4 10 3494 https://omeka.ibu.edu.ba/files/original/56654ded39270dde599ac211dd39c8f8.pdf 8e2871dab454dcf442a74e4b3334aca6 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 CUSTOMER CHURN USING MACHINE LEARNING: A DATA-DRIVEN APPROACH FOR RETENTION STRATEGIES Author Author DŽELILA TINJAK Abstract A summary of the resource. Customer churn poses a critical challenge to business sustainability, leading to significant revenue loss and signaling potential issues in customer satisfaction. This project addresses this by developing a robust machine learning framework to accurately predict customers at high risk of discontinuing service, equipping businesses with a data-driven tool for proactive retention. The methodology encompasses the entire data science lifecycle, beginning with the preprocessing of the Telco Customer Churn dataset, where advanced feature engineering and the Synthetic Minority Oversampling Technique (SMOTE) were applied to enhance data quality and address class imbalance. A diverse portfolio of classification algorithms—including logistic regression, random forests, and gradient boosting—was trained and rigorously evaluated using metrics such as accuracy, precision, recall, and AUC-ROC. To maximize predictive power, a soft-voting ensemble was constructed from the top-performing models. The final ensemble achieved an overall accuracy of 76.8% and, critically, a recall of 73% for the churn class, demonstrating strong reliability in identifying at-risk individuals. A key deliverable is an interactive user interface allowing stakeholders to leverage model predictions, visualize churn trends, and interpret the key factors driving customer behavior. This study culminates in a predictive tool that provides actionable insights, enabling businesses to transition from a reactive to a proactive approach in managing customer relationships. By identifying at-risk customers, companies can deploy targeted interventions to enhance loyalty, reduce churn, and secure long-term profitability, creating a practical bridge between advanced analytics and strategic decision-making Keywords Keywords. Customer Churn, Predictive Modeling, Machine Learning, Customer Retention, Classification, Ensemble Learning, Feature Engineering, Data Science. customer churn data science machine learning prediction https://omeka.ibu.edu.ba/files/original/0019c00e368c84a90212a2e8d5aa8bf0.pdf 1bf003b378422d6682af5bbb401cae6b 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 Leveraging of Machine Learning for Early Cancer Risk Identification and Predictive Flagging Author Author Belmin Šestić Abstract A summary of the resource. Early detection of cancer remains a vital component in reducing mortality and enhancing treatment outcomes. Traditional diagnostic approaches, such as biopsies, imaging scans, and clinical assessments, often identify cancer at a stage where the disease has already advanced. This delay in detection arises because early-stage cancers typically exhibit minimal or no symptoms, increasing the risk of late diagnoses and reduced chances of recovery. This proposed study investigates the potential of machine learning methodologies in facilitating early cancer risk assessment by analyzing complex medical datasets. The primary objective is to assess whether machine learning models can reliably identify patients at heightened risk before the disease becomes clinically evident. Through this approach, the study aims to contribute to the development of predictive systems that can trigger early interventions and encourage proactive health monitoring. The research seeks to answer the core question: “Can machine learning models effectively assess the risk of early-stage cancer using molecular-level data, such as gene expression profiles, prior to the onset of clinical symptoms?” Sub-questions to be explored include the accuracy of early-stage cancer detection using machine learning, the types of data that most influence prediction performance, and the feasibility of using such models to prompt timely medical evaluations in the absence of traditional diagnostic markers. The findings are expected to support advancements in personalized medicine by laying the groundwork for tools that assist in identifying high-risk individuals, potentially transforming the current approach to cancer screening and prevention. Keywords Keywords. Machine Learning, Cancer Risk Assessment, Early Detection, Medical Data, Predictive Modeling Cancer Risk Assessment Early Detection machine learning master’s thesis Medical Data Predictive Modeling https://omeka.ibu.edu.ba/files/original/8d66acc444227b11b0604c66cff36946.pdf 6841d67bcbb7734abb69327017b5db0a 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 Comparison of Performanse and Security Aspects of Database Access via Stored Procedures and APIs Author Author Ramiz Šuvalija Abstract A summary of the resource. Modern applications typically get the information in one of two modalities, namely API as an intermediary layer or stored procedures in the same database. The aim of this study is to contrast these methods, mainly performance-wise, and then securitywise, as well as suitability for maintenance as well as scalability. The project will implement identical stored procedures in the PostgreSQL database, and a API backend in Python. Execution time for a query, resource consumption as well as susceptibility to security flaws will be evaluated. The plan is to perform 10 runs for each comparison so as to ensure the obtained results are as accurate as well as dependable as possible. And one of the aims is to devise practical recommendations as to when to apply a stored procedure, and when the API method, where a boundary (equilibrium) has to be drawn between the logic of intermixing in the same data as well as the logic in the app layer. Today with applications being used in distributed environments on a widespread basis, awareness of them is most important in ensuring smooth and effective development of information systems, particularly in those fields where a lot of information has to be processed, such as e-business, banks etc. Keywords Keywords. Stored