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Francesco Sergio Pisani
Francesco Sergio Pisani
Icar-CNR
Verified email at icar.cnr.it
Title
Cited by
Cited by
Year
On learning effective ensembles of deep neural networks for intrusion detection
F Folino, G Folino, M Guarascio, FS Pisani, L Pontieri
Information Fusion 72, 48-69, 2021
442021
A distributed intrusion detection framework based on evolved specialized ensembles of classifiers
G Folino, FS Pisani, P Sabatino
Applications of Evolutionary Computation: 19th European Conference …, 2016
342016
Combining ensemble of classifiers by using genetic programming for cyber security applications
G Folino, FS Pisani
European Conference on the Applications of Evolutionary Computation, 54-66, 2015
302015
Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain
G Folino, FS Pisani
Applied Soft Computing 47, 179-190, 2016
292016
An incremental ensemble evolved by using genetic programming to efficiently detect drifts in cyber security datasets
G Folino, FS Pisani, P Sabatino
Proceedings of the 2016 on genetic and evolutionary computation conference …, 2016
282016
Boosting cyber-threat intelligence via collaborative intrusion detection
M Guarascio, N Cassavia, FS Pisani, G Manco
Future Generation Computer Systems 135, 30-43, 2022
272022
A GP-based ensemble classification framework for time-changing streams of intrusion detection data
G Folino, FS Pisani, L Pontieri
Soft Computing 24 (23), 17541-17560, 2020
182020
Automatic offloading of mobile applications into the cloud by means of genetic programming
G Folino, FS Pisani
Applied Soft Computing 25, 253-265, 2014
172014
A framework for modeling automatic offloading of mobile applications using genetic programming
G Folino, FS Pisani
European Conference on the Applications of Evolutionary Computation, 62-71, 2013
52013
Efficient discovery of data mining services over DHT-based overlays
G Folino, F Pisani, P Trunfio
2014 International Conference on High Performance Computing & Simulation …, 2014
42014
Learning and explanation of extreme multi-label deep classification models for media content
M Minici, FS Pisani, M Guarascio, E De Francesco, P Lambardi
International Symposium on Methodologies for Intelligent Systems, 138-148, 2022
22022
A scalable architecture exploiting elastic stack and meta ensemble of classifiers for profiling user behaviour
G Folino, CO Godano, FS Pisani
2022 30th Euromicro International Conference on Parallel, Distributed and …, 2022
22022
Movie tag prediction: An extreme multi-label multi-modal transformer-based solution with explanation
M Guarascio, M Minici, FS Pisani, E De Francesco, P Lambardi
Journal of Intelligent Information Systems, 1-23, 2024
12024
Unbiasing Collaborative Filtering for Popularity-Aware Recommendation (Discussion Paper).
L Caroprese, G Manco, M Minici, FS Pisani, E Ritacco
SEBD, 450-457, 2021
12021
A cybersecurity framework for classifying non stationary data streams exploiting genetic programming and ensemble learning
G Folino, FS Pisani, L Pontieri
International Conference on Numerical Computations: Theory and Algorithms …, 2019
12019
Profiling human behavior through multidimensional latent factor modeling
M Guarascio, FS Pisani, E Ritacco, P Sabatino
New Frontiers in Mining Complex Patterns: 5th International Workshop, NFMCP …, 2017
12017
Modeling the offloading of different types of mobile applications by using evolutionary algorithms
G Folino, FS Pisani
European Conference on the Applications of Evolutionary Computation, 86-97, 2014
12014
Balanced Quality Score (BQS): Measuring Popularity Debiasing in Recommendation
E Coppolillo, M Minici, E Ritacco, L Caroprese, FS Pisani, G Manco
ACM Transactions on Intelligent Systems and Technology, 2024
2024
Exploiting Deep Learning and Explanation Methods for Movie Tag Prediction
E Coppolillo, M Guarascio, M Minici, FS Pisani
Proceedings of the 27th International Database Engineered Applications …, 2023
2023
Towards Self-Supervised Cross-Domain Fake News Detection
C Comito, FS Pisani, E Coppolillo, A Liguori, M Guarascio, G Manco
2023
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