1 10 15 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/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. 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. 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/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. 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. 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. 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. 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. Keywords Keywords. Machine Learning, Sport Results, Prediction machine learning master's thesis prediction sport results 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. 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. 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. 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. 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. 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/b405bccb488e75bf8fb55f27f34023be.pdf a77087e12a6314709f1c2dad1d73a655 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis Techniques and Applications in the News Articles Author Author Mirza Novalić Abstract A summary of the resource. Sentiment analysis is essential for understanding public opinion, especially in the context of news articles, where tone and sentiment can significantly impact and control readers' perception and understanding of the content. This study explores a variety of sentiment analysis techniques that are applied to a vast amount of articles gathered from “New York Times” in the past two decades. The research focuses on the performance of traditional machine learning models, deep learning models and hybrid approaches. The aim of the paper is to answer three key questions regarding which approach is the most suitable for this problem and how fine-tuning affects end results. To address these questions, throughout the research, traditional machine learning models including Naive Bayes, Linear Support Vector Classification (SVC) and Logistic Regression were implemented. Among these approaches, Linear SVC achieved the best scores across all evaluation metrics. In the deep learning category, Long Short-Term Memory (LSTM) networks were applied. This approach provided exceptional performance which was overall better than traditional models. RNNs scored similarly as Linear SVC, while outperforming other traditional algorithms. A hybrid approach including the BERT model was another method that was explored, which combined specific architecture with deep learning-based contextual understanding. The results demonstrated high classification results, which supports the hypothesis that hybrid models can increase performance of sentiment prediction. Furthermore, fine-tuning of different models improved their performance, which highlights the importance of optimizing pretrained models for specific types of analysis. Overall, the findings confirm that deep learning models usually outperform traditional variants of machine learning methods while hybrid models can offer additional potential and perspective for enhancing sentiment classification in news articles. The study provides deep and valuable insights into effectiveness of different sentiment analysis and natural language processing (NLP) techniques, while at the same time discussing new possibilities and improvements in the field. Keywords Keywords. Sentiment Analysis, Traditional Algorithms, Deep Learning, Fine-Tuning, Hybrid Approach deep learning fine-tuning hybrid approach sentiment analysis Traditional Algorithms https://omeka.ibu.edu.ba/files/original/85f02200d8b6a059ec3861f417da6e72.pdf 1f82ffbbe02c17154c3399fe61ce48b1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis And Price Prediction For Accommodation Reviews in Bosnia And Herzegovina: A Comparative Study of NLTK and Hugging Face NLP Techniques Author Author Amila Čaušević Abstract A summary of the resource. The growing field of natural language processing (NLP) has huge potential in the advancement of consumer feedback and its application in determining pricing strategy in the hospitality industry. In this thesis, sentiment analysis and price predictions of accommodation reviews in Bosnia and Herzegovina are analyzed through a comparative study of two of the most commonly used approaches in NLP: NLTK - representing traditional methods, and Hugging Face - representing modern techniques. Initially, a long process of text preprocessing is performed that includes tokenization, lemmatization, stopword removal, and filtering of positive and negative reviews. Quantitative analysis such as word frequency distributions, measures of lexical diversity, and word co-occurrence tests reveal patterns within language use as well as the relationship between review attributes and sentiment. Different frameworks for sentiment analysis are then compared. The Hugging Face sentiment pipeline and more modern and recent transformer architectures like BERT, RoBERTa, and XLNet are compared with more traditional techniques (e.g., NLTK/VADER). Metrics for evaluation such as accuracy, precision, recall, and F1-score are used to assess the performance of the sentiment models. In order to develop predictive price models based on regression techniques like Linear Regression, Random Forest, and Gradient Boosting, the thesis additionally integrates sentiment scores with quantitative metadata, such