1. Machine learning identifies distinct movement control impairment clusters in patients with chronic neck painŽiva Majcen Rošker, Jernej Rošker, 2026, original scientific article Abstract: atients with chronic neck pain experience various impairments, with reduced movement control suggested as a significant contributing factor. The heterogeneity of this patient population and suboptimal rehabilitation outcomes suggests the existence of latent subgroup characteristics. The aim of this study was to identify distinct groups among patients with neck pain based on the movement control test and pain intensity and to provide information on cluster-specific impairments. 135 patients with idiopathic neck pain performed a movement control test (the Butterfly test) at three difficulty levels and were assessed for pain intensity, providing 13 different parameters (classifiers). Louvain, hierarchical and k-means clustering methods were applied and the number of clusters determined by observing the symmetry and size of silhouette scores. Further, different machine learning algorithms were applied to develop and evaluate a classification framework (based on AUC, classification accuracy, sensitivity, and specificity) and to provide information on individual cluster characteristics using the Shapley Additive Explanations. The k-means and deep learning neural network methods provided the most efficient clustering and classification performance extracting 4 meaningful clusters. Patients between groups differed in the amount of impairment, with cluster 2 and 1 representing the most severe impairments and with clusters 3 and 4 the least severe impairments. Additionally, specific motor control impairments were observed in individual clusters suggesting distinct neck movement control adaptations. Identifying subgroups of patients with neck pain and their specific characteristics based on the results of the Butterfly test may inform future development of targeted rehabilitation strategies.
Keywords: neck pain, kinesthesia, proprioception clustering, machine learning
Published in RUP: 16.03.2026; Views: 455; Downloads: 6
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2. Analiza sentimenta bosanskega jezika : doktorska disertacijaSead Jahić, 2026, doctoral dissertation Keywords: Bosnian, sentiment analysis, negation, intensifiers, machine learning, classifier, neural networks, BoSA, BOSentiment
Published in RUP: 12.03.2026; Views: 374; Downloads: 21
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3. Designing the ideal political identity questionnaire using machine learning and ideology scalesAna Nikolić, Uroš Sergaš, Marko Tkalčič, 2025, published scientific conference contribution (invited lecture) Abstract: Political ideology shapes beliefs, behavior and attitudes toward society. Many existing questionnaires for measuring ideology are lengthy, repetitive, and misaligned with self-perception. This paper investigates whether a shorter, reliable, two-dimensional political identity questionnaire can be created using machine learning and psychometric methods. Sixty participants completed four ideological instruments (MFQ, SDO, RWA and 8values). Lasso regression and Random Forest with nested cross-validation identified predictive items, while psychometric evaluation included CFA and Cronbach’s alpha. Random Forest outperformed Lasso. Internal reliability was excellent and factor loadings supported a two-factor structure despite moderate model it. Findings show that ideology can be measured efficiently with reduced items, supporting applications in research, digital platforms and political psychology.
Keywords: political ideology, questionnaire design, machine learning, psychometrics
Published in RUP: 30.01.2026; Views: 501; Downloads: 3
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10. Occupancy estimation using indoor air quality data : opportunities and privacy implicationsDomen Vake, Niki Hrovatin, Jernej Vičič, Aleksandar Tošić, 2025, original scientific article Abstract: Indoor Air Quality (IAQ) has long been a significant concern due to its health-related risks and potential benefits. Readily available air quality sensors are now affordable and have been installed in many buildings with public buildings taking center stage. The dynamics of IAQ are commonly studied in relation to different materials used in construction, building design, room utility and effects on occupants. However, besides what the sensors were designed to measure, it is possible to infer other information. In this paper, we present a Machine Learning (ML) model that predicts the presence of people in the room with an accuracy as high as 93 % and the exact number of occupants with 2.17 MAE. We validate our proposed approach in the use-case of an elementary school in Slovenia. In collaboration with the elementary school in Ajdovščina, 8 air quality sensors were placed in classrooms and air quality parameters (VOC, CO, Temperature, and Humidity) were monitored for 6 months. During the monitoring period, school staff collected anonymous data about classroom occupancy. The indoor air quality data was paired with external weather data as well as occupancy to train the model. Moreover, we compare our approach with other commonly used ML approaches and provide results related to our use case. Finally, these results highlight the privacy concerns related to structural monitoring due to the established ability to infer potentially sensitive information.
Keywords: indoor air quality, occupancy estimation, machine learning, sensor networks, privacy, building monitoring
Published in RUP: 02.06.2025; Views: 1592; Downloads: 10
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