Browsing by Subject "LSTM"

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Aplikacja do rozliczania paragonów jako przykład ilustrujący problem digitalizacji dokumentów.
(Data obrony: 2019-07-26) Drzazga, Kamil
Wydział Fizyki i Informatyki Stosowanej
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Deep learning model for general stock price movement prediction
(Data obrony: 2020-01-23) Mosiński, Jakub
Wydział Inżynierii Mechanicznej i Robotyki
Access status: Open Access ,
Detection and forecasting of Parkinson disease progression from speech signal features using multi-layer perceptron and LSTM
(Wydawnictwa AGH, 2025) Majid, Ali; Hina, Shakir; Asia, Samreen; Sohaib, Ahmed
Accurate diagnosis of Parkinson′s disease, especially in its early stages, can be a challenging task. The application of machine learning (ML) techniques has helped improve the diagnostic accuracy of Parkinson′s disease (PD) detection but integration of diagnostic features in ML models for the prediction of disease progression has remained an unexplored research avenue. In this research work, Long Short Term Memory (LSTM) was trained using diagnostic features on Parkinson patients speech signals, to predict the disease progression while a Multilayer Perceptron (MLP) was trained on the same diagnostic features to detect PD. Diagnostic features were selected using two well known feature selection methods named Relief F and Sequential Forward Selection method. The integration of feature selection methods in LSTM model has resulted in PD progression forecast with an accuracy of 88.7%. Furthermore, with the application of input diagnostic features on MLP, PD stage was accurately detected with an accuracy of 98.63%, precision of 97.64% and recall of 98.8% showing model robustness and efficiency for its potential application in health care.
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Evaluation of prediction mechanisms in time series
(Data obrony: 2019-07-08) Kos, Kamil
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Intrusion detection with machine learning: a two-step federated approach using the CIC IoT 2023 dataset
(Wydawnictwa AGH, 2025) Jakotiya, Komal; Shirsath, Vishal; Inamadar, Sharanabasava
The main objective of the planned effort is to provide analytical analyses of current intrusion detection systems grounded on ML algorithms. Furthermore, examined in this work are the useful data sets and several techniques already in use to develop an effective IDS using single, hybrid, and ensemble machine learning algorithms. The approaches in the literature have then been investigated under several criteria to provide a clear road and direction for the next projects that will be successful. Nowadays, companies of all kinds include an intrusion detection system (IDS), which inhibits cybercrime to protect the network, resources, and private data. Many strategies have been suggested and implemented up till now to prevent uncivil behaviour. Since machine learning (ML) approaches are successful, the proposed approach applied several ML models for the intrusion detection system. The CIC IoT 2023 Dataset is the one applied in this paper, and a two-step process for Intrusion detection was proposed. Tested with several techniques including random forest, XGBoost, logistic regression, MLP model, and RNN. Following fine-tuning, the federated learning model using neural networks had the best accuracy – 99.84%.
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Klasyfikacja danych tekstowych przy pomocy rekurencyjnych sieci neuronowych
(Data obrony: 2018-01-23) Łyko, Tomasz
Wydział Informatyki, Elektroniki i Telekomunikacji
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Predykcja wyniku partii szachowej z wykorzystaniem sieci neuronowych
(Data obrony: 2020-07-16) Wątor, Grzegorz
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Open Access ,
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets
(Wydawnictwa AGH, 2025) Ogonowski, Aleksander; Żebrowski, Michał; Ćwiek, Arkadiusz; Jarosiewicz, Tobiasz; Klimaszewski, Konrad; Padee, Adam; Wasiuk, Piotr; Wójcik, Michał
potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. Our investigation utilizes the CIC-IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.
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Prognozowanie jakości powietrza z wykorzystaniem sieci neuronowych
(Data obrony: 2019-12-06) Jawor, Klaudia
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
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Rozdzielanie sygnału audio przy zastosowaniu sieci neuronowych
(Data obrony: 2020-05-25) Kwiatkowski, Kamil
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
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Struktura i własności sieci neuronowych typu LSTM
(Data obrony: 2021-02-01) Pytel, Adrianna
Wydział Fizyki i Informatyki Stosowanej
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Zastosowanie i analiza różnych algorytmów sieci neuronowych do przewidywania cen kryptowalut
(Data obrony: 2020-10-27) Pasiut, Bartłomiej
Wydział Informatyki, Elektroniki i Telekomunikacji
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Zastosowanie uczenia maszynowego do poprawy jakości dźwięku
(Data obrony: 2020-10-29) Hałaciński, Roman
Wydział Informatyki, Elektroniki i Telekomunikacji