  | MANCELLARI, E.* , TOPAL, A. O.* , & LEPREVOST, F.*. (21 August 2025). PoI+NBU: A Feasibility study in Generating High-Resolution Adversarial Images with a Black Box Evolutional Algorithm based Attack. Avances en Ciencias e Ingenierías, 17 (2), 92-106. doi:10.18272/aci.vi.3699  Peer reviewed* These authors have contributed equally to this work. |
 | LEPREVOST, F.* , TOPAL, A. O.* , & MANCELLARI, E.*. (2025). Réseaux neuronaux convolutionnels et reconnaissance d’images : évaluation des risques. 1024, le Bulletin, (25), 121-139. doi:10.48556/sif.1024.25 Peer reviewed* These authors have contributed equally to this work. |
 | TOPAL, A. O.* , MANCELLARI, E.* , LEPREVOST, F.* , AVDUSINOVIC, E.* , & GILLET, T.*. (April 2024). The Noise Blowing-Up Strategy Creates High Quality High Resolution Adversarial Images against Convolutional Neural Networks. Applied sciences (Basel, Switzerland), 14 (8), 3493. doi:10.3390/app14083493 Peer reviewed* These authors have contributed equally to this work. |
  | LEPREVOST, F.* , TOPAL, A. O.* , MANCELLARI, E.* , & LAVANGNANANDA, K.*. (2023). Zone-of-Interest Strategy for the Creation of High-Resolution Adversarial Images Against Convolutional Neural Networks. In 2023 15th International Conference on Information Technology and Electrical Engineering, ICITEE 2023 (pp. 127-132). Institute of Electrical and Electronics Engineers Inc. doi:10.1109/ICITEE59582.2023.10317668 Peer reviewed* These authors have contributed equally to this work. |
 | LEPREVOST, F., TOPAL, A. O., & MANCELLARI, E. (2023). Creating High-Resolution Adversarial Images Against Convolutional Neural Networks with the Noise Blowing-Up Method. In N. T. Nguyen & B. Hnatkowska (Eds.), Intelligent Information and Database Systems - 15th Asian Conference, ACIIDS 2023, Proceedings (pp. 121-134). Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-981-99-5834-4_10 Peer reviewed |
MANCELLARI, E., Bedalli, E., & Rada, R. (2020). Some assessments on applications of fuzzy clustering techniques in multimedia compression systems. In 11th International Conference on Management of Digital EcoSystems, MEDES 2019 (pp. 111-114). Association for Computing Machinery, Inc. doi:10.1145/3297662.3365799 Editorial reviewed |
Bedalli, E., MANCELLARI, E., & Haskasa, E. (2018). Exploring User Feedback Data via a Hybrid Fuzzy Clustering Model Combining Variations of FCM and Density-Based Clustering. In Lecture Notes on Data Engineering and Communications Technologies (pp. 71-81). Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-3-319-98557-2_7 Editorial reviewed |
Bedalli, E., MANCELLARI, E., & Asilkan, O. (25 October 2016). A Heterogeneous Cluster Ensemble Model for Improving the Stability of Fuzzy Cluster Analysis. Procedia Computer Science, 102, 129 - 136. doi:10.1016/j.procs.2016.09.379 Editorial reviewed |
