Browsing by Subject "MLP"

Now showing 1 - 5 of 5

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.
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%.
Access status: Restricted ,
Opracowanie Systemu Ekspertowego do przewidywania zagrożeń internetowych
(Data obrony: 2020-01-22) Bartkowski, Przemysław
Wydział Inżynierii Metali i Informatyki Przemysłowej
Access status: Restricted ,
Selected methods of optimization predictors in well logging measurements
(Data obrony: 2019-12-10) Pyśniak, Kacper
Wydział Geologii, Geofizyki i Ochrony Środowiska
Access status: Restricted ,
Wykorzystanie metody wektorów nośnych w analizie danych geofizycznych.
(Data obrony: 2020-02-04) Knap, Natalia
Wydział Geologii, Geofizyki i Ochrony Środowiska