5 10 3494 https://omeka.ibu.edu.ba/files/original/274af762575d2bd14cba8379ccd23541.pdf e6f213bf1fffcef3422ef791fbdec30f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Void Pointer: Motorized Radio Satellite Tracker Solution Author Author Muhamed Mulić Abstract A summary of the resource. There are many existing programs to receive and decode radio signals, others to process them (e.g. signals received from a weather satellite into an actual image), and other programs to control and manage necessary accessories (e.g. an antenna rotator). They are all typically unrelated, and function separately. There are some specialised programs, but they do not meet certain criteria and are not useful for general operation. This project aims to design and implement an automated antenna rotator system capable of tracking satellites and facilitating radio communication. The system integrates a custom-built hardware platform with a software interface to: (1) automatically adjust the antennas direction based on the position of the ground station and a chosen satellite, (2) set the appropriate radio frequency and configure other parameters based on selected satellite, and (3) provide a user-friendly interface for satellite selection and real-time control. The project targets Low Earth Orbit (LEO) satellites, such as weather satellites (e.g. NOAA, MetOp, etc.), with the potential to receive and decode signals like weather images. The system is intended to be modular; if certain features are not needed they do not break the rest of the system (e.g. for tracking celestial objects other than satellites, where decoding is not necessary). The main purpose of this project is not to develop another universal weather satellite decoder with extra features, but a more specific and simplified system for amateur use. Keywords Keywords. senior design project, SDR, antenna, rotator, tracker, weather satellite, HRPT, NOAA, POES, C++, Qt sdp weather satellite https://omeka.ibu.edu.ba/files/original/0d4e6cc0864479e1c3a4931d6a975a11.pdf 860d50573eb6d1f84672e5d4da6d1f7c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource DOCTOR APPOINTMENT BOOKING SYSTEM Author Author Amna Mutap Abstract A summary of the resource. Access to timely and organized medical care is often hindered by inefficient appointment booking systems, resulting in scheduling conflicts, long wait times, and administrative burden. This project addresses these challenges by developing DocBook, a web-based doctor appointment booking system designed to improve healthcare accessibility and streamline the scheduling process for patients, doctors, and administrators. The system was implemented using a modular, role-based structure that enables patients to search for doctors, view profiles, book or cancel appointments, and make payments online. Doctors can manage their schedules and track appointments, while administrators are provided with tools to oversee users and system activity. Methods included designing user-centered interfaces, applying secure authentication mechanisms, and testing the system through automated end-to-end simulations. Keywords Keywords. healthcare access, appointment scheduling, patient experience, medical administration, online booking system management system sdp software engineering https://omeka.ibu.edu.ba/files/original/b51ff162deeada2bfa73eee6e6bcfdfe.pdf 95ff3233f4a9e449e44a599c394bf4b6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource FAKE REVIEW DETECTION USING NLP AND MACHINE LEARNING Author Author Ajla Beća Abstract A summary of the resource. As AI tools like large language models become more advanced, it is increasingly difficult to tell apart human-written and AI-generated product reviews. This thesis presents a system that detects AI-generated reviews and explains its predictions in a user-friendly way. Several models were tested, including Logistic Regression, Support Vector Machines, GRUs, and transformer-based models like RoBERTa and DeBERTa. The best performance came from RoBERTa with label smoothing and DeBERTa-v3, both reaching 98% accuracy. While these advanced models were the most accurate, simpler models like GRU were still competitive and easier to interpret. The thesis also examined linguistic differences between real and AI-generated reviews. Real reviews were shorter, used more personal and emotional language, while AI-generated ones were longer, more structured, and often overused formal or generic phrases. A working browser extension was built as part of the project. It allows users to analyze reviews directly on websites and see predictions with basic explanations. Although the tool works well, there are still limitations, such as handling newer AI models and providing clearer feedback for non-technical users. Keywords Keywords. fake reviews, AI detection, browser extension, RoBERTa, NLP machine learning NLP sdp https://omeka.ibu.edu.ba/files/original/1ff757ec0e4dd76435d2580e147df1bd.pdf a4817cf193a40e562572b8d31f4ff610 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource SOFTWARE ASSISTING TEACHING STAFF IN TESTING PROCEDURES USING RFID TECHNOLOGY Author Author Eldin Guzin Abstract A summary of the resource. Manual attendance taking during academic examinations and lectures tends to be a time-consuming and error-prone process, especially when it comes to a large amount of inputs at the same