5 10 70 https://omeka.ibu.edu.ba/files/original/9c29e4d40232e01702af619983abfb59.pdf dc20133763eb478f485ac342c6add442 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 Movie Recommender Web Application Author Author Salih Rogo Abstract A summary of the resource. Problem Statement: The exponential growth of digital content has created an information overload problem, making it increasingly difficult for users to discover relevant movies from vast catalogs. Traditional browsing methods are inefficient and fail to leverage user preferences and behavioral patterns, necessitating intelligent recommendation systems that can provide personalized movie suggestions. Methods and Procedures: This project developed a comprehensive movie recommendation system utilizing collaborative filtering techniques, implemented with a Python FastAPI backend and a Remix.js frontend. The system employs the Singular Value Decomposition (SVD) algorithm, trained on the MovieLens 32M dataset, which contains 162,541 users and 59,047 movies. The architecture integrates multiple data sources, including IMDb metadata through the OMDb API, implements RESTful API endpoints for recommendation generation, and provides a modern web interface for user interaction. The system was deployed on Heroku with MySQL database hosting on the Railway platform. You can visit the recommender system by yourself on the following URL: www.salihrogo.me Results: Comprehensive evaluation demonstrated solid performance across key metrics: Mean Absolute Error of 0.82, indicating good predictive accuracy, Hit Rate of 58.7% showing effective recommendation relevance, and catalog coverage of 72.3% ensuring adequate movie variety. The system achieved 86.4% user coverage, minimizing cold start problems, while maintaining a diversity score of 0.612 and a novelty score of 0.578, indicating balanced recommendations between popular and lesser-known content. Testing suite comprising 43 test cases validated system reliability across unit, integration, and end-to-end scenarios. Conclusion: The implemented movie recommender system successfully addresses the content discovery challenge through effective collaborative filtering, demonstrating production-ready performance with clear pathways for future enhancement. The system provides a scalable foundation for personalized movie recommendations while maintaining data integrity, security, and user experience standards. Keywords Keywords. collaborative filtering, movie recommendation system, SVD algorithm, MovieLens dataset, FastAPI, machine learning, personalized recommendations, web application sdp web development https://omeka.ibu.edu.ba/files/original/38241da3fc5ef5b9474be1c93fe89fa7.pdf 6bba05f2040411ac0ec700b0bff6b369 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 Quantitative Analysis of Voice Recognition Models Author Author Faris Muhović Abstract A summary of the resource. With the growing adoption of virtual communication and voice-driven applications, the need for accurate, real-time, and privacy-conscious transcription tools has become critical. Existing solutions largely rely on cloud infrastructure, introducing concerns around latency, cost, and data privacy. This project investigates whether modern speech recognition models can perform competitively in fully offline environments while maintaining accuracy and responsiveness. To this end, we conducted a comparative evaluation of four voice transcription model, Whisper, Faster-Whisper, Wav2Vec2, and Vosk, using the AMI Meeting Corpus. Each model was assessed based on four key metrics: Word Error Rate (WER), Character Error Rate (CER), BLEU, and ROUGE-L. Our findings demonstrate that Faster-Whisper outperforms the others in accuracy and latency, making it a strong candidate for edge deployment. Building upon this analysis, a lightweight desktop application was developed using Python and PyQt5. The app captures microphone input in real time, applies VAD (Voice Activity Detection) and loudness filtering to reduce noise, and transcribes valid segments using Faster-Whisper. Additionally, the tool integrates Ollam, a local LLM engine to optionally generate intelligent responses to transcribed text. This work contributes a dual outcome: a detailed empirical evaluation of modern transcription models on realistic meeting audio, and a functional, privacy-preserving voice assistant prototype for local systems. The results highlight the feasibility and value of running sophisticated voice AI tools on personal machines without cloud dependency, paving the way for secure adoption in sensitive domains such as legal, healthcare, and enterprise communication. Keywords Keywords. speech recognition, Whisper, Faster-Whisper, transcription models, real-time, privacy, WER, PyQt5 machine learning sdp https://omeka.ibu.edu.ba/files/original/92ae89ad7a41ba9c5ec9a5571ee571f8.pdf bf338fbd29dfcc0e55ec2cf5e3c1ed98 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 MODEL FOR PREDICTION OF LUNG CANCER Author Author Lamija Šetić Abstract A summary of the resource. Lung cancer remains one of the leading causes of cancer-related deaths globally, with early detection being critical to increasing survival rates. The primary goal of this project is to design and implement a machine learning classification model capable of accurately identifying lung cancer based on patient data. This work utilizes a publicly available dataset which includes features such as age, gender, air pollution levels, smoking habits, and other relevant health indicators. The methodology involves preprocessing the dataset to handle missing values, normalize input features, and encode categorical variables. Various classification algorithms were explored, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting, to determine the most effective model. Model performance was evaluated using standard metrics such as accuracy, precision, recall, and F1-score through cross-validation techniques to ensure robustness. Initial results indicate that ensemble methods, particularly Random Forest and Gradient Boosting, significantly outperform other models, achieving an accuracy of over 96%. These findings suggest that machine learning techniques can play a crucial role in assisting medical professionals with early diagnosis, thereby contributing to timely treatment and improved patient outcomes. In