Supply Chain Network Design under Uncertainty with Evidence Theory. - Ahmed Samet; Yamine Bouzembrak; Eric Lefèvre (Logist. Res., 2017)

The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data. - Peter Wittenburg; Hana Pergl Sustkova; Annalisa Montesanti; Margreet Bloemers; S. H. de Waard; Mark A. Musen; John B. Graybeal; Kristina M. Hettne; Annika Jacobsen; Robert Pergl; Rob W. W. Hooft; Christine Staiger; et al. (CoRR, 2019)

Predicting Nitrogen Excretion of Dairy Cattle with Machine Learning. - Herman Mollenhorst; Yamine Bouzembrak; Michel de Haan; Hans J. P. Marvin; Roel F. Veerkamp; Claudia Kamphuis (ISESS, 2020)

Machine learning models for predicting malnutrition in NICU patients: A comprehensive benchmarking study. - Sander M. W. Janssen; Yamine Bouzembrak; Nadir Yalcin; Bedir Tekinerdogan (Comput. Biol. Medicine, 2025)

Assessing the potential of quantum computing in agriculture. - Torsten Pook; Jérémie Vandenplas; Juan Carlos Boschero; Esteban Aguilera; Koen Leijnse; Aneesh Chauhan; Yamine Bouzembrak; Rob Knapen; Michael Aldridge (Comput. Electron. Agric., 2025)

Food fraud detection using explainable artificial intelligence. - Okan Buyuktepe; Cagatay Catal; Gorkem Kar; Yamine Bouzembrak; Hans J. P. Marvin; Anand K. Gavai (Expert Syst. J. Knowl. Eng., 2025)

Transforming Hyperspectral Images Into Chemical Maps: An End-to-End Deep Learning Approach. - Ole-Christian Galbo Engstrøm; Michela Albano-Gaglio; Erik Schou Dreier; Yamine Bouzembrak; Maria Font-i-Furnols; Puneet Mishra; Kim Steenstrup Pedersen (CoRR, 2025)