8 10 3494 https://omeka.ibu.edu.ba/files/original/f6920e7597178a744db73ca7a65acede.pdf c2b6a783fa42ff5765c2eb66aafc6a50 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 BOOKS 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 Transgressive Space in Literature: Theory in Practice Author Author Ibrahim Murat Öner Publisher An entity responsible for making the resource available International Burch University https://omeka.ibu.edu.ba/files/original/32457844bdc2553cb82d2f01f486d89e.pdf 31a94ce65e9b2acb9f5da0be97ca7c0e 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 Credit Card Fraud Detection Using Machine Learning Algorithms and Data Analysis Techniques Author Author Sarah Kozić Abstract A summary of the resource. In today’s world usage of card-based and online payment methods is rapidly increasing, and with this growth comes the issue of cybersecurity and overall fraud. The credit card fraud rate has never been higher, and it is following a growing trend.<br /><br />Therefore, improvement of credit card fraud detection systems is the main priority for all banks, systems that are providing credit card-based payments and all the participants in the digital payments market. This also comes for the purpose of the large percentage of the population that is using their credit cards daily, from everyday payments to international transactions that are of great value.<br /><br />The goal was to train multiple models to define if referenced transactions should be treated as fraud, and the results were measured by standard machine learning parameters. The model that had best results is Ensembled model using Decision Tree, Logistic Regression and K-Nearest Neighbor models with overall accuracy of 99.91% with Feature Selection algorithm applied. Ensemble method combines multiple models and creates the model with the best metrics possible. Along with this model, we have trained Logistic Regression model, K-Nearest Neighbors, Support Vectors Machines and Neural Networks, with accuracies respectively 88.37%, 85.48%, 00.73% and 98.11% with features selected.<br /><br />This research also covers the part of data preprocessing, as this step is crucial when building a model for credit card fraud detection systems. These systems must be fast and precise in order to be usable, as they are dealing with large sets of imbalanced data.<br /> <p>At the end of the study, individuals will have better insight in credit card transactions, will also be familiar with the different methods for detecting credit card frauds and will have insight in which model suits the needs of this case the most.</p> Keywords Keywords. Credit Card, Fraud, Transaction, Machine Learning Algorithms, Classification, Dataset Preprocessing algorithms classification credit card dataset preprocessing fraud machine learning master's thesis transaction https://omeka.ibu.edu.ba/files/original/b0fb3a777e0d8db43479c1568e3648d5.pdf 6bfa35ecaa5b9c1ceb5ab4ea446bcee7 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 Sports Results Prediction Using Machine Learning Author Author Alden Obradović Abstract A summary of the resource. <p>Over recent decades, machine learning technology has increasingly been used to predict sports performance. The sports industry generates extensive statistical data on players, teams, and seasons. Traditional prediction methods have shown limited accuracy. With data mining, sports organizations have recognized the outdated analysis in their data and are now utilizing it effectively.</p> <p>The goal of this thesis is to explore accurate sports result predictions. Identifying significant features and analysing their impact on match outcomes is essential. Key variables include team statistics, player statistics, and historical data. These factors help managers and club directors forecast match winners and determine strategies. Machine learning techniques like KNN, Random Forest, logistic regression, and SVM are often applied to predict match results.</p> <p>These predictions help coaches and managers assess player performance, evaluate skills, anticipate injuries, and strategize for upcoming games. Additionally, accurate predictions have significantly fuelled the sports betting industry, which is expanding rapidly thanks to the convenience of mobile and tablet devices.<br /><br />This research proposes an AI-based framework to predict football match outcomes. Itexamines the effectiveness of system learning algorithms and reviews data mining techniques for predicting sports performance, highlighting their strengths and weaknesses. Despite previous research attempts, achieving high precision in game result predictions remains challenging.</p> Keywords Keywords. Machine Learning, Sport Results, Prediction machine learning master's thesis prediction sport results https://omeka.ibu.edu.ba/files/original/77cb7e13b96d9afa8c0bbf3b0a8ce5b9.pdf 1f49214f1fcfe31a520f996a66583162 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 Application of Machine Learning in Neuromarketing Research for the Analysis of Customer Preferences Author Author Admir Krilašević Abstract A summary of the resource. <p>Neuromarketing combines neuroscience and marketing to analyze consumer behavior through tools like electroencephalography (EEG), which captures subconscious and emotional responses. This thesis applies machine learning (ML) techniques to EEG data for predicting purchase decisions, addressing the limitations of traditional marketing methods. Using the NeuMa dataset, which includes EEG and eye-tracking data, key features such as frontal alpha asymmetry (FAA), power spectral density (PSD), and alpha-beta power ratios were extracted to build predictive models. Four ML algorithms—Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—were evaluated based on accuracy, ROC AUC, and execution time. SVM emerged as the best-performing model, achieving 94.3% accuracy. 