Predrag Matavulj
Predrag Matavulj
Postdoctoral Researcher @I4DS
Verified email at
Cited by
Cited by
Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps
I Šaulienė, L Šukienė, G Daunys, G Valiulis, L Vaitkevičius, P Matavulj, ...
Atmospheric Measurement Techniques 12 (6), 3435-3452, 2019
RealForAll: real-time system for automatic detection of airborne pollen
D Tešendić, D Boberić Krstićev, P Matavulj, S Brdar, M Panić, V Minić, ...
Enterprise Information Systems 16 (5), 1793391, 2022
Towards European automatic bioaerosol monitoring: comparison of 9 automatic pollen observational instruments with classic Hirst-type traps
JM Maya-Manzano, F Tummon, R Abt, N Allan, L Bunderson, B Clot, ...
Science of the Total Environment 866, 161220, 2023
Why should we care about high temporal resolution monitoring of bioaerosols in ambient air?
M Smith, P Matavulj, G Mimić, M Panić, Ł Grewling, B Šikoparija
Science of the Total Environment 826, 154231, 2022
Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations
P Matavulj, A Cristofori, F Cristofolini, E Gottardini, S Brdar, B Sikoparija
Science of the Total Environment 851, 158234, 2022
Real-time automatic detection of starch particles in ambient air
B Šikoparija, P Matavulj, G Mimić, M Smith, Ł Grewling, Z Podraščanin
Agricultural and Forest Meteorology 323, 109034, 2022
Do we need continuous sampling to capture variability of hourly pollen concentrations?
B Sikoparija, G Mimić, P Matavulj, M Panić, I Simović, S Brdar
Aerobiologia 36, 3-7, 2020
Advanced CNN architectures for pollen classification: Design and comprehensive evaluation
P Matavulj, M Panić, B Šikoparija, D Tešendić, M Radovanović, S Brdar
Applied Artificial Intelligence 37 (1), 2157593, 2023
Domain adaptation with unlabeled data for model transferability between airborne particle identifiers
P Matavulj, S Brdar, M Racković, B Šikoparija, IN Athanasiadis
17th International Conference on Machine Learning and Data Mining MLDM 2021 …, 2021
Manual and automatic quantification of airborne fungal spores during wheat harvest period
I Simović, P Matavulj, B Šikoparija
Aerobiologia 39 (2), 227-239, 2023
Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy
S Brdar, M Panić, P Matavulj, M Stanković, D Bartolić, B Šikoparija
Scientific Reports 13 (1), 3205, 2023
Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices
M Gonzalez-Alonso, J Oteros, M Widmann, JM Maya-Manzano, C Skjøth, ...
Science of the Total Environment 931, 172913, 2024
Interseasonal transfer learning for crop mapping using Sentinel-1 data
M Pandžić, D Pavlović, P Matavulj, S Brdar, O Marko, V Crnojević, ...
International Journal of Applied Earth Observation and Geoinformation 128 …, 2024
Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer
B Sikoparija, P Matavulj, I Simovic, P Radisic, S Brdar, V Minic, ...
EGUsphere 2024, 1-36, 2024
Integration of data from different rapid e-devices supports pollen classification in more locations
P Matavulj, A Cristofori, F Cristofolini, E Gottardini, S Brdar, B Sikoparija
One health Paestum 2022: 5th MedPalyos Symposium. 16th AIA Congress (Italian …, 2022
High temporal resolution monitoring of Ambrosia pollen in ambient air
L Grewling, P Matavulj, G Mimić, M Panić, M Smith, B Šikoparija
Detection of starch rain in ambient air of Novi Sad, Serbia
B Šikoparija, P Matavulj, G Mimić, M Smith, L Grewling, Z Podraščanin
Multi-modal architecture based on machine learning for real-time pollen classification
D Tešendić, S Brdar, P Matavulj
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