Faculty of Electrical Engineering and Information Technologies

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    Image Segmentation as an Instrument for Setting Attention Regions in Convolutional Neural Networks for Bias Detection Purposes
    (2023)
    Velichkovska, Bojana
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    Efnusheva, Danijela
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    Convolutional neural networks (CNNs) are constantly being used for medical image processing with increased application in publicly available datasets and are later being actively applied in medical practice. Therefore, since patient lives are at stake, it is important that the functionality of the neural network is beyond reproach. In this paper, due to dataset availability, we present two lung segmentation approaches using traditional image processing and deep learning methodologies; these approaches can later be used to focus a CNN for image segmentation and classification tasks, with implementations spanning everything from disease diagnosis to demographic and bias analysis. The aim of this paper is to provide a framework for segmentation in medical images of the chest cavity, as a way of applying attention regions and localizing sources of bias in images. Both of the proposed segmentation tools, the traditional image approach using computer tomography scans and the CNN applied to chest X-rays, provide excellent lung segmentation comparable to popular methods in the image processing sphere. This allows for an all-encompassing application of the developed methodology regardless of different image formats, therefore making it widely applicable in setting attention regions for CNNs.
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    Machine Learning-Based Forecasting of Bitcoin Price Movements
    (2024)
    Angelovski, Darko
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    Velichkovska, Bojana
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    ;
    Efnusheva, Daniela
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    In the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks, Gradient Boosting, LightGBM (LGBM) Regressor, and Random Forest algorithms to unravel the complexities of price movements. We perform a comprehensive analysis, and observe patterns and dependencies within historical data at hour-long intervals in the last 30 and 45 days, by using a holdout technique with 80% of the data used for training and 20% used for testing. We evaluate the models using four standard regression metrics. The training data incorporates a diverse range of features capturing hourly trends, day-of-the-week variations, and the correlation between opening and closing prices. Our study delves into the ability for forecasting Bitcoin price movements using ensemble algorithms and LSTM. The results show best performance for the LSTM models, especially when trained on longer training intervals. Namely, our LSTM model obtains R2 of 0.98 when trained on 30 days and 0.99 when trained on 45 days. In comparison, the ensemble methods show volatility and lower predictive ability.