3 10 70 https://omeka.ibu.edu.ba/files/original/2c428581cfdc05ce202f12b106dc91c7.pdf 3f925e4f252f924ce18cefc9645499ef 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 Sentiment Analysis Models on Financial Market Predictions Author Author Hazrin Redžepi Abstract A summary of the resource. Financial markets are influenced not only by numerical indicators but also by public sentiment, which is often expressed through news, reports, and social media platforms. Traditional forecasting models typically rely on historical financial data, ignoring this important textual dimension. This research examines how sentiment analysis models can enhance the accuracy of financial market predictions. The research focuses on evaluating various classical machine learning algorithms for sentiment classification in financial texts. A publicly available dataset combining FIQA and Financial PhraseBank sources was used, containing over 5,800 labeled financial sentences. Data preprocessing steps included cleaning, tokenization, stopword removal, and lemmatization. Exploratory data analysis was conducted to understand sentiment distribution, text length patterns, and word frequencies. Sentiment labels were encoded numerically to serve as target variables in classification models. A range of traditional machine learning algorithms was implemented and assessed to explore their suitability for sentiment analysis in the financial domain. Evaluation metrics including accuracy, precision, recall, and F1-score were used to assess model performance. Preliminary results indicate that traditional machine learning models can effectively classify sentiment in financial texts, especially when supported by proper text preprocessing. Furthermore, integrating sentiment analysis with financial data shows potential for improving market forecasting accuracy. This work contributes to the growing field of financial data science by demonstrating the effectiveness of NLP-driven sentiment analysis and offering a framework for building predictive systems that combine textual and numerical financial indicators. Keywords Keywords. senior design project, sentiment analysis, machine learning, natural language processing, financial data science, stock market prediction, financial forecasting data science machine learning NLP sdp https://omeka.ibu.edu.ba/files/original/d4a06240ce96fab43f263cd22d42b9c0.pdf ca1d620b80bf88d267f654a1995666b1 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 NBA Players Comparison and Prediction Author Author Edin Duraković Abstract A summary of the resource. Basketball scouting and player evaluation have traditionally relied on subjective assessments by coaches, scouts, and analysts. With the advancement of data-driven decision-making, machine learning (ML) and natural language processing (NLP) provide new opportunities to quantify scouting reports and predict player performance in a structured way. This project aims to predict the statistical performance of college basketball players upon entering the NBA by integrating structured data (rookie-year statistics) with unstructured data (text-based scouting reports). The proposed system applies NLP techniques to extract features from scouting narratives, focusing on player strengths, weaknesses, and comparisons. These textual features are combined with quantitative performance metrics to develop predictive models for key statistical categories such as points, assists, rebounds, blocks, and steals. Clustering methods are further applied to group players with similar scouting profiles. Instead of labeling clusters as fixed archetypes, the approach emphasizes identifying the most comparable NBA players for each prospect. This similarity-based framework provides interpretability for scouting evaluations and a practical foundation for projecting performance. The predictive component then leverages historical outcomes of comparable players to generate forecasts for new prospects. The results demonstrate that clustering can organize players into meaningful groups based on textual analysis, while the predictive framework produces reasonable estimates of rookie-year performance. Together, these findings highlight the potential of integrating NLP and ML into scouting workflows to complement traditional evaluation methods, reduce bias, and enhance the accuracy of performance projections. Keywords Keywords. NBA scouting, player evaluation, natural language processing, clustering, prediction, machine learning https://omeka.ibu.edu.ba/files/original/405711553b574d8fdbc957a5deff9ac7.pdf 0c19e7ad5b81c69f372bca28387779a5 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 CINEMA TICKET BOOKING - QUIKYTIX Author Author Adnan Brkić Abstract A summary of the resource. In a world where leisure time is precious, finding a hassle-free way to book cinema tickets can often be a challenge, leading to inefficiency and inconvenience. To address this issue, we introduce Quiky Tix, a user-centric platform designed to streamline the cinema ticket booking experience. Quiky Tix leverages modern web technologies such as Next.js, React, and TypeScript for robust development and utilizes Prisma and PostgreSQL for database management. The application offers a comprehensive movie list with titles, descriptions, and trailers, empowering users to make informed decisions. Through an intuitive interface, users can easily select seats and personalize their cinema experience. The implementation of Quiky Tix results in a seamless and efficient ticket booking process. Users can register effortlessly, accessing features like storing tickets with scannable barcodes. Timely notifications keep users informed about purchases and upcoming releases, enhancing their overall experience. Quiky Tix redefines the cinema ticket booking landscape, providing a user-friendly solution to inefficiencies in traditional methods. By combining ease of use with modern technology, the application revolutionizes the way people enjoy movies. Through its streamlined approach, Quiky Tix ensures a seamless and enjoyable movie experience for all users. booking management