1. Analiza sentimenta bosanskega jezika : doktorska disertacijaSead Jahić, 2026, doktorska disertacija Ključne besede: Bosnian, sentiment analysis, negation, intensifiers, machine learning, classifier, neural networks, BoSA, BOSentiment
Objavljeno v RUP: 12.03.2026; Ogledov: 76; Prenosov: 3
 Celotno besedilo (3,04 MB)
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2. Designing the ideal political identity questionnaire using machine learning and ideology scalesAna Nikolić, Uroš Sergaš, Marko Tkalčič, 2025, objavljeni znanstveni prispevek na konferenci (vabljeno predavanje) Opis: 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.
Ključne besede: political ideology, questionnaire design, machine learning, psychometrics
Objavljeno v RUP: 30.01.2026; Ogledov: 249; Prenosov: 2
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9. Occupancy estimation using indoor air quality data : opportunities and privacy implicationsDomen Vake, Niki Hrovatin, Jernej Vičič, Aleksandar Tošić, 2025, izvirni znanstveni članek Opis: 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.
Ključne besede: indoor air quality, occupancy estimation, machine learning, sensor networks, privacy, building monitoring
Objavljeno v RUP: 02.06.2025; Ogledov: 1223; Prenosov: 8
Celotno besedilo (3,66 MB)
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