http://dspace.epoka.edu.al/handle/1/1639 2026-02-04T18:24:53Z http://dspace.epoka.edu.al/handle/1/2593 A DYNAMIC MODEL FOR PERSONALIZED E-LEARNING USING INTERNET OF THINGS Spaho, Edlir Personalized Online Learning is an adaptive educational approach that tailors learning experiences based on individual learner needs, preferences, and progress. It utilizes artificial intelligence, machine learning, and big data analytics to customize teaching and learning services, thus enhancing academic performance. Traditional POL systems predominantly rely on limited dynamic learner profiles based on behavioral and historical data. They struggle to provide real-time adaptation and context-aware personalization by disregarding critical dynamic factors such as cognitive load, emotional state, and environmental conditions. This dissertation proposes an innovative Internet of Things (IoT) based personalized e-learning framework that integrates real-time learner data, addressing the limitations of current approaches in adaptive e-learning. The research introduces a novel IoT-based dynamic model for personalized e-learning, comprising five modules: (1) a prior knowledge classification and clustering module that dynamically adjusts learning content level based on learners prior knowledge levels; (2) a machine learning-driven learning style preference module that customizes content formats according to individual learning styles; (3) an IoT module that integrates environmental and biological data; (4) a customized Learning Object Container that enriches learning objects with additional metadata; and (5) a rule-based Smart Engine that manages and integrates all modules to personalize learning materials dynamically. The framework is implemented within the Moodle learning management system, and a pilot case study is conducted to evaluate its effectiveness. The study addresses significant challenges, including prior knowledge classification and clustering model, new learning style classification model, real-time data processing, and integration of IoT devices in POL systems. It also explores pedagogical considerations such as cognitive load management, adaptive assessment and feedback, and interactive learning strategies. Empirical validation demonstrates the model’s effectiveness in improving engagement, knowledge retention, and academic performance through data-driven, context-aware adaptation. This research advances the field of IoT-based educational technologies by offering a scalable, intelligent framework that transforms online learning into a dynamic learner-centered experience. Future research directions include artificial intelligence enhanced personalization, scalability in large-scale deployments, and integration with emerging educational technologies. 2025-04-28T00:00:00Z http://dspace.epoka.edu.al/handle/1/2592 BIOMATERIAL TOXICITY ASSESSMENT USING NON-INVASIVE MICROSCOPY AND AI-DRIVEN CELL MORPHOLOGY ANALYSIS Duro, Xhoena Medical image analysis has significantly advanced with machine learning, enhancing disease diagnosis and biomaterial toxicity assessment. However, challenges such as data heterogeneity, computational complexity, and the lack of standardized evaluation metrics hinder its full potential. The study investigates supervised classification of three morphologically similar cell types, evaluating architectures (VGG16, Inception V3, SqueezeNet) and classifiers (Neural Network, Random Forest, KNN, etc.). Results indicate VGG16 paired with Neural Networks achieves the highest accuracy. Unsupervised clustering is explored by applying ISO guidelines to assess biomaterial toxicity levels, leveraging VGG16 and SqueezeNet for feature extraction. A hybrid clustering approach enhances classification into toxicity levels, demonstrating improved separability with high-pass filtering techniques. A U-Net-based model is optimized for cell counting, evaluating optimizer-loss function combinations for segmentation and confluency analysis. v Experiments on cells exposed to biomaterials (PAR50, UniFast) reveal toxicity patterns through morphological changes. Hybrid loss functions (Dice-Focal, Jaccard-BCE) significantly improve segmentation accuracy. Quantization and pruning techniques are applied to reduce computational demands without compromising accuracy to enable real-world deployment. A pruned U-Net achieves 95% segmentation accuracy. This research contributes novel methodologies for biomedical image analysis by: (i) developing a benchmarked unsupervised clustering framework aligned with ISO standards, (ii) proposing a high-accuracy classification model for cell types, (iii) optimizing U-Net for segmentation and counting, and (iv) enhancing computational efficiency through model compression. These findings support automated biomaterial toxicity assessment, improving efficiency and standardization in medical imaging applications. 2025-04-15T00:00:00Z http://dspace.epoka.edu.al/handle/1/2340 JUSTICE REFORM IN ALBANIA AND ITS IMPACT ON DEMOCRACY (2016-2023) Haxhiu, Dea The instruments of the judicial system are the Constitution and national, international, and European law that the country has adopted and ratified. Law quality and democratic legislation are on one side. The rule of law is the instrument of this quality law and democratic legislation to determine above all and for everything. All should be equal before the law. No one can escape the responsibility to respect the law. The study analysis is divided into two spheres. The theoretical analysis focuses on justice reform, beginning with the constitutional reform in 2016, the vetting process, and the new judicial institutions that have contributed to the consolidation of the rule of law, balance of power, accountability, and transparency as foundations of liberal democracy. Implementation analysis, which analyses the effectiveness of the justice reform through 2016-2023. The challenges have prolonged the justice reform leading to debatable outcomes. The methodology is conducted through interviews, analysis of formal documents, academic contribution, and data highlighting the uncertainty of the contribution of justice reform in the Albanian judiciary. 2024-09-23T00:00:00Z http://dspace.epoka.edu.al/handle/1/2339 PERCEIVED ELECTORAL FRAUD AND POLITICAL POLARISATION IN POST-COMMUNIST ALBANIA Nako, Alban; Nako, Alban This dissertation examines the intricate relationship between electoral fraud and partisan polarisation in post-communist Albania, a nation where democratic institutions are still grappling with the legacies of authoritarian rule. Over the past three decades, Albania’s electoral system has evolved amid persistent challenges, with electoral fraud emerging as a critical barrier to democratic consolidation and public trust. This study seeks to unravel the complex dynamics that link electoral fraud to the deepening political divisions within the country. Employing a robust mixed-methods approach, this research integrates historical institutional analysis with a comprehensive survey of Albanian voters. This unique methodology allows for a deep exploration of how perceptions and personal experiences of electoral fraud fuel partisan polarisation. The study meticulously traces the evolution of electoral fraud from Albania’s first multi-party elections in 1991 to the recent 2021 elections, shedding light on how fraudulent practices have distorted electoral outcomes, entrenched political loyalties, and exacerbated societal divisions. The dissertation’s findings reveal that electoral fraud in Albania is not merely a procedural flaw but a deeply ingrained political strategy that reinforces partisan identities and undermines democratic engagement. This study significantly contributes to the broader discourse on post-communist democratisation by highlighting the crucial link between electoral integrity and political polarisation. Importantly, it offers both theoretical insights and practical recommendations for strengthening electoral processes in transitional democracies, thereby providing a roadmap for policymakers and practitioners. In this comprehensive examination, the dissertation underscores the pressing need to address electoral fraud in Albania. The urgency of this issue cannot be overstated, as it directly impacts political stability and democratic resilience. 2024-10-01T00:00:00Z