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Annalisa Appice
Annalisa Appice
Researcher of Computer Science, University of Bari Aldo Moro
Adresă de e-mail confirmată pe di.uniba.it - Pagina de pornire
Titlu
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Autoencoder-based deep metric learning for network intrusion detection
G Andresini, A Appice, D Malerba
Information Sciences 569, 706-727, 2021
1462021
Using convolutional neural networks for predictive process analytics
V Pasquadibisceglie, A Appice, G Castellano, D Malerba
2019 international conference on process mining (ICPM), 129-136, 2019
1392019
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
A Appice, M Ceci, A Lanza, FA Lisi, D Malerba
Intelligent Data Analysis 7 (6), 541-566, 2003
1352003
Top-down induction of model trees with regression and splitting nodes
D Malerba, F Esposito, M Ceci, A Appice
IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5), 612-625, 2004
1322004
Multi-channel deep feature learning for intrusion detection
G Andresini, A Appice, N Di Mauro, C Loglisci, D Malerba
IEEE Access 8, 53346-53359, 2020
1272020
GAN augmentation to deal with imbalance in imaging-based intrusion detection
G Andresini, A Appice, L De Rose, D Malerba
Future Generation Computer Systems 123, 108-127, 2021
1242021
Insomnia: Towards concept-drift robustness in network intrusion detection
G Andresini, F Pendlebury, F Pierazzi, C Loglisci, A Appice, L Cavallaro
Proceedings of the 14th ACM workshop on artificial intelligence and security …, 2021
1162021
Nearest cluster-based intrusion detection through convolutional neural networks
G Andresini, A Appice, D Malerba
Knowledge-Based Systems 216, 106798, 2021
1012021
A co-training strategy for multiple view clustering in process mining
A Appice, D Malerba
IEEE transactions on services computing 9 (6), 832-845, 2015
992015
Activity prediction of business process instances with inception CNN models
N Di Mauro, A Appice, TMA Basile
AI* IA 2019–Advances in Artificial Intelligence: XVIIIth International …, 2019
872019
Stepwise induction of multi-target model trees
A Appice, S Džeroski
European conference on machine learning, 502-509, 2007
862007
Mr-SBC: a multi-relational naive bayes classifier
M Ceci, A Appice, D Malerba
Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on …, 2003
772003
Mining spatial association rules in census data
D Malerba, F Esposito, FA Lisi, A Appice
Research in Official Statistics. v5 i1, 19-44, 2003
712003
Redundant feature elimination for multi-class problems
A Appice, M Ceci, S Rawles, P Flach
Proceedings of the twenty-first international conference on Machine learning, 5, 2004
642004
Network regression with predictive clustering trees
D Stojanova, M Ceci, A Appice, S Džeroski
Data Mining and Knowledge Discovery 25, 378-413, 2012
632012
A multi-view deep learning approach for predictive business process monitoring
V Pasquadibisceglie, A Appice, G Castellano, D Malerba
IEEE Transactions on Services Computing 15 (4), 2382-2395, 2021
612021
Dealing with spatial autocorrelation when learning predictive clustering trees
D Stojanova, M Ceci, A Appice, D Malerba, S Džeroski
Ecological Informatics 13, 22-39, 2013
602013
A multi-stage machine learning approach to predict dengue incidence: a case study in Mexico
A Appice, YR Gel, I Iliev, V Lyubchich, D Malerba
Ieee Access 8, 52713-52725, 2020
592020
Empowering a GIS with inductive learning capabilities: the case of INGENS
D Malerba, F Esposito, A Lanza, FA Lisi, A Appice
Computers, Environment and Urban Systems 27 (3), 265-281, 2003
572003
Predictive process mining meets computer vision
V Pasquadibisceglie, A Appice, G Castellano, D Malerba
Business Process Management Forum: BPM Forum 2020, Seville, Spain, September …, 2020
552020
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