time. This senior design project addresses the need for a more efficient method of monitoring student attendance during lectures and exams. To tackle this issue, this project proposes a software-assisted system that uses RFID (Radio Frequency Identification) technology integrated with an Arduino microcontroller. Each student already has their unique student identification card that has an integrated RFID chip. With the given software, it can be scanned upon entering the examination room or a classroom. The scanned data is immediately transmitted to a Ruby on Rails based web application that logs attendance records in real time. This system supports secure authentication, timestamped logs and intuitive administrative interface for educators to monitor attendance activity, enhances transparency in the testing process, strengthens exam policy enforcement and ensures that attendance data is accessible in a digital format.. The project combines low-cost hardware components such as RFID readers and Arduino boards, with robust web development practices. Arduino hardware acts as the physical interface for RFID scanning, while the backend web application performs data processing, storage and visualization. Keywords Keywords. senior design project, keywords, template rfid sdp software engineering https://omeka.ibu.edu.ba/files/original/7f17ded94652315afc7fbd922bcea059.pdf 98a134d0ae38d8a4fefd574989346b94 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AI STORYBOOK GENERATOR Author Author Ajla Čakić Abstract A summary of the resource. The rapid advancement of artificial intelligence has opened new possibilities for educational and creative applications. This project presents the design and development of an AI-powered Storybook Generator—a full-stack web application that allows users to generate personalized children’s storybooks using natural language prompts. The goal is to make storytelling more interactive, accessible, and creatively empowering by automating story creation, illustration, and narration through AI. The application addresses the problem of limited access to personalized, diverse, and engaging story content, especially for children from various cultural and linguistic backgrounds. By integrating advanced AI services, the platform generates unique stories based on user-provided inputs such as title, age group, genre, and illustration style. Using technologies like Google AI, Hugging Face’s text generation models, and custom text-to-speech tools, the system delivers cohesive narratives paired with AI-generated visuals and narration. The backend is developed using Node.js and Express.js, while the frontend is built with React.js, offering a responsive and user-friendly interface. Data is securely stored and managed using a Drizzle ORM with a PostgreSQL database. Results show that the system can produce high-quality storybooks with consistent plots, age-appropriate language, and stylized imagery, enhancing the reading experience. The platform also includes features like story exploration, user account management, coin-based generation limits, and payment integration for purchasing additional credits. In conclusion, the AI Storybook Generator showcases the potential of merging generative AI with interactive design to promote literacy and creativity in young users. Future work may involve mobile app development, multilingual support, story sharing features, and integration of educational objectives into story structure. The application serves as a scalable, customizable tool for families, educators, and storytellers worldwide. Keywords Keywords. AI story generator, children’s storybooks, Hugging Face, Google AI, Node.js, React, generative storytelling, text-to-speech AI sdp web application https://omeka.ibu.edu.ba/files/original/898d3390481715985e1899366de2077e.pdf c83f8b36a7dcda7b6bb84552da258b06 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimization of Email Marketing Campaigns Leveraging Machine Learning Techniques Author Author Nejla Zukorlić Abstract A summary of the resource. Email marketing is widely recognized as an effective digital marketing channel, offering a considerable return on investment (ROI). One key challenge is determining the optimal day and time to send emails to maximize customer response rates. This thesis explores the application of machine learning (ML) algorithms to predict the best send times for email marketing campaigns, focusing on improving response rates. The research utilizes historical email marketing data, including customer demographics, response behavior, and email send dates. Based on this data, various machine learning models, including decision trees and random forests, as well as ensemble methods at the end, will be used to predict the optimal day for sending emails. The study will also examine how factors like customer age and tenure influence response rates at different times. The question is if the machine learning-based predictions of the optimal send day and time will significantly improve response rates compared to traditional methods. Also, incorporating demographic factors, such as age and tenure, hopefully will improve the accuracy of these predictions. The expected outcome is that MLbased optimization will outperform traditional scheduling methods, providing a more effective and data-driven strategy for email campaign