AUK Digital Repository
American University Kyiv electronic data repository, also called an e-archive or centralized data repository
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Recent Submissions
3D VISUALIZATION OF A ROOM USING A GROUND-BASED DRONE
(Manuscript, 2025-06-09) Shevchenko, Dmytro
This project presents the design and implementation of a low-cost, mobile 3D scanning system using the Intel RealSense
D435i RGB-D camera mounted on a wheeled rover. The system
performs indoor environment reconstruction by alternating
between precise 360° panoramic scans and short forward
movements, accumulating depth data at each waypoint.
Combining IMU feedback, servo control, and depth sensing, it
builds point clouds without high-end LiDAR. The result is a 3D
model suitable for tasks like inspection, logistics, or demining
support. The modular software stack, based on Open3D and
Python, enables fast prototyping and flexible deployment in
real-world environments
MACHINE LEARNING METHODS AND SOFTWARE TOOLS FOR PREDICTING CYCLICAL ECONOMIC PROCESSES IN A STOCK MARKET
(Manuscript, 2025-06-09) Saveliev, Myron
This capstone project focuses on forecasting cyclical economic trends in the stock market, using
Apple Inc. (AAPL) stock data as a case study. Key research questions include: (1) How effective are
machine learning models in forecasting market cycles? (2) Which models provide the most accurate
predictions? Historical stock price data from 2010 to 2023 was collected from Yahoo Finance, and seven
forecasting models were implemented: ARIMA, SARIMA, Gradient Boosting, Random Forest, LSTM,
GRU, and Prophet.
The research involved preprocessing the data, performing exploratory analysis, and evaluating
the performance of each model using key metrics, including RMSE, MAPE, and R². Results
demonstrated that ARIMA and SARIMA provided the most accurate forecasts, achieving an RMSE of
3.11, MAPE of 0.01, and R² of 0.98. Among deep learning models, LSTM outperformed GRU with an
RMSE of 4.35 and R² of 0.96, effectively capturing non-linear dependencies. Gradient Boosting, Random
Forest, and Prophet models underperformed, with higher RMSE and negative R² values, making them
less suitable for this task.
The study concludes that ARIMA and SARIMA are optimal for efficient and accurate
forecasting, while LSTM is well-suited for complex, non-linear patterns. Future work could explore the
incorporation of macroeconomic indicators, hybrid models, or alternative data sources, such as sentiment
analysis, to enhance predictive accuracy. This research offers practical tips for utilizing machine learning
in financial forecasting.
DEVELOPMENT OF ENHANCED MACHINE LEARNING METHODS FOR THE AUTOMATIC MODULATION CLASSIFICATION OF QAM SIGNALS
(Manuscript, 2025-06-09) Savenkov, Andrii
This study evaluates multiple machine learning approaches for Automatic Modulation Classification (AMC) of five digital modulation schemes: BPSK, QPSK, 8PSK, 8QAM, and 16QAM. The research implements and compares four classification methods: Gaussian Mixture Models, multinomial regression with polynomial features, nearest neighbor approach, and Convolutional Neural Networks. The system is integrated into a web service using Elixir/Phoenix framework and Python. Experimental results demonstrate high accuracy across all methods, with CNNs achieving 99% accuracy on simulated signals and consistent 99% accuracy on real signals from a CDM-625 modem. The study provides a practical solution for deployment in modern communication systems.
ЗВІТ БІБЛІОТЕКИ ЗА 2024 РІК
(2024) Guzhva, Alla
У звіті описано основну операційну діяльність Бібліотеки АЮК та головні статистичні показники.
MOTIVATION TOOLS IN COMPANIES WITH DIFFERENT LEVELS OF INNOVATION
(Manuscript, 2025) Zasiedatielieva, Yaroslava
The topic of the research is to analyse the impact of motivational tools on the innovative development of companies in the context of the countries' positions in the Global Innovation Index. The main question of this study is how innovative companies use motivational strategies to stimulate creativity and innovative development. The participants are companies from countries that currently rank high in the innovation rankings (Switzerland, Sweden, the United States, Singapore, and the United Kingdom), as well as from countries that currently rank low in the innovation rankings (Nigeria and Nepal). For each country, two companies were selected: one large and well-known company engaged in innovation or technology development, and one medium-sized or local company. The method of data collection was based on content analysis of official materials, including corporate websites, HR policies, reports and media publications. The results of the research show that Self-Determination Theory is an effective tool for stimulating innovation in both high and low ranking countries in the Global Innovation Index. Potential implications of the study include recommendations for companies to develop effective incentive strategies that promote innovation. Future research could be focused on creating universal incentive models that consider global and local specifics.