20.500.12556/RUL-182226 Complexity and performance analysis of supervised machine learning models for applied technologies an experimental study with impulsive [alpha]-stable noise Impulsive alpha (α)-stable noise, characterized by heavy tails and intense outliers, is a key ingredient in simulating financial, medical, seismic, and digital communication technologies. It poses versatile challenges to conventional machine learning (ML) algorithms in predicting noise parameters for multidisciplinary artificial intelligence (AI)-embedded devices. In this study, we adopted a two-phase methodology to investigate the complexity and performance of supervised ML algorithms while classifying impulsive noise parameters. We generated synthetic datasets of α-stable noise distributions for experimentation in a controlled environment. It was followed by experimental evaluation to derive the complexity and performance of ML classifiers—k-nearest neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF). Moreover, we employed a very high channel noise level of −15 dB in the test datasets to ensure that the derived analysis applies to real-world devices. The results demonstrate the high performance of DT and RF in structured binary classification of the α regime and the sign of skewness, while incurring satisfactory computational costs. However, SVM and kNN are comparatively more robust for multi-class classification, albeit with higher memory and training costs. On the contrary, NB fails to address the skewed and impulsive behavior of α-stable noise. We observed that even the most effective classifiers struggle to achieve perfect accuracy in multi-class classification. Overall, the experimental results reveal significant trade-off relationships between the complexity and performance of ML classifiers. Conclusively, simple models are well-suited for coarse-grained tasks, such as α-approximation and sign-of-skewness classification. In contrast, sophisticated models can be deployed to predict noise parameters to some extent. Our study provides a clear set of trade-offs for future applied AI devices that address adversarial and impulsive noise. supervised machine learning complexity performance SVM kNN NB DT random forest α stable distributions impulsive noise nadzorovano strojno učenje kompleksnost zmogljivost podporni vektorski stroji k-najbližjih sosedov naivni Bayesov klasifikator odločitveno drevo naključni gozd α-stabilne porazdelitve true false true Angleški jezik Slovenski jezik Članek v reviji 2026-05-04 13:02:31 2026-05-04 13:02:34 2026-05-05 04:44:17 0000-00-00 00:00:00 2026 0 0 24 str. iss. 5, art. 252 Vol. 14 May 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.85 2227-7080 10.3390/technologies14050252 276850179 RAZ_Ahmed_Areeb_2026.pdf RAZ_Ahmed_Areeb_2026.pdf 1 7A61A569A13E95A856EACB6966788DAB f0585bca6a275d0f7d91fd53c5e35a0cc364477d413970a083f2b9a0e80dbea6 dec8c332-47b0-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233294 https://www.mdpi.com/2227-7080/14/5/252 1 79b35760-47a8-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233287 Fakulteta za računalništvo in informatiko 0 0 0