Quantitative EEG features such as frontal alpha asymmetry, spectral ratios and signal-complexity measures are often presented as interpretable biomarkers of emotion. Such claims require the markers to generalize across individuals, yet common evaluation protocols allow overlapping epochs and recordings from the same pa...
Ema Pandilova, Aleksandar Stojmenski, Ivan Chorbev et al.· Italian National Conference...· 0 citations
Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Although deep learning methods have achieved strong results for crop and weed detection, many studies rely on single-dataset evaluation, making it difficult to assess robustn...
Hristina Zdraveska, Vlatko Spasev, Ivica Dimitrovski et al.· 0 citations
A dataset-centric benchmark of deep learning methods for grape leaf disease classification and detection that emphasizes dataset provenance, realistic field evaluation, annotation compatibility, and external validation for reliable vineyard disease recognition is presented.
Petar Canoski, Vlatko Spasev, I. Dimitrovski et al.· 0 citations
Accurate early warning of vineyard disease risk can support timely intervention and more targeted crop-protection decisions. However, conventional daily-status classification does not directly measure whether a model can anticipate the beginning of a new risk episode. This study investigates an event-based formulation...
I. Dimitrovski, Ivan Kitanovski, D. Davcev et al.· International Journal of Dat...· 0 citations
The Multi-Agent Readiness Score (MARS), a standardized evaluation framework assessing models across four structural dimensions, provides a crucial, reproducible metric to audit model maturity, guide system architecture, and ensure future models are structurally prepared for the rigorous regulatory demands of precision...
Blagojche Gjorgjioski, D. Bukovec, Ivana Vichentijevikj et al.· Future Internet· 0 citations
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