conclusion, this project demonstrates the effectiveness of supervised machine learning algorithms in medical data analysis and highlights the potential of data-driven solutions for real-world health challenges. Future improvements may involve integrating additional medical features and deploying the model in a web-based diagnostic tool for practical use. Keywords Keywords. senior design project, lung cancer classification, machine learning, Random Forest, early diagnosis https://omeka.ibu.edu.ba/files/original/88daf60e805b44dc5b629d7eadd735c1.pdf 17ea5fe0b17f652e17be65bb21682b36 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 development https://omeka.ibu.edu.ba/files/original/34883605c92ec5c83805af0d48d3f5d2.pdf 09e0a628f3dfc1e932cdb2ba9c58350f 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 WASTE MANAGEMENT SYSTEM Author Author Ahmed Kedić Abstract A summary of the resource. Waste management is a major issue in modern cities. Many places have difficulties with collecting, sorting, and disposing of waste in an efficient way. The Waste Management System project addresses these problems by using a software application. The system helps organize and optimize the process of waste collection, track vehicles, manage employees, and keep records of containers and routes. A three-layer architecture is used: Data Access Layer (DAL), Business Logic Layer (BLL), and API Layer. The DAL stores and manages the data in a database. The BLL contains the main logic for how the system works. The API Layer allows users and other systems to interact with the application through web requests. The user interface is implemented with modern web technology, React, which makes the system easy to use and accessible from any device. C# and ASP.NET Core are used for development. The results show that the system can make waste management more efficient. It becomes easier to assign tasks, monitor progress, and generate reports. In conclusion, this project can help cities or companies improve their waste management process and reduce problems related to waste. Keywords Keywords. waste management, software, database, ASP.NET Core, C#, React, efficiency, environment management system sdp web development https://omeka.ibu.edu.ba/files/original/29fb8a7277899c0614dfe0c2e329cbdf.pdf 95ddc94f7844bd718a4392a8df30e99b 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 Efficient Parking Reservation System for IBU Students Author Author Faris Gigić Abstract A summary of the resource. Parking availability is a growing concern for university students, often leading to time inefficiencies, increased stress, and congestion within campus premises. This project aims to develop a web-based parking reservation system tailored specifically for students of the International Burch University (IBU). The core objective is to provide an intuitive platform that allows students to view available parking slots in real-time and reserve them in advance, reducing unnecessary vehicle circulation and improving overall parking management. The system is built using modern web technologies, with a front-end developed in React and styled with Bootstrap for responsiveness and user-friendly interaction. Authentication is managed through Google Sign-In, restricted to university-issued student emails (ending in @stu.ibu.edu.ba) to ensure authorized access. The backend employs Node.js and Sequelize ORM for handling database operations, with parking data being managed dynamically t o reflect real-time changes in availability. Functionality includes viewing parking layouts, selecting available slots, and booking or canceling reservations. A calendar and/or map interface provides a visual and interactive overview of parking slot statuses. The system distinguishes between student and professor parking zones to prevent cross-access and ensure fair usage. Keywords Keywords. parking reservation, student parking, IBU, web application, React, Google authentication, smart campus parking software engineering web application https://omeka.ibu.edu.ba/files/original/31d0cee861ad332002485e26ed2bff9b.pdf ce074fd1b0af5ad15d6e96245e59603c 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 E387 - Digital Platform for Real-Time Control of EV Charging Stations and Chargers Author Author Faruk Ćidić Abstract A summary of the resource. The growing adoption of electric vehicles (EVs) highlights the urgent need for efficient, secure, and user-friendly digital platforms to manage EV charging infrastructure. This project, E387 - Digital Platform for Real-Time Control of EV Charging Stations and Chargers, addresses this need by developing a comprehensive software ecosystem to simplify charging processes for users and operators alike. The project focuses on four core components: a mobile application, a backend system, Point of Sale (POS) integration, and a charger tablet application. The mobile application enables EV users to locate charging stations, initiate and monitor charging sessions, and process payments with ease. For operators, the backend system ensures seamless management of real-time charging station statuses, secure payment processing, and detailed transaction logging. POS integration expands payment flexibility by supporting on-site transactions via embedded tablet applications and POS devices. Additionally, the tablet application, installed on chargers, offers an intuitive interface for users to initiate charging sessions and track charging progress. The design and development process prioritizes scalability, user experience, and security. The mobile and tablet applications are built with intuitive user interfaces to cater to a diverse audience. The backend employs robust frameworks and real-time data handling to ensure reliability and scalability, while the integration of POS devices leverages secure payment protocols. Upon completion, this platform is expected to deliver a seamless and flexible user experience for EV owners, streamline operations for administrators, and improve the overall adoption of EV charging infrastructure. This work represents a significant step forward in creating sustainable and user-centered solutions for the growing EV ecosystem. Keywords Keywords. electric vehicles, EV charging stations, real-time monitoring, mobile application, POS, e-mobility, OCPP. electric vehicles mobile application software engineering