributions are threefold: (i) a feature fusion strategy that combines visual and textual cues, (ii) the construction of a comprehensive dataset using feature extraction and natural language processing (NLP), and (iii) a comparative evaluation of multiple machine learning models. We extract 27 discriminative URL features and evaluate Random Forest, XGBoost, LightGBM, and Naı̈ve Bayes. Random Forest achieves the best performance, reaching 98.6% accuracy with a false positive rate of 0.01, demonstrating the effectiveness of the proposed multimodal approach for phishing detection on social media.
Accurate estimation of learners’ skill mastery is essential for adaptive learning systems, particularly in programming education, where evaluating learner performance remains challenging due to the complexity of code assessment and the need to capture partial correctness and diverse learning behaviors. This study aims to improve mastery estimation and adaptive exercise recommendations through a continuous knowledge-tracing approach based on an extension of Bayesian Knowledge Tracing. The proposed approach integrates continuous performance scores from an automated evaluation process that combines structural code analysis, dynamic testing, and large language model–based analysis, while accounting for task difficulty and guidance format. The estimated mastery is then used to recommend programming exercises adapted to the learner’s knowledge state. To evaluate the approach, controlled simulations involving diverse learner profiles and real interaction data were conducted. Results show that the proposed model variants reduce mastery estimation error by up to 70% compared with the baseline model, particularly for learners with low and average proficiency levels. Furthermore, the approach improves estimation stability and generates recommendations consistent with learner progression. The main contribution of this work is a continuous extension of Bayesian Knowledge Tracing that leverages fine-grained assessment data to improve learner modeling and adaptive exercise recommendation in programming education.
This study examines the impact of tourism demand and financial development on economic growth in five Western Balkan countries over the period 2000–2024. International tourist arrivals are used as a proxy for tourism demand, while domestic credit to the private sector represents financial development. Gross capital formation and labour force participation are included as control variables. To investigate the short-run and long-run relationships among the variables, the study employs a panel econometric framework comprising cross-sectional dependence tests, first- and second-generation panel unit root tests, panel cointegration tests, and the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) estimator. The Mean Group (MG) and Dynamic Fixed Effects (DFE) estimators are also estimated, while the Hausman specification test is used to select the preferred estimator. This study contributes to the tourism–finance–growth literature by providing updated panel evidence for five Western Balkan countries and by simultaneously examining tourism demand and financial development within a comprehensive PMG-ARDL framework. The results reveal significant cross-sectional dependence and confirm the existence of a stable long-run equilibrium relationship among the variables. The Hausman test supports the PMG estimator. The PMG-ARDL results indicate that tourism demand, financial development, gross capital formation, and labour force participation positively and significantly influence economic growth. Furthermore, the error-correction term is negative and significant, indicating that approximately 19.1% of short-run disequilibrium adjusts toward long-run equilibrium annually. The findings support the Tourism-Led Growth Hypothesis and highlight the importance of tourism development and financial sector expansion in promoting sustainable economic growth in the Western Balkan countries. JEL Classification: O40; Z32
Mg-doped ZnFe₂O₄ spinel ferrite nanoparticles (MgxZn1-xFe2O4, x = 0.1–0.3) were synthesized via a sago-starch-assisted sol–gel autocombustion method and evaluated for solar-driven photocatalytic degradation of ciprofloxacin and tetracycline antibiotics. Comprehensive characterization using XRD, FTIR, Raman spectroscopy, TGA, SEM-EDS, TEM, VSM, UV–Vis DRS, and BET confirmed the formation of single-phase spinel structures with tunable physicochemical properties. The materials exhibited superparamagnetic behavior with saturation magnetization values of 11.9–19.6 emu g⁻¹, enabling facile magnetic recovery. Optical analysis revealed visible-light absorption with band gap energies in the range of 1.45–1.67 eV. Morphological observations by SEM and TEM revealed relatively uniform nanoparticles ranging from 15 to 45 nm in size. Among the compositions, MgxZn1-xFe2O4 (x = 0.25) demonstrated superior photocatalytic performance, achieving about 85% degradation of ciprofloxacin and 70% degradation of tetracycline within 120 min under solar irradiation. The enhanced activity is attributed to the synergistic effects of reduced crystallite size, increased surface area, and improved charge separation, which facilitate the generation of reactive oxygen species. The integration of Mg substitution with a sago-starch-mediated green synthesis route offers an environmentally benign approach to producing efficient, magnetically recoverable photocatalysts. These findings highlight the potential of Mg-modified ZnFe2O4 for sustainable treatment of antibiotic-contaminated wastewater.
