1. NiaAML : AutoML for classification and regression pipelinesIztok Fister, Laurenz A. Farthofer, Luka Pečnik, Iztok Fister, Andreas Holzinger, 2025, original scientific article Abstract: In this paper we present NiaAML, an AutoML framework that we have developed for creating machine learning pipelines and hyperparameter tuning. The composition of machine learning pipelines is presented as an optimization problem that can be solved using various stochastic, population-based, nature-inspired algorithms. Nature-inspired algorithms are powerful tools for solving real-world optimization problems, especially those that are highly complex, nonlinear, and involve large search spaces where traditional algorithms may struggle. They are applied widely in various fields, including robotics, operations research, and bioinformatics. This paper provides a comprehensive overview of the software architecture, and describes the main tasks of NiaAML, including the automatic composition of classification and regression pipelines. The overview is supported by an practical illustrative example.
Keywords: AutoML, classification, nature-inspired algorithms, optimization
Published in DKUM: 19.01.2026; Views: 0; Downloads: 1
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3. Survey and challenges of dental metallic materialsKarlo Raić, Rebeka Rudolf, Vojkan Lazić, Peter Majerič, 2024, review article Abstract: The classification of dental metallic biomaterials is illustrated in depth, with a focus on dental casting alloys. Methods for creating dental prostheses as well as manufacturing shape memory alloys (SMA) Ni-Ti alloys are briefly given. The effect of surface oxide films on metallic biomaterials in the human environment is considered. In order for metal implants to interact with the human body, they need to meet certain requirements.
Keywords: dental metallic alloys, classification, creating dental prostheses, manufacturing SMA Ni-Ti Alloys, surface oxide films, requirements
Published in DKUM: 13.01.2026; Views: 0; Downloads: 0
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5. Automated speech analysis in depressive disorder: enhancing diagnosis and monitoring : magistrsko deloAljaž 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: 10
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6. Uporaba metod strojnega učenja za klasifikacijo nalog po prioritetah v IT projektihTatyana Unuchak, Mirjana Kljajić Borštnar, Yauhen Unuchak, 2025, original scientific article Abstract: Določanje prioritet in razvrščanje nalog še vedno predstavlja izziv pri učinkovitem vodenju projektov. Obstaja veliko klasičnih pristopov
za določanje prioritet. Vendar so te tehnike delovno intenzivne, subjektivne in neprilagodljive. V prispevku obravnavamo pristope za
samodejno določanje prioritet nalog v IT projektih, ki temeljijo na strojnem učenju. Raziskujemo, kako lahko z uporabo metod strojnega učenja pomagamo projektnim vodjem pri učinkovitejšem razvrščanju nalog v IT projektih. V ta namen smo na množici več kot
1000000 zapisov projektnih nalog razvili klasifikacijski model za samodejno določanje prioritet. Problem, ki smo ga obravnavali, je
večrazredni, pri tem je večina primerov, označenih z najvišjo prioriteto, kar predstavlja izziv pri modeliranju kot tudi pri učinkovitosti
upravljanja IT projektov. Preskusili smo različne algoritme ter različne pristope, s ciljem izboljšanja rezultatov klasifikacije. Pokazali
smo, da je naloge smiselno razvrstiti v manjše skupine prioritet, kar prispeva k večji natančnosti klasifikacijskega modela in preglednosti prioritet nalog, slednje pa lahko olajša upravljanje IT projektov.
Keywords: IT project management, machine learning, task prioritization, multiclass classification, data imbalance
Published in DKUM: 28.08.2025; Views: 0; Downloads: 8
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7. Classification of finger movements through optimal EEG channel and feature selectionMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2025, original scientific article Keywords: classification, finger movements, EEG, feature selection, applied physics
Published in DKUM: 22.07.2025; Views: 0; Downloads: 8
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8. Evaluating Proprietary and Open-Weight Large Language Models as Universal Decimal Classification Recommender SystemsMladen Borovič, Eftimije Tomovski, Tom Li Dobnik, Sandi Majninger, 2025, original scientific article Abstract: Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claude models (3-Haiku and 3.5-Haiku), Gemini series (1.0-Pro, 1.5-Flash, and 2.0-Flash), and Llama, Gemma, Mixtral, and DeepSeek architectures. Models were evaluated zero-shot on 900 English and Slovenian academic theses manually classified by professional librarians. Classification prompts utilized the RISEN framework, with evaluation using Levenshtein and Jaro–Winkler similarity, and a novel adjusted hierarchical similarity metric capturing UDC’s faceted structure. Proprietary systems consistently outperformed open-weight alternatives by 5–10% across metrics. GPT-4o achieved the highest hierarchical alignment, while open-weight models showed progressive improvements but remained behind commercial systems. Performance was comparable between languages, demonstrating robust multilingual capabilities. The results indicate that LLM-powered recommender systems can enhance library classification workflows. Future research incorporating fine-tuning and retrieval-augmented approaches may enable fully automated, high-precision UDC assignment systems.
Keywords: universal decimal classification, large language models, conversational systems, recommender systems, prompt engineering, zero-shot classification, hierarchical similarity
Published in DKUM: 21.07.2025; Views: 0; Downloads: 13
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9. AI model for industry classification based on website dataTimotej Jagrič, Aljaž Herman, 2024, original scientific article Abstract: This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.
Keywords: industry classification, BERT transformer, business descriptions, multiclass text classification, AI
Published in DKUM: 01.07.2025; Views: 0; Downloads: 12
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10. Fostering fairness in image classification through awareness of sensitive dataIvona Colakovic, Sašo Karakatič, 2025, original scientific article Abstract: Machine learning (ML) has demonstrated remarkable ability to uncover hidden patterns in data. However, the presence of biases and discrimination originating from the data itself and, consequently, emerging in the ML outcomes, remains a pressing concern. With the exponential growth of unstructured data, such as images, fairness has become increasingly critical, as neural network (NN) models may inadvertently learn and perpetuate societal and historical biases. To address this challenge, we propose a fairness-aware loss function that iteratively prioritizes the worst-performing sensitive group during NN training. This approach aims to balance treatment quality across sensitive groups, achieving fairer image classification outcomes while incurring only a slight compromise in overall performance. Our method, evaluated on the FairFace dataset, demonstrates significant improvements in fairness metrics while maintaining comparable overall quality. These trade-offs highlight that the minor decrease in overall quality is justified by the improvement in fairness of the models.
Keywords: fairness, search-basimage classification, machine learning, supervised learnign, neural networks
Published in DKUM: 23.04.2025; Views: 0; Downloads: 7
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