Procedures, Api, Comparison, Database Access, Postgresql Api Comparison Database Access master’s thesis Postgresql Stored Procedures https://omeka.ibu.edu.ba/files/original/f8eb06a01cf315bd9472696d8c54256c.pdf 42a74922c571b4bfc89b486398f30cd1 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 THE IMPACT OF LOW-CODE PLATFORMS ON TRADITIONAL AND AGILE SOFTWARE DEVELOPMENT TEAMS Author Author Emina Dedić Abstract A summary of the resource. The adoption of Low-Code/No-Code (LCNC) platforms is increasing in the software development industry, promising to modernize the development process. While these platforms are often known for their accessibility and efficiency, there is still discussion about their effects on software development teams. This research aims to investigate the effects of LCNC platform adoption, focusing on changes to team dynamics, roles, and productivity. A sample project is implemented across different LCNC platforms, allowing for a direct comparison of the development process, time, and challenges encountered. Keywords Keywords. Low-Code/No-Code platforms, software development, team dynamics, traditional development, productivity, adoption impact, skill set, project timelines, collaboration. sdp traditional development https://omeka.ibu.edu.ba/files/original/6b633ca8d1ff2416b3a1b2a2b1b1b085.pdf 0021952bc952d2f79e1479fd60858c06 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 LIGHTFIN: LIGHTWEIGHT LINUX-BASED MICROSERVICES SYSTEM FOR FISCALIZATION, LOAN AND SUBSIDY MANAGEMENT Author Author Anur Peljto Abstract A summary of the resource. From small businesses to large corporations, fiscalization has been a cornerstone of financial management for decades and will continue to be for many more to come. However, many businesses still struggle with outdated systems for fiscalization, loan and subsidy management that rely on legacy monolithic architectures. These systems can be very difficult to maintain and scale. The aim of this project was to introduce a new solution to these issues: Lightfin, a fiscalization, loan and subsidy management system built with a microservice architecture, designed to offer enhanced performance, low delays, easy scalability and high stability. Keywords Keywords. Linux, fiscalization, lightfin, microservice driven architecture, loan, subsidy, performance Linux sdp https://omeka.ibu.edu.ba/files/original/efdc7dd6b5a9c26d9f609631b9ef5c5f.pdf 6eb7b5ea6b70f45b621a686bf12c0da5 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 Eventik Author Author Emir Prašović Abstract A summary of the resource. The objective of this paper is to introduce Eventik, a web application developed to centralize event discovery, creation and booking in Bosnia and Herzegovina. The idea is to solve the current problem with finding or creating events such as concerts, parties, or community activities, which is that the process requires searching through multiple social media platforms, making the whole process inefficient and frustrating. Eventik solves this by providing a single application where users can find and manage events of various types, including everything from entertainment to volunteering. This promotes social engagement by encouraging open invitation events such as dance parties and community events like city cleanups and tree-planting. The MERN stack, which features MongoDB, Express, React and Node.js, together with Tailwind is used for development. The system features secure user authentication, featureful event management and filtering, event reviewing and rating, and intuitive user management. Eventik eases the process of event searching, enhances event visibility, simplifies participation in events and supports a more connected community, something much needed today. Keywords Keywords. Event, Event Booking, Web Application, MERN, Volunteering, Bosnia and Herzegovina, Socializing, Depression, Event Discovery, Performance management system sdp software engineering https://omeka.ibu.edu.ba/files/original/52dc124399ed7c9054aa82d13da645e8.pdf 011bc4e6c80906e3f6e154d54afe17f0 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 MY WALLET - AI-BASED PERSONAL FINANCE MANAGER Author Author Samira Zeba Abstract A summary of the resource. In the contemporary financial landscape, many individuals face significant challenges in effectively managing their personal finances. The proliferation of digital transactions often leads to difficulties in comprehensively tracking expenditures, accurately categorizing spending, establishing realistic saving goals, and gaining a clear, consolidated financial overview. This lack of intelligent and streamlined solutions can result in financial oversight, missed opportunities for savings, and increased personal financial stress. The main intention of "My Wallet - AI-Based Personal Finance Manager" is to make personal finance management simpler, less time-consuming, and more insightful for every user. "My Wallet" is a web-based system designed to empower users with an intuitive and intelligent platform for financial control. This system enables individuals to efficiently manage their transactions, track income and expenses, set and monitor saving goals, and maintain a clear overview of their financial health. Key functionalities include secure user authentication, comprehensive transaction management (both manual entry and automated parsing from uploaded bank statements), and dedicated sections for incomes, expenses, upcoming bills, and saving goals. A distinctive feature of this application is its integration of Artificial Intelligence, which intelligently categorizes transactions, detects beneficiaries from bank statement data, and generates personalized saving plans based on user-defined objectives. While designed for ease of use and intuitive navigation, the application incorporates relatively complex AI algorithms and data processing logic beneath its user-friendly interface. Keywords Keywords. personal finance management, artificial intelligence, web application, financial tracking, saving goals AI sdp software engineering transaction https://omeka.ibu.edu.ba/files/original/d9b80b0a3557b3d6c12ca132d7c4511d.pdf