https://omeka.ibu.edu.ba/files/original/8dc806ba3dc087b54166763398d36382.pdf add856703176e935c20358b7baef4535 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 Company Vehicle Mileage Tracking System Author Author Adrijan Krtalić Abstract A summary of the resource. This project aims to develop a Mileage Tracker mobile application that enables employees to record their trips while using company vehicles. The issue it addresses is the inefficiency of manual mileage logging, which can be time-consuming and prone to errors. The app utilizes Google Maps API to track movement automatically and stores trip data in Google Sheets, ensuring accuracy and reducing manual effort. Google Sign-In is integrated for secure access, allowing each user to manage their travel records effortlessly. The final result is a user-friendly mobile app that simplifies mileage tracking and enhances data reliability. In conclusion, this Mileage Tracker provides a practical and automated solution for employees, improving efficiency and reducing administrative workload. Keywords Keywords. mileage tracker, automated logging, travel records, Google Maps API mileage tracker mobile application software engineering https://omeka.ibu.edu.ba/files/original/3fa9458b02b90648302831db7e7bd444.pdf 0d0000278c444cae0fe6fa9238220c21 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 Travel Souvenir Author Author Miralem Mašić Abstract A summary of the resource. This project addresses the common problem of disorganized digital travel photos by developing a mobile application to automatically curate and enrich them with geographical and informational context. The primary objective was to create a "digital souvenir" experience that goes beyond simple photo storage. The "Travel Souvenir" application was built for the Android platform using a modern, modular architecture designed for stability and scalability. The methodology involved integrating a secure cloud backend for user management, real-time data synchronization, and media storage. The app utilizes the device's built-in location services for automatic city categorization, an on-device machine learning model for real-time landmark recognition, and external data APIs to add descriptive context to each souvenir. The result is a fully functional application where user photos are automatically organized into location-based albums. The app successfully syncs data across devices and includes features such as a public feed for shared albums, personal note-taking, and AI-powered landmark identification. The project concludes that by integrating modern mobile and cloud technologies, it is possible to create an engaging and automated solution to the challenge of preserving digital travel memories. Keywords Keywords. travel application, photo organization, digital journaling, location-based services, landmark recognition, machine learning, cloud sync machine learning software engineering travelling https://omeka.ibu.edu.ba/files/original/aadf1e90460f20eab873f5560771d8b3.pdf 2987dcc83603d295cda42928b37c46d8 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 Predicting Sleep Disorders Using Machine Learning Algorithms Author Author Fikret Zajmović Abstract A summary of the resource. Sleep disorders such as insomnia and obstructive sleep apnea (OSA) affect millions globally and are linked to significant physical, cognitive, and psychological impairments. Traditional diagnostic methods—including polysomnography and self-reported questionnaires—are resource-intensive, time-consuming, and often unsuitable for large-scale or early-stage screening. To address these limitations, this study proposes a non-invasive, machine learning–based framework for the automated classification of sleep disorders using demographic, behavioral, and physiological features. The research utilizes the publicly available Sleep Health and Lifestyle Dataset, comprising 400 records with 13 features, including age, gender, BMI category, sleep duration, stress level, blood pressure, and physical activity level. Five supervised learning algorithms were developed and evaluated: Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and an Artificial Neural Network (ANN). The models were trained to classify individuals into one of three sleep health categories: No Disorder, Insomnia, or Sleep Apnea. A comprehensive preprocessing pipeline was implemented, involving data cleaning, feature scaling, one-hot encoding, and SMOTE-based class balancing. Model development followed a nested 5-fold cross-validation strategy, with hyperparameter optimization conducted using GridSearchCV. Performance was evaluated using standard classification metrics: accuracy, macro-averaged precision, recall, F1-score, and ROC-AUC. Results showed that XGBoost and ANN achieved the highest performance, with almost all scores exceeding 0.9, indicating strong predictive accuracy and generalization across validation folds. Feature importance analysis revealed that sleep duration, blood pressure, and BMI category were the most influential predictors. Visualization tools—including confusion matrices, radar charts, and feature importance plots—were used to enhance model interpretability and diagnostic transparency. Despite the promising results, limitations exist. The relatively small dataset (n = 400) and the absence of critical variables such as sleep stage architecture, oxygen saturation, and environmental or comorbidity data constrain generalizability and clinical applicability. Future research should focus on incorporating larger, more diverse datasets and integrating longitudinal or real-time data from wearable devices to improve predictive robustness. In conclusion, this study demonstrates the feasibility and effectiveness of machine learning algorithms in classifying sleep disorders using non-invasive inputs. The findings support the development of scalable, AI-driven diagnostic tools that can enhance sleep disorder screening in both clinical and consumer health settings, contributing to the advancement of telemedicine, digital health innovation, and personalized preventive care. Keywords Keywords. sleep disorder classification, insomnia, sleep apnea, machine learning, XGBoost, artificial neural networks, SMOTE, predictive modeling, telehealth, health informatics classification models data science machine learning sleep disorder