2025

• Michelucci, U., & Venturini, F. (2025). New statistical framework for extreme error probability in high-stakes domains for reliable machine learning. arXiv. https://doi.org/10.48550/arXiv.2503.24262

• Smeesters, L., Venturini, F., Paulus, S., Mahlein, A. K., Perpetuini, D., Cardone, D., ... Michelucci, U., …, & Mignani, A. G. (2025). 2025 photonics for agrifood roadmap: towards a sustainable and healthier planet. Journal of Physics: Photonics.

2024

• Michelucci, U., & Venturini, F. (2024). Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets and Application to the Oxidation of Olive Oil, Nature Scientific Reports, Springer Nature

• Ghazouali, S. E., Gucciardi, A., & Michelucci, U. (2024). Class-Conditional self-reward mechanism for improved Text-to-Image models. arXiv preprint arXiv:2405.13473.

• Michelucci, U., Fundamental Mathematical Concepts for Machine Learning in Science (2024), Springer Nature

• El Ghazouali, S., Mhirit, Y., Oukhrid, A., Michelucci, U., & Nouira, H. (2024). FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything. Sensors, 24(9), 2889.

• Venturini, F., Fluri, S., Mejari, M., Baumgartner, M., Piga, D., & Michelucci, U. (2024, April). Machine learning-enhanced fluorescence spectroscopy for the quality assessment of extra virgin olive oil during ageing. In SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024.

• S.E. Ghazouali, A. Gucciardi, N. Venturi, M. Rueegsegger, U. Michelucci, FlightScope: A Deep Comprehensive Assessment of Aircraft Detection Algorithms in Satellite Imagery, arXiv preprint arXiv:2404.02877.

• S.E. Ghazouali, Y. Mhirit, A. Oukhrid, U. Michelucci, H. Nouira, FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything, arXiv preprint arXiv:2403.00175 (accepted for publication in Sensors).

• Fabio, L., Piga, D., Umberto, M., & Safouane, E. G. (2024). BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery. arXiv preprint arXiv:2402.13918.

• Venturini, F., Fluri, S., Mejari, M., Baumgartner, M., Piga, D., & Michelucci, U. (2024). Shedding light on the ageing of extra virgin olive oil: Probing the impact of temperature with fluorescence spectroscopy and machine learning techniques. LWT, 191, 115679.

• Arnaud Gucciardi, Safouane Ghazouali, Francesca Venturini, Vida Groznik, Umberto Michelucci, Symbrain: a large-scale dataset of MRI images for neonatal brain symmetry analysis, arXiv preprint: arXiv:2401.11814.

2023

• Michela Sperti, Umberto Michelucci, Arnaud Gucciardi, Konstantinos Panagiotopoulos, Guglielmo Gallone, Mario Iannaccone, Francesco Bruno, Ovidio De Filippo, Fabrizio D’Ascenzo, Gaetano M. De Ferrari, Umberto Morbiducci, Marco A. Deriu (2023), Personalized Time-to-Event Prediction in Acute Coronary Syndrome, Proceedings of the 18th Conference on Computational Intelligence Methods for Bioinformatics & Biostatistics (CIBB 2023)

• Schmid, C., Laurenzi, E., Michelucci, U., Venturini, F. (2023). Explainable AI for the Olive Oil Industry. In: Hinkelmann, K., López-Pellicer, F.J., Polini, A. (eds) Perspectives in Business Informatics Research. BIR 2023. Lecture Notes in Business Information Processing, vol 493. Springer, Cham. https://doi.org/10.1007/978-3-031-43126-5_12

• Michelucci, U., & Venturini, F. (2023). New metric formulas that include measurement errors in machine learning for natural sciences. Expert Systems with Applications, 224, 120013.

• Michelucci, U., Fluri, S., Baumgartner, M., & Venturini, F. (2023, March). Deep learning super resolution for high-speed excitation emission matrix measurements. In AI and Optical Data Sciences IV (Vol. 12438, pp. 127-137). SPIE.

• Venturini, F., Michelucci, U., Sperti, M., Gucciardi, A., & Deriu, M. A. (2023, March). Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra. In AI and Optical Data Sciences IV (Vol. 12438, pp. 178-184). SPIE.

• Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2023). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. Journal of Food Engineering, 336, 111198.

• Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2023). Dataset of fluorescence spectra and chemical parameters of olive oils. arXiv preprint arXiv:2301.04471.
  

2022

• Kenzo Milleville, Chandrasekar Krishna Kumar Thirukokaranam, Thibault Blyau, Aurora Iannello, Umberto Michelucci, Steven Verstockt, (2022), Extraction and Classification of HIstorical Stamp Cards using Computer Vision, DH Benelux 2022: RE-MIX

• Venturinia, F., Sperti, M., Michelucci, U., Gucciardi, A., Martose, V. M., & Deriu, M. A. (2022). Physico-chemical properties extraction from the fluorescence spectrum with 1D-convolutional neural networks: application to olive oil. arXiv preprint arXiv:2203.07229.

• F. Venturini, U. Michelucci, M. Sperti, A. Gucciardi, V. M. Martos Nuneze, and M. A. Deriu, “One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge: explainability techniques applied to olive oil fluorescence spectra,” in Optical Sensing and Detection VII 12139, 326-333.

• A. Gucciardi, U. Michelucci, F. Venturini, M. Sperti, V. M. Martos Nuneze, and M. A. Deriu, “Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil,” Optical Sensing and Detection VII 12139.