99% ROC AUC, with efficient processing time, making it suitable for neuromarketing research. The results confirm the critical role of EEG features from the frontal region, particularly FAA and alpha-beta power ratios, in predicting consumer preferences. These metrics reflect emotional and subconscious responses, emphasizing their importance in purchase decisions. This study demonstrates the value of integrating EEG with ML for consumer analysis, offering a scalable, unbiased, and data-driven approach to marketing research. By combining neuroscience with modern methods, this research provides a foundation for improving consumer preference analysis. It highlights the potential of EEG-based metrics and ML models to enhance marketing strategies, moving beyond traditional self-report methods toward more objective and accurate insights.</p> Keywords Keywords. Neuromarketing, Machine Learning, EEG, Consumer Behaviour, Neural Networks, SVM, Frontal Alpha Asymmetr consumer behaviour EEG frontal alpha asymmetr machine learning master's thesis neural networks neuromarketing SVM https://omeka.ibu.edu.ba/files/original/fd76526093c7238ce55f4b166ca6764c.jpg a85fe68e363e21c47c6b6a88e44bc90b https://omeka.ibu.edu.ba/files/original/871be2531dd5fd486d1e030ff138eb1b.pdf 818e536035b8d512f606a4cc9f16a862 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 Cancer Cells Detection Using Supervised Machine Learning Author Author Ajla Džaferbegović Abstract A summary of the resource. Cancerous cells invade and destroy the healthy tissue of the body, including organs. It often begins in one part of the body before spreading uncontrollably to other areas of the organism. According to the World Health Organization (WHO), cancer is the cause of death worldwide - taking around 10 million lives yearly. The predominant cancers are colon, breast, lung, rectum, and prostate. Early detection is crucial to increase survival chances tremendously. Machine Learning (ML) tools have the potential to recognize key features in complex datasets enabling the classification of low and high risk patients. This research focuses on the use of supervised machine learning algorithms, such as Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs) for the development of predictive models, expected to result in effective and accurate decision-making based on available scientific experiments. The results of the study have showcased high accuracy rates (above 90 percent) on all applied models, with the highest accuracy scores using Feed Forward Neural Networks (approx. 97 percent). The use of machine learning methods can enhance the overall understanding of cancer progression, early detection, and treatment; however, thorough medical validation from professionals is essential for these methods to be adopted into routine clinical practice. The idea of adopting machine learning in the medical field is not to substitute human intelligence but to aid patients in receiving faster healthcare. Keywords Keywords. Cancer Cells, Machine Learning, Predictive Models cancer data science machine learning master's thesis prediction https://omeka.ibu.edu.ba/files/original/3f47c819268e8944166d7462c61a1e13.pdf a4f0a7101d48b2cf011decf055b2c1a6 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 Hotel Reservation System Author Author Haris Hukara Abstract A summary of the resource. Many domestic hotels do not have a nice solution for the hotel reservation system. Some hotels do not have web applications at all, while others are purely designed and hard to use. This presents a huge problem and customers are not satisfied. In the twenty-first century almost everything is available online and if your business is not reachable you are likely losing out to your competitors. On the other hand, having a nice web application may only help you grow your business. The main intention of this application is to make room reservations simpler, less time-consuming and easier for managing. Hotel Reservation System is a web-based system that enables users all over the world to make room reservations. This system supports two types of users which are employees and guests. Guests should be able to quickly make a reservation request for a specified date and room. On the other hand, employees can easily interact with the database and fetch the details of the reservation request and customer that created it. Also, they can approve, reject and update reservation details. Functionalities of this application will help to keep track of the available rooms and the customers that are staying in the hotel. It is designed to be very intuitive, easy to use and navigate around, but under the hood it has some relatively complex logic. Keywords Keywords. reservation, system, web, application, responsive, PHP, MySQL, HTML, CSS, JS https://omeka.ibu.edu.ba/files/original/9233db6b5f27ef3a67490513f37aed17.pdf 8a155102af2e3cbe133923a378ebc75c 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 ResolveNet - Optimizing IT Service Desk Operations Using Agent-Based Modeling in NetLogo Author Author Adnan Mutilović Abstract A summary of the resource. IT service desks are an important aspect of maintaining IT systems by processing user reported problems, managing service requests, and assigning resources efficiently. Real world service desks, nonetheless, suffer from inefficiencies including high backlog of tickets, uneven staff workload, and slow response time. In this thesis, we introduce ResolveNet, an agent-based model (ABM) in NetLogo to model and optimize the operations of an IT service desk. The model recognizes the ticket processing dynamics, workload distribution, and user satisfaction to understand performance improvement. Through a comparative study of simulated data and real world IT service desk logs, this study identifies critical inefficiencies in ticket resolution workflows. The results are that priority-based ticket assignment, automated workload