Faculty of Electrical Engineering and Information Technologies
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Item type:Publication, A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification(Institute of Electrical and Electronics Engineers (IEEE), 2025-03-07) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, On the joint spectra of operators and antiunitaries(National Library of Serbia, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bias in vital signs? Machine learning models can learn patients’ race or ethnicity from the values of vital signs alone(BMJ, 2025-07-10); ; ; ; Mullan, Irene DankwaObjectives To investigate whether machine learning (ML) algorithms can learn racial or ethnic information from the vital signs alone. Methods A retrospective cohort study of critically ill patients between 2014 and 2015 from the multicentre eICU-CRD critical care database involving 335 intensive care units in 208 US hospitals, containing 200 859 admissions. We extracted 10 763 critical care admissions of patients aged 18 and over, alive during the first 24 hours after admission, with recorded race or ethnicity as well as at least two measurements of heart rate, oxygen saturation, respiratory rate and blood pressure. Pairs of subgroups were matched based on age, gender, admission diagnosis and disease severity. XGBoost, Random Forest and Logistic Regression algorithms were used to predict recorded race or ethnicity based on the values of vital signs. Results Models derived from only four vital signs can predict patients’ recorded race or ethnicity with an area under the curve (AUC) of 0.74 (±0.030) between White and Black patients, AUC of 0.74 (±0.030) between Hispanic and Black patients and AUC of 0.67 (±0.072) between Hispanic and White patients, even when controlling for known factors. There were very small, but statistically significant differences between heart rate, oxygen saturation and blood pressure, but not respiration rate and invasively measured oxygen saturation. Discussion ML algorithms can extract racial or ethnicity information from vital signs alone across diverse patient populations, even when controlling for known biases such as pulse oximetry variations and comorbidities. The model correctly classified the race or ethnicity in two out of three patients, indicating that this outcome is not random. Conclusion Vital signs embed racial information that can be learnt by ML algorithms, posing a significant risk to equitable clinical decision-making. Mitigating measures might be challenging, considering the fundamental role of vital signs in clinical decision-making. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Spectral mapping theorem and the Taylor spectrum(National Library of Serbia, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart forecasting: Enhancing virtual power plant performance with analytical frameworks(Elsevier BV, 2025-04-24); ; ; ; - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Indoor–Outdoor Particulate Matter Monitoring in a University Building: A Pilot Study Using Low-Cost Sensors(MDPI AG, 2026-01-30); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed generation placement in distributive substations analysis using Markov Chain Monte Carlo model considering the reliability of power supply(IOP Publishing, 2020-06-01); <jats:title>Abstract</jats:title> <jats:p>Proper operation of the power substations is of great importance for power network reliability, stability and uninterrupted power supply. Distributed generation provides higher reliability in power supply, but still, there are contingencies in the electric power production and supply process, which lead to outages in the power supply. In this paper, a method for substations’ reliability estimation with distributed generation is presented based on Markov Chain Monte Carlo method. The method considers the possible substation operation states and using random number generator in MATLAB, it simulates faults and calculates the substations’ reliability. The method is demonstrated on two cases of 110/35 kV substations, each consisting of two transformers and distributed generator, analysing the best placement for the distributed generation.</jats:p> - Some of the metrics are blocked by yourconsent settings
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