as review ratings, location ratings, and accommodation categories. The results show that Random Forest regression is the most effective method for identifying subtle, non-linear sentiment-price correlations, even though transformer-based sentiment analysis can show promise in identifying subtle signals within guest reviews. Last but not least, this work offers helpful recommendations to help hoteliers in Bosnia and Herzegovina to create focused pricing strategies while also enhancing the general guest experience. Keywords Keywords. NLP, Sentiment Analysis, Accommodation Reviews, Price Prediction, Bosnia and Herzegovina Accommodation Reviews Bosnia and Herzegovina NLP Price Prediction sentiment analysis https://omeka.ibu.edu.ba/files/original/1e77e2888b1af34ce1b04e8c6c8b1578.pdf 3102207937ea6ff2b0c16d894cdecc49 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Analysis of Machine Learning Algorithms for Real Estate Price Prediction: Bosnia and Herzegovina vs. USA Author Author Adnan Krndžija Abstract A summary of the resource. Real estate markets are impacted by a variety of variables, including changes in the population, urban development projects, and changes in economic policy. This thesis sets out to investigate the effectiveness of machine learning algorithms in predicting real estate prices, paying close attention to the particular circumstances of Bosnia and Herzegovina as well as the United States. While the US real estate market has a long history and is well-known for its capacity to bounce back from downturns in the economy, the tale of the BiH real estate industry is very different. In contrast to the United States, which has seen centuries of economic expansion, financial crises, and legislative changes, Bosnia and Herzegovina's market development is a result of a combination of past influences and present difficulties. Beyond simple quantitative comparisons, our research takes a holistic method to uncover the predictive capability of machine learning models. We explore the complexities of random forests and decision trees, making use of their ability to reveal intricate patterns in real estate databases. This research also includes time series modeling to recognize and comprehend the evolving patterns that characterize real estate dynamics throughout time. The analysis of SARIMAX, ARIMA, and Holt-Winters time-series models shows ARIMA's consistent accuracy, while SARIMAX and Holt-Winters excel in stability and trend capture, respectively. In machine learning, Decision Trees offer interpretability, while Random Forests show reduced error rates and enhanced accuracy. In the US dataset, SARIMAX has a Mean Absolute Percentage Error (MAPE) of 3.35% and ARIMA achieves 1.66%, while Holt-Winters shows 3.54%. Decision Trees have a MAPE of 2.97%, and Random Forests achieve 2.10%. In the BiH dataset, SARIMAX has a MAPE of 5.08%, ARIMA achieves 1.22%, while Holt-Winters shows 2.17%. Decision Trees have a MAPE of 0.83%, and Random Forests achieve 0.82%. Keywords Keywords. Real Estate, Prediction, Bosnia and Herzegovina, United States Bosnia and Herzegovina master's thesis prediction Real Estate United States https://omeka.ibu.edu.ba/files/original/898d3390481715985e1899366de2077e.pdf c83f8b36a7dcda7b6bb84552da258b06 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimization of Email Marketing Campaigns Leveraging Machine Learning Techniques Author Author Nejla Zukorlić Abstract A summary of the resource. Email marketing is widely recognized as an effective digital marketing channel, offering a considerable return on investment (ROI). One key challenge is determining the optimal day and time to send emails to maximize customer response rates. This thesis explores the application of machine learning (ML) algorithms to predict the best send times for email marketing campaigns, focusing on improving response rates. The research utilizes historical email marketing data, including customer demographics, response behavior, and email send dates. Based on this data, various machine learning models, including decision trees and random forests, as well as ensemble methods at the end, will be used to predict the optimal day for sending emails. The study will also examine how factors like customer age and tenure influence response rates at different times. The question is if the machine learning-based predictions of the optimal send day and time will significantly improve response rates compared to traditional methods. Also, incorporating demographic factors, such as age and tenure, hopefully will improve the accuracy of these predictions. The expected outcome is that MLbased optimization will outperform traditional scheduling methods, providing a more effective and data-driven strategy for email campaign timing. Keywords Keywords. Email Marketing, Machine Learning, Send-Time Optimization, Customer Engagement, Response Rates, Data-Driven Marketing Customer Engagement Data-Driven Marketing Email Marketing machine learning master's thesis Response Rates Send-Time Optimization https://omeka.ibu.edu.ba/files/original/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. 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. 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. 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. 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/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