timing. Keywords Keywords. Email Marketing, Machine Learning, Send-Time Optimization, Customer Engagement, Response Rates, Data-Driven Marketing Customer Engagement Data-Driven Marketing Email Marketing machine learning master's thesis Response Rates Send-Time Optimization https://omeka.ibu.edu.ba/files/original/1e77e2888b1af34ce1b04e8c6c8b1578.pdf 3102207937ea6ff2b0c16d894cdecc49 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Analysis of Machine Learning Algorithms for Real Estate Price Prediction: Bosnia and Herzegovina vs. USA Author Author Adnan Krndžija Abstract A summary of the resource. Real estate markets are impacted by a variety of variables, including changes in the population, urban development projects, and changes in economic policy. This thesis sets out to investigate the effectiveness of machine learning algorithms in predicting real estate prices, paying close attention to the particular circumstances of Bosnia and Herzegovina as well as the United States. While the US real estate market has a long history and is well-known for its capacity to bounce back from downturns in the economy, the tale of the BiH real estate industry is very different. In contrast to the United States, which has seen centuries of economic expansion, financial crises, and legislative changes, Bosnia and Herzegovina's market development is a result of a combination of past influences and present difficulties. Beyond simple quantitative comparisons, our research takes a holistic method to uncover the predictive capability of machine learning models.<br /><br />We explore the complexities of random forests and decision trees, making use of their ability to reveal intricate patterns in real estate databases. This research also includes time series modeling to recognize and comprehend the evolving patterns that characterize real estate dynamics throughout time. The analysis of SARIMAX, ARIMA, and Holt-Winters time-series models shows ARIMA's consistent accuracy, while SARIMAX and Holt-Winters excel in stability and trend capture, respectively. In machine learning, Decision Trees offer interpretability, while Random Forests show reduced error rates and enhanced accuracy. In the US dataset, SARIMAX has a Mean Absolute Percentage Error (MAPE) of 3.35% and ARIMA achieves 1.66%, while Holt-Winters shows 3.54%. Decision Trees have a MAPE of 2.97%, and Random Forests achieve 2.10%. In the BiH dataset, SARIMAX has a MAPE of 5.08%, ARIMA achieves 1.22%, while Holt-Winters shows 2.17%. Decision Trees have a MAPE of 0.83%, and Random Forests achieve 0.82%. Keywords Keywords. Real Estate, Prediction, Bosnia and Herzegovina, United States Bosnia and Herzegovina master's thesis prediction Real Estate United States https://omeka.ibu.edu.ba/files/original/85f02200d8b6a059ec3861f417da6e72.pdf 1f82ffbbe02c17154c3399fe61ce48b1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis And Price Prediction For Accommodation Reviews in Bosnia And Herzegovina: A Comparative Study of NLTK and Hugging Face NLP Techniques Author Author Amila Čaušević Abstract A summary of the resource. The growing field of natural language processing (NLP) has huge potential in the advancement of consumer feedback and its application in determining pricing strategy in the hospitality industry. In this thesis, sentiment analysis and price predictions of accommodation reviews in Bosnia and Herzegovina are analyzed through a comparative study of two of the most commonly used approaches in NLP: NLTK - representing traditional methods, and Hugging Face - representing modern techniques. Initially, a long process of text preprocessing is performed that includes tokenization, lemmatization, stopword removal, and filtering of positive and negative reviews. Quantitative analysis such as word frequency distributions, measures of lexical diversity, and word co-occurrence tests reveal patterns within language use as well as the relationship between review attributes and sentiment.<br /><br />Different frameworks  for sentiment analysis are then compared. The Hugging Face sentiment pipeline and more modern and recent transformer architectures like BERT, RoBERTa, and XLNet are compared with more traditional techniques (e.g., NLTK/VADER). Metrics for evaluation such as accuracy, precision, recall, and F1-score are used to assess the performance of the sentiment models. In order to develop predictive price models based on regression techniques like Linear Regression, Random Forest, and Gradient Boosting, the thesis additionally integrates sentiment scores with quantitative metadata, such as review ratings, location ratings, and accommodation categories. The results show that Random Forest regression is the most effective method for identifying subtle, non-linear sentiment-price correlations, even though transformer-based sentiment analysis can show promise in identifying subtle signals within guest reviews. Last but not least, this work offers helpful recommendations to help hoteliers in Bosnia and Herzegovina to create focused pricing strategies while also enhancing the general guest experience. Keywords Keywords. NLP, Sentiment Analysis, Accommodation Reviews, Price Prediction, Bosnia and Herzegovina Accommodation Reviews Bosnia and Herzegovina NLP Price Prediction sentiment analysis https://omeka.ibu.edu.ba/files/original/b405bccb488e75bf8fb55f27f34023be.pdf