• M. Sperti, U. Michelucci, F. Venturini, A. Gucciardi, and M. A. Deriu, “Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks,” in Optical Sensing and Detection VII 12139.

• Sperti, M., Gucciardi, A., Michelucci, U., Venturini, F., & Deriu, M. A. (2022, March). Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks. In Optical Sensing and Detection VII (Vol. 12139). SPIE.

• Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2023). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. Journal of Food Engineering, 336, 111198.

• U. Michelucci, “An Introduction to Autoencoders”, arXiv:2201.03898

2021

• U. Michelucci, “Writing and reading scientific papers”, TOELT LLC, 2021 (PDF DOWNLOAD)

• U. Michelucci, M. Sperti, D. Piga, F. Venturini, and M. A. Deriu, “A model-agnostic algorithm for bayes error determination in binary classification,” Algorithms, vol. 14, iss. 11, p. 301, 2021. (COVER STORY OF THE JOURNAL ALGORITHMS)

• F. Venturini, U. Michelucci, and M. Baumgartner. "New approach for temperature-immune oxygen sensing based on Pt-TFPP." Optical Sensors. Optical Society of America, 2021.

• F. Venturini, U. Michelucci, and M. Baumgartner, “Implementation of multi-task learning neural network architectures for robust industrial optical sensing,” in Optical measurement systems for industrial inspection xii, 2021, p. 117822H.

• F. Venturini, M. Sperti, U. Michelucci, I. Herzig, M. Baumgartner, J. P. Caballero, A. Jimenez, and M. A. Deriu, “Exploration of spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques,” Foods, vol. 10, iss. 5, 2021. doi:10.3390/foods10051010

• G. Halasz, M. Sperti, M. Villani, U. Michelucci, P. Agostoni, A. Biagi, L. Rossi, A. Botti, C. Mari, M. Maccarini, and others, “A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score,” Journal of medical internet research, vol. 23, iss. 5, p. e29058, 2021.

• U. Michelucci and F. Venturini, “Estimating neural network’s performance with bootstrap: a tutorial,” Machine learning and knowledge extraction, vol. 3, iss. 2, p. 357–373, 2021. doi:10.3390/make3020018

• F.D’Ascenzo, D. O. Filippo, G. Gallone, G. Mittone, M. A. Deriu, M. Iannaccone, A. Ariza-Solé, C. Liebetrau, S. Manzano-Fernández, G. Quadri, T. Kinnaird, G. Campo, J. S. Henriques, J. Hughes, A. Dominguez-Rodriguez, M. Aldinucci, U. Morbiducci, G. Patti, S. Raposeiras-Roubin, E. Abu-Assi, D. G. M. Ferrari, F. Piroli, A. Saglietto, F. Conrotto, P. Omedé, A. Montefusco, M. Pennone, F. Bruno, P. P. Bocchino, G. Boccuzzi, E. Cerrato, F., O. Varbella, M. Sperti, S. B. Wilton, L. Velicki, I. Xanthopoulou, A. Cequier, A. Iniguez-Romo, M. I. Pousa, C. M. Fern, C. B. Queija, R. Cobas-Paz, A. Lopez-Cuenca, A. Garay, F. P. Blanco, A. Rognoni, B. G. Zoccai, S. Biscaglia, I. Nunez-Gil, T. Fujii, A., ro Durante, X. Song, T. Kawaji, D. Alexopoulos, Z. Huczek, J. R. G. Juanatey, S. -P. Nie, M. Kawashiri, I. Colonnelli, B. Cantalupo, R. Esposito, S. Leonardi, W. G. Marra, A. Chieffo, U. Michelucci, D. Piga, M. Malavolta, S. Gili, M. Mennuni, C. Montalto, L. O. Visconti, and Y. Arfat, “Machine learning-based prediction of adverse events following an acute coronary syndrome (praise): a modelling study of pooled datasets,” The lancet, vol. 397, iss. 10270, p. 199–207, 2021.

2020

• U. Michelucci and F. Venturini, “New autonomous intelligent sensor design approach for multiple parameter inference,” Engineering proceedings, vol. 2, iss. 1, 2020. doi:10.3390/engproc2020002096

• F. Venturini, U. Michelucci, and M. Baumgartner, “Multi-task learning approach for optical luminescence sensing,” in Applied machine learning days (amld), lausanne, 25-29 january 2020, 2020.

• F. Venturini, U. Michelucci, and M. Baumgartner, “Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks,” in Optical sensing and detection vi, 2020, p. 113541C.

• F. Venturini, U. Michelucci, and M. Baumgartner, “Dual oxygen and temperature luminescence learning sensor with parallel inference,” Sensors, vol. 20, iss. 17, p. 4886, 2020.

• F. Venturini, U. Michelucci, and M. Baumgartner, “Deep-learning for multi-parameter luminescence sensing: demonstration of dual sensor,” in Frontiers in optics, 2020, p. FTu2B–5.

2019

• U. Michelucci, M. Baumgartner, and F. Venturini, “Optical oxygen sensing with artificial intelligence,” Sensors, vol. 19, iss. 4, p. 777, 2019.

• F. Venturini, M. Baumgartner, and U. Michelucci, “New approach for luminescence sensing based on machine learning,” in Optical data science ii, 2019, p. 109370H.

• U. Michelucci, Advanced applied deep learning: convolutional neural networks and object detection, Apress, 2019.

• U. Michelucci and F. Venturini, “Multi-task learning for multi-dimensional regression: application to luminescence sensing,” Applied sciences, vol. 9, iss. 22, p. 4748, 2019.