c2b2c2432e3a99989b5134b97fee1d98 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 TRIPTIDY - AI BASED TRAVEL PLANNER Author Author Emina Peljto Abstract A summary of the resource. In the contemporary travel landscape, many individuals face significant challenges in effectively planning their trips. This lack of an integrated and intelligent solution can result in overwhelming manual effort, missed opportunities for personalized experiences, and increased stress during the planning phase. The main intention of "TripTidy: AI-Based Travel Planner" is to make personalized trip planning simpler, less time-consuming, and more insightful for every user. TripTidy is a web-based application designed to empower users with an intuitive and intelligent platform for comprehensive travel organization. This system enables individuals to efficiently generate personalized itineraries based on their destination, dates, budget, and preferences. Key functionalities include robust user authentication, dynamic integration with external APIs for real-time hotel (Booking.com via RapidAPI) and flight (Amadeus API) searches, AI-powered content generation (via TogetherAI), image retrieval, and geolocation services (Foursquare API) [1][2][3][4]. Users can also customize generated itineraries, track and manage expenses, and browse pre-made itineraries on a dedicated Guide page. While designed for ease of use and intuitive navigation, the application incorporates sophisticated AI logic and data processing beneath its user-friendly interface, built with React for the front-end, Node.js with Express.js for the back-end, and MySQL as the database [5][6][7][8]. Keywords Keywords. senior design project, travel planning, AI-powered, itinerary generation, web application, full-stack development AI planner sdp travelling https://omeka.ibu.edu.ba/files/original/727d86ddfca78fa793fcb6c54b3b92f3.pdf 97116e206fdc2d31dff2cfc1ae5a3693 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 Tweet Categorization and Sentiment Analysis of Tweets Author Author Vedad Fejzagić Abstract A summary of the resource. <span>In today’s era, using internet platforms to convey information to others, whether family, friends, or strangers has become the norm. One of the leading social platforms in that regard is “Twitter” (now “X”). The effectiveness of communication on such platforms can be analyzed through the process of sentiment analysis. Sentiment analysis is considered a classification problem that determines whether an input is positive or negative.</span><br /><br /><span>The research aimed to show to what extent certain machine learning models outperform others for the given subset of data, depending on the choice of preprocessing steps within the sentiment analysis domain. This can be divided into two goals. The first goal was to present results on how one pipeline of preprocessing steps affects each machine learning model compared to the other preprocessing pipeline. The second goal was to present results on the viability of using several machine learning models for sentiment analysis of tweets by comparing the accuracies of each. For that purpose, a single subset was taken from the data and duplicated two times. Each subset duplicate had different preprocessing steps applied to it. Afterward, both subsets were fed to several machine-learning models in order to gauge their performance. </span><br /><br /><span>Finally, this paper presented results on the aforementioned processes for which it was found that the Naïve Bayes machine learning model had the best accuracy, while the choice of preprocessing steps proved to be almost negligible in improving the overall model accuracy.</span> Keywords Keywords. Sentiment Analysis, Machine Learning, Preprocessing, Natural Language Processing, Twitter machine learning master's thesis natural language processing preprocessing sentiment analysis twitter https://omeka.ibu.edu.ba/files/original/b4627f5dad9e972398402945f83151e2.pdf 361d42f2cbf43adc52da37e85bb16344 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 AGORA - ONLINE MEETING PLATFORM Author Author Elnur Safarov Abstract A summary of the resource. The Agora Online Meeting Platform is a web-based application designed to facilitate the organization and management of online meetings, with a particular focus on psychologists and their clients. The main objective of this project is to provide a secure, user-friendly, and efficient environment for scheduling, conducting, and managing online sessions. The backend of the platform is implemented using the Laravel PHP framework, ensuring robust authentication, role-based access control, and seamless integration with payment systems such as Stripe. The frontend is developed using React and Next.js with TypeScript, providing a modern, responsive, and calming user interface specifically designed for individuals seeking psychological support. The platform features real-time video meetings powered by PeerJS and WebRTC technology, enabling secure peer-to-peer communication directly in the browser. The system is designed with GDPR compliance in mind, ensuring that user data is handled securely and users have full control over their personal information. The system supports user registration, meeting creation, participant management, calendar export functionality, and role-based access control with an intuitive interface that prioritizes user comfort and accessibility. The frontend design incorporates a carefully chosen color palette and smooth interactions to create a supportive environment for mental health professionals and their clients. Comprehensive testing was conducted to ensure reliability, security, and cross-browser compatibility. The results demonstrate that the platform can effectively streamline the process of organizing online meetings, making it a valuable tool for professionals and their clients while providing a safe and welcoming digital space for psychological support. Keywords Keywords. online meeting platform, Laravel, React, Next.js, PeerJs, video conferencing, mental health, user-friendly design, Stripe integration mental health sdp