This paper proposes a hybrid multi-criteria decision-making (MCDM) framework for selecting the optimal AI algorithms in the context of real-time infrared signal detection systems. Five performance criteria were considered, namely the processing speed, detection accuracy, segmentation efficiency, noise robustness and energy efficiency, reflecting the requirements of real-time image processing and embedded computer vision systems. This framework integrates the SWARA method for expert-based criteria weighting with Net Worth Analysis (NWA) for algorithm ranking, enabling a transparent and systematic evaluation. The experimental results show that the Fast R-CNN algorithm achieves the highest overall performance, while algorithms such as EfficientDet obtain lower scores and require further refinement to be effectively used in real-time infrared signal detection applications. To sum up, the proposed method addresses the current lack of structured decision-support tools for selecting among various AI-based infrared signal detection models under operational constraints. The research findings provide actionable guidance for researchers and practitioners developing embedded AI, surveillance and automated monitoring systems.
Multi-criteria decision making (MCDM), SWARA method, Net Worth Analysis (NWA), Artificial intelligence, Image processing algorithms, Computer vision, Algorithm evaluation.
Nikola GLIGORIJEVIĆ, Dejan VIDUKA, Stefan POPOVIĆ, Danilo STRUGAREVIĆ, Vladimir ČABRIĆ, "A Hybrid MCDM Framework for Selecting Optimal AI Algorithms in Real-Time Infrared Signal Detection Systems", Studies in Informatics and Control, ISSN 1220-1766, vol. 35(1), pp. 45-55, 2026. https://doi.org/10.24846/v35i1y202604
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