a77087e12a6314709f1c2dad1d73a655 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis Techniques and Applications in the News Articles Author Author Mirza Novalić Abstract A summary of the resource. Sentiment analysis is essential for understanding public opinion, especially in the context of news articles, where tone and sentiment can significantly impact and control readers' perception and understanding of the content. This study explores a variety of sentiment analysis techniques that are applied to a vast amount of articles gathered from “New York Times” in the past two decades. The research focuses on the performance of traditional machine learning models, deep learning models and hybrid approaches. The aim of the paper is to answer three key questions regarding which approach is the most suitable for this problem and how fine-tuning affects end results.<br /><br />To address these questions, throughout the research, traditional machine learning models including Naive Bayes, Linear Support Vector Classification (SVC) and Logistic Regression were implemented. Among these approaches, Linear SVC achieved the best scores across all evaluation metrics. In the deep learning category, Long Short-Term Memory (LSTM) networks were applied. This approach provided exceptional performance which was overall better than traditional models. RNNs scored similarly as Linear SVC, while outperforming other traditional algorithms. <br /><br />A hybrid approach including the BERT model was another method that was explored, which combined specific architecture with deep learning-based contextual understanding. The results demonstrated high classification results, which supports the hypothesis that hybrid models can increase performance of sentiment prediction. Furthermore, fine-tuning of different models improved their performance, which highlights the importance of optimizing pretrained models for specific types of analysis. <br /><br />Overall, the findings confirm that deep learning models usually outperform traditional variants of machine learning methods while hybrid models can offer additional potential and perspective for enhancing sentiment classification in news articles. The study provides deep and valuable insights into effectiveness of different sentiment analysis and natural language processing (NLP) techniques, while at the same time discussing new possibilities and improvements in the field. Keywords Keywords. Sentiment Analysis, Traditional Algorithms, Deep Learning, Fine-Tuning, Hybrid Approach deep learning fine-tuning hybrid approach sentiment analysis Traditional Algorithms https://omeka.ibu.edu.ba/files/original/c99f4e38c2dcc97c5a3b0ca83c326795.pdf 734d69b81ac5f0a793d17702b63a259c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Retrieval-Augmented Generation System for Efficient Access to University Information Author Author Aldin Ahmethodžić Abstract A summary of the resource. Accessing university-related information, such as course syllabi, class schedules, and announcements, can be inefficient and fragmented, especially when data is distributed across multiple platforms. This often leads to time-consuming searches and a suboptimal user experience for both students and staff. This project presents a solution in the form of an intelligent, centralized platform capable of understanding natural language queries and delivering accurate, contextually relevant responses. It implements a Retrieval-Augmented Generation (RAG) system designed to streamline information access at International Burch University. The system features a multi-layered architecture combining semantic search and generative AI. Vespa.ai serves as the vector database enabling high-performance similarity search, while OpenAI’s GPT models handle natural language understanding and generation. MongoDB is used for session tracking and user state management. Documents in PDF, TXT, and web-based formats are ingested through a pipeline that performs scraping, text extraction, and chunking using LangChain’s RecursiveCharacterTextSplitter. The system is accessible via a modern web interface built with React.js, Vite, and Tailwind CSS, and also exposes a FastAPI-based REST API for backend interaction. Secure access is enforced using JWT-based authentication and authorization. The chatbot component retrieves relevant context through semantic search and maintains coherent multi-turn conversations using conversation history tracking. The developed system significantly improves the accessibility and efficiency of retrieving university information. It provides fast, context-aware responses to user queries by combining robust retrieval techniques with generative language models. The web-based interface ensures ease of use for both technical and non-technical users, while the modular backend supports scalability and maintainability. Intelligent query rephrasing and optimized chunk retrieval contribute to improved precision and user satisfaction. Overall, this solution demonstrates how RAG-based systems can transform information access in academic environments by offering a centralized, intelligent platform tailored to users' needs. Keywords Keywords. Retrieval-Augmented Generation, Semantic Search, Vespa.ai, OpenAI GPT, FastAPI, React.js, MongoDB, University Information System, Natural Language Processing natural language processing sdp