leveling, and predictive analytics yield significant efficiency improvements, reducing average ticket resolution time by up to 40% and improving user satisfaction ratings. Scenario-based performance evaluation also identifies staff shortage, ticket spikes, and lack of automation as primary drivers of backlog accumulation. The findings of this study have practical implications for IT service management by providing data driven strategies to enhance operational performance. Future upgrades may involve real-time integration with live IT service desks, machine learning driven optimization models, and further automation of routine ticket resolutions. Keywords Keywords. IT service desk, Agent-Based Modeling, NetLogo, ticket resolution, workload balancing, user satisfaction, service optimization. https://omeka.ibu.edu.ba/files/original/a2efba5468ee04e85f9c649a9d0d1f0f.pdf 00a47ae23f75f2dd6c7fb4bd16b3822b 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 Interactive Budgeting Web App with Long-Term Financial Impact Analysis Author Author Amina Ajkunić Abstract A summary of the resource. Effective personal financial management is a major problem that people all over the world face, particularly in regard to growing living expenses and financial issues. Typical budgeting techniques frequently fall short in offering consumers the interactive and predictive insights necessary to make wise financial decisions. This project presents an interactive web application for budgeting that gives users the ability to make wise financial decisions and plans. Users can classify expenses, track payments, make budgets, and examine the long-term financial effects of their choices with this tool. Combined dynamic features like income and expense forms, interactive spending category charts, and a financial summary dashboard with total incomes, expenses, and net balances are important to its design. In order to create an intuitive and visually appealing interface, the development process combined responsive web technologies like React.js and CSS with a user-centered design approach. The program integrates capabilities to predict possible savings over predetermined timeframes and dynamically modifies aspects such as color-coded transaction lists based on type (income or expenses) to improve usability. Advanced features that help customers reach their financial objectives include future savings predictions and customized financial guidance. The project shows how technology may be used to promote disciplined financial habits and increase financial literacy. Users expressed more trust in the ability to properly manage their finances and connect their spending to goals for the future. The application offers practical advice on reaching financial security and doubles as an educational tool in addition to being a personal budget tracker. Keywords Keywords. budgeting, financial planning, web application, interactive dashboard, savings projection, user-centered design. Language A language of the resource Bosnian language sdp software engineering https://omeka.ibu.edu.ba/files/original/c50a0942a1a03c62de846628aefce456.pdf c068049fa4e801e6e9751c07d1fe9f62 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 TripConnect: Travel Booking Made Simple Author Author Sead Gačanović Abstract A summary of the resource. This project aims to develop a web-based travel application that allows users to search, view, and book hotel rooms and flight tickets. The system facilitates hotel and flight searches, user account management, and booking history tracking. With a robust backend built on Java and MongoDB, the application also ensures secure user authentication and authorization using JWT (JSON Web Tokens). The front end leverages modern technologies such as HTML, CSS, and Bootstrap for a seamless user experience. Keywords Keywords. travel application, hotel booking, flight booking, Spring Framework, JWT, MongoDB Language A language of the resource English language sdp software engineering web development https://omeka.ibu.edu.ba/files/original/fd55fdbe0850f3abd4549cd9ca0d57d0.pdf 9f2c21bcdf13358080aafa5d58f175df 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 TanjirVise Mobile Application Author Author Minela Mustafi Abstract A summary of the resource. TanjirVise system aims to create a surplus food redistribution platform that addresses the major food waste problem that could be used for social welfare. Restaurants discard significant amounts of excess food while there are so many individuals who struggle to provide even a single meal for themselves and their families daily. TanjirVise system is designed to bridge this gap by connecting restaurants with socially disadvantaged groups with the help of volunteers. TanjirVise was developed using the .NET Core framework for the backend, React Native for the mobile client, and MS SQL server for the database. Key functionalities include user registration, authentication through login, creation of food requests, and their management and tracking. TanjirVise is deployed using Azure, precisely Azure App Service for backend deployment and Azure SQL Database for database hosting. Mailgun is integrated as an email service for email verification. It also implements JWT-based authentication and role-based authorization, guarding the system from unauthorized access and ensuring that only authorized users can access specific features. TanijrVise shows how technology can be used to connect different things for mutual benefit, and the benefit of the community. By providing a platform for connecting restaurants with surplus food and socially disadvantaged groups, it offers a solution to two problems: it helps reduce food waste and supports those in need. Keywords Keywords. food redistribution, food waste, social welfare, social impact, .NET Core, React Native Language A language of the resource English language sdp software engineering