http://hdl.handle.net/20.500.12323/7585 2026-04-04T04:51:01Z http://hdl.handle.net/20.500.12323/7600 Title: Analyzing Credit Card Fraud Cases with Supervised Machine Learning Methods: Logistic Regression and Naive Bayes Authors: Habibullayeva, Naila; Kalejahi, Behnam Kiani Abstract: Frauds involving credit cards are simple and simple to target. With the rise of online payment credit cards have had a huge role in our daily life and economy for the past two decades and it is an important task for companies to identify fraud and non-fraud transactions. As the number of credit cards grows every day and the volume of transactions increases quickly in tandem, fraudsters who wish to exploit this market for illegitimate gains have come to light. Nowadays, it's quite simple to access anyone's credit card information, which makes it simpler for card fraudsters to do their crimes. Thanks to advances in technology, it is now possible to determine whether information gained with malicious intent has been used by looking at the costs and time involved in altering account transactions. The Credit Card Fraud analysis data set, which was obtained from the Kaggle database, was used in the modeling process together with The Logistic regression method and Naive Bayes algorithms. Using the Knime platform, we are going to apply machine learning techniques to practical data in this study. The goal of this study is to identify who performed the transaction by examining the periods when people used their credit cards. The Logistic regression approach and the Naive Bayes method both had success rates of 99.83%, which was the highest. The two methods' results are based on Cohen's kappa, accuracy, precision, recall, and other metrics. These and many more outcomes are shown in the confusion matrix. 2023-01-01T00:00:00Z http://hdl.handle.net/20.500.12323/7599 Title: Selection of Artificial Lift Method Authors: 𝐆𝐚𝐬𝐢𝐦𝐨𝐯𝐚, 𝐊𝐡𝐚𝐝𝐢𝐣𝐚 Abstract: For most of producing oil and gas wells to be profitable over the long term, the appropriate artificial lift technique must be chosen. The present research examines the key selection criteria for the eight current primary artificial lift techniques and offers useful recommendations on the performance and applicability of the techniques based on real-world and tested technology. This paper discusses plunger lift, gas lift, hydraulic jet pumping, beam pumping, progressive cavity pumping, electric submersible pumping. The main goal of this study is to choose the best method, such as natural drive and artificial lift methods for accelerating and optimizing hydrocarbon production in the ARC oil field using the PROSPER software package and fictitious well data from Well WE1. The electrical submersible pump method is the subject of the nodal analysis for both natural drive cases and artificial lift techniques on PROSPER. Natural drive, ESP have calculated oil production rates of 57.2sm3 /day, 94.4 sm3 /day respectively. According to this study, using artificial lift techniques dramatically boosts oil production. 2023-01-01T00:00:00Z http://hdl.handle.net/20.500.12323/7598 Title: Building IOS App for Language Learning Authors: Samadova, Ilaha; Muradkhanli, Leyla Abstract: This paper presents the development of an iOS app for language learning, aimed at enhancing user experience and engagement. The app is designed to facilitate language learning by providing definitions, vocabulary lists, and pronunciation practice in a mobile platform. The app was developed using Swift programming language and integrated with a cloud-based server to enable real-time data synchronization and user tracking. The study conducted an evaluation of the app's user experience and engagement through a usability test and user survey. Results showed that the app is effective in enhancing language learning experience and user engagement, particularly in its interactive and personalized approach. The development of the iOS app for language learning with embedded definitions was an extensive process that involved a wide range of considerations. The main objective of this project was to create an app that provides a comprehensive learning experience for English language learners. The development of this app was made possible through the use of Xcode and Swift programming language. Xcode is an integrated development environment (IDE) that allows developers to create apps for Apple devices, while Swift is a programming language developed specifically for Apple platforms. The app's design was carefully crafted to ensure that it is easy to use, engaging, and interactive. The user interface (UI) was designed to be intuitive, allowing users to navigate the app with ease. The app's color scheme and typography were chosen to be visually appealing and consistent with modern design trends. Additionally, the app's design was optimized for different screen sizes, ensuring that it looks great on all iOS devices. One of the key features of the app is its embedded definitions. This feature allows users to easily understand the meanings of new words by providing definitions within the app. This helps learners develop their vocabulary and comprehension skills in a natural way. The app also includes sample sentences to provide context for the new words and help users understand how they are used in context. The app also includes pronunciation exercises to help users improve their pronunciation skills. The app's pronunciation exercises are designed to be fun and engaging, encouraging users to practice and improve their pronunciation skills.Overall, the development of the iOS app for English language learning with embedded definitions was a significant undertaking that involved a wide range of considerations. The app's features, design, and technological advancements were carefully crafted to provide a comprehensive learning experience for English language learners. With its embedded definitions, sample sentences, and pronunciation exercises, the app is an effective tool for improving users' vocabulary and comprehension skills. 2023-01-01T00:00:00Z http://hdl.handle.net/20.500.12323/7597 Title: Quantum Dots in Semiconductors for Coming Optical Applications Authors: Emdadi, Babak Abstract: Review of semiconductor quantum dots (QDs) development and research for optical applications. The QDs are tiny crystals, around 10 nm in size, made of semiconductors III-V, II-VI, IV, and IV-VI. They are divided into two categories. The self-assembled QDs, which are grown epitaxially on a semiconductor substrate, are one type. The other type is colloidal QDs, which are chemically produced in a solvent. Due to the fact that both QDs' emission wavelengths span a broad spectrum, from visible to infrared, the QDs are appealing to a variety of application domains. In the areas of replacing current devices, quantum information devices, and solar energy conversion devices, research on both epitaxial QDs and colloidal QDs has advanced. The QD devices will be crucial to the 21st century's large-capacity information-oriented civilization and the solution to the energy crisis. The three application sectors, namely the replacement of current products, quantum information devices, and solar cells, are the main topics of this article. 2023-01-01T00:00:00Z