1. Automated speech analysis in depressive disorder: enhancing diagnosis and monitoringAljaž Neuberg, 2025, master's thesis Abstract: This paper investigates the automatic recognition of depression by integrating acoustic, linguistic and emotional features extracted from clinical interviews in the DAIC-WOZ dataset. A total of six classical machine learning classifiers such as Decision Tree, Random Forest, SVM, Gradient Boosting, AdaBoost and XGBoost were systematically evaluated under different class balancing methods (such as SMOTE, SMOTETomek and Random Undersampling) and feature selection strategies. The best model, a decision tree classifier with SMOTE-based balancing and a feature selection technique, achieved a weighted F1 score and accuracy of 0.78 with only eight selected features. These features included all three modalities, demonstrating the
added benefit of a multimodal approach. The results suggest that even relatively simple models, when supported by careful preprocessing and dimensionality reduction, can provide accurate and interpretable predictions. This work emphasizes the importance of feature engineering and balancing techniques in clinical machine learning tasks and lays the foundation for future research on scalable and explainable depression detection systems.
Keywords: Depression, Classification, Machine Learning, Data Balancing, Feature Selection
Published in DKUM: 03.11.2025; Views: 0; Downloads: 0
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2. Optimal ensemble-based framework for ground-fault protection in radial MV distribution networks with resonant grounding☆Boštjan Polajžer, Younes Mohammadi, Thomas Olofsson, Gorazd Štumberger, 2025, original scientific article Abstract: Ground fault relays (GFRs) in resonant-grounded medium voltage distribution networks shall not operate during phase-to-ground (Ph-G) fault inception, allowing the Petersen coil to suppress self-extinguishing faults, but the designated GFR must operate during permanent faults. In order to enhance the performance of GFRs, particularly during high-impedance faults, the scope of this paper is to propose a straightforward, machine-learning-based protection framework. The enhanced GFR is modeled as a classification task. Depending on the GFR’s position and the Ph-G fault location in the network, fault samples are labeled as “no operation,” “primary,” “backup,” or “backup of backup,” forming two-class, three-class, and four-class GFR setups, respectively. This assures selective operation across three protection zones and improves the reliability of all GFRs. The proposed protection scheme employs backward optimal feature selection to identify the most relevant discrete features obtained from measured zero-sequence current and voltage waveforms. An ensemble of k-nearest neighbor classifiers is utilized for accurate classification, simulating the GFR operating conditions, with measurement errors and sensitivity incorporated in the preprocessing. A 20 kV case study network validates the proposed framework, achieving F1-scores exceeding 96 %. The maximum operation delay of the protection scheme for an enhanced GFR is 225 ms, accommodating the required time window (200 ms), prediction time (5 ms), and change detection time (20 ms), thus assuring safe operation. Compared to other machine-learning-based methods used for Ph-G fault protection in resonant-grounded radial networks, this framework is high-performing, fast, and easy to implement, utilizing a simpler structure than neural networks.
Keywords: resonant grounded networks, ground-fault relay, high-impedance faults, ensemble-based learning, optimal feature selection
Published in DKUM: 25.07.2025; Views: 0; Downloads: 3
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3. EEG-based finger movement classification with intrinsic time-scale decompositionMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2024, original scientific article Keywords: brain-computer interfaces, electroencephalogram, feature reduction, machine learning, finger movements classification, time series analysis
Published in DKUM: 16.04.2025; Views: 0; Downloads: 3
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4. EEG channel and feature investigation in binary and multiple motor imagery task predictionsMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2024, original scientific article Keywords: brain-computer interfaces, electroencephalogram, feature and channel investigation, feature selection, machine learning, motor imagery task classification
Published in DKUM: 19.12.2024; Views: 0; Downloads: 5
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5. Statistically significant features improve binary and multiple motor imagery task predictions from EEGsMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2023, original scientific article Abstract: In recent studies, in the field of Brain-Computer Interface (BCI), researchers have
focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram
(EEG) signals provide the interaction and communication between the paralyzed
patients and the outside world for moving and controlling external devices
such as wheelchair and moving cursors. However, current approaches in the
Motor Imagery-BCI system design require.
Keywords: brain-computer interfaces, electroencephalogram, feature selection, machine learning, task classification
Published in DKUM: 10.09.2024; Views: 31; Downloads: 8
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