system https://omeka.ibu.edu.ba/files/original/a466804a307f87da1bb51a73262bfb42.pdf 59ae545e6cbe6c4a7a18808ee06a14cf 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 TOURISTBOT: AI-POWERED MULTILINGUAL TRAVEL COMPANION FOR SEAMLESS TRAVEL ASSISTANCE IN BOSNIA AND HERZEGOVINA Author Author Selma Đozić Abstract A summary of the resource. Tourists visiting Bosnia and Herzegovina often encounter language barriers and difficulties in accessing relevant travel information in real-time, hindering their overall travel experience. Existing solutions are either limited in multilingual support or fail to deliver seamless, contextually appropriate assistance. This project represents a practical solution, namely, a TouristBot, an AI-powered, multilingual virtual travel assistant, designed to enhance the travel experience for visitors of Bosnia and Herzegovina. The system employs Natural Language Processing (NLP), and trained models, to process the user queries in English, and uses Google Cloud’s Translation API for enabling multilingual support, thus returning the answer in the user's original language. The backend, built in Python, using Flask framework, follows three-layered architecture, thus firstly searching for the answer directly from the custom dataset, then using pretrained models for prediction if the threshold for the first layer is not achieved, and finally mBert model is being used for deep semantic understanding if the first two layers fail. Also the frontend is built in JS’s framework, React-Native, for cross-platform functionality. That modular development methodology ensures higher reliability of the TouristBot, and eliminates the problem of the single point of failure. Ultimately, the outcome is a fully functional cross-platform mobile app, which aims to contribute to the tourism industry by offering an innovative, user-friendly solution that bridges language gaps and streamlines the travel experience in Bosnia and Herzegovina. Keywords Keywords. Natural Language Processing (NLP), virtual travel assistant, multilingual chatbot, cross-platform app, pretrained models, tourism in Bosnia and Herzegovina chatbot NLP https://omeka.ibu.edu.ba/files/original/db9cd6c74851eb266934d449e0e18e06.pdf a0af5d2b4a29f065a9ebd035e72278e5 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 DEVELOPMENT OF A BUSINESS MANAGEMENT APPLICATION: DOCUBOOK Author Author Faris Selimović Abstract A summary of the resource. This paper presents the design, implementation, and validation of DocuBook, a web-based business management application developed to reduce the administrative burden on small and medium-sized enterprises (SMEs) in Bosnia and Herzegovina. The system addresses critical challenges related to regulatory compliance, document management, and financial record-keeping by replacing error-prone, paper-based processes with a structured and secure digital platform. Developed using the Django framework, DocuBook provides centralized management for outgoing (KIF) and incoming (KUF) invoices, protocol-registered documents, and daily cash flow (DP) entries. Its modular architecture ensures maintainability and supports future expansion. The application has been successfully deployed in a live business environment, where it has demonstrated effectiveness in reducing administrative overhead, minimizing human error, and streamlining compliance procedures. The project confirms that a tailored, lightweight solution can significantly modernize operations for local businesses without the complexity and cost of large-scale enterprise systems. Keywords Keywords. business management, regulatory compliance, documentation system, Django, digital transformation business management Django https://omeka.ibu.edu.ba/files/original/771867435fe045d85b4bbfd55e69edaf.pdf d080d8a24752c37de4cf039f0c0dd1aa 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 GITHUB PROFILE ANALYZER Author Author ALI MARAT Abstract A summary of the resource. The GitHub Profile Analyzer was developed to assess developers' technical abilities by looking into the structure and meaning of their public repositories. Rather than just focusing on basic metrics like stars or forks, this project uses a combination of semantic embeddings (CodeBERT), structural analysis (ASTMiner), and rule-based heuristics for a more thorough understanding of coding practices. The analyzer has gone through several updates: CRAv2 set the foundation with Random Forest classifiers, CRAv3 took a semantic-first approach, and CRAv4 introduced a mixed strategy that fuses rules, semantic pattern analysis, and weighted confidence scoring. There was also an experimental Smart Repository Classifier (SRC) that tried to combine rule-based and machine-learning techniques, plus a side project that looked into detecting design patterns using AST embeddings and Code Property Graphs. The system was trained on 122 repositories and tested on 47 others across seven different categories. The results were impressive: CRAv3 hit an overall accuracy of 26.4%, while CRAv4 shot up to 70%, with big improvements in web applications (+133%) and CLI tools (+45%). Even though Random Forest tests only maxed out at about 35% in some categories, CRAv4's hybrid method turned out to be both more accurate and easier to understand. Some key hurdles included the lack of comprehensive datasets, issues with ASTMiner's dependencies, and the author's initial unfamiliarity with machine learning. Tackling these challenges taught valuable lessons in building datasets, feature engineering, and evaluating model In summary, this project highlights the benefits of blending semantic, structural, and rule-based strategies to gauge developer skills. Looking ahead, plans involve expanding datasets, diving into deep learning for design pattern detection, and enhancing the analyzer into a more general code intelligence tool. Keywords Keywords. GitHub analysis, semantic embeddings, ASTMiner, CRAv, machine learning, rule-based classification, design pattern detection 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/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