Stepwise Induction of Model Trees. - Donato Malerba; Annalisa Appice; Antonia Bellino; Michelangelo Ceci; Domenico Pallotta (AI*IA, 2001)

Generating Logic Descriptions for the Automated Interpretation of Topographic Maps. - Antonietta Lanza; Donato Malerba; Francesca A. Lisi; Annalisa Appice; Michelangelo Ceci (GREC, 2001)

Trading-Off Local versus Global Effects of Regression Nodes in Model Trees. - Donato Malerba; Annalisa Appice; Michelangelo Ceci; Marianna Monopoli (ISMIS, 2002)

KDB2000: Uno strumento per la scoperta della conoscenza. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (SEBD, 2002)

Mining Classification and Association Rules in Geographical Data with SDMOQL. - Donato Malerba; Annalisa Appice; Michelangelo Ceci; Nicola Vacca (SEBD, 2002)

Multi-relational Structural Bayesian Classifier. - Michelangelo Ceci; Annalisa Appice; Donato Malerba; Vincenzo Colonna (AI*IA, 2003)

Mining Model Trees: A Multi-relational Approach. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (ILP, 2003)

Comparing Simplification Methods for Model Trees with Regression and Splitting Nodes. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (ISMIS, 2003)

MR-SMOTI: A Data Mining System for Regression Tasks Tightly-Coupled with a Relational Database. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (KDID, 2003)

Simplification Methods for Model Trees with Regression and Splitting Nodes. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (MLDM, 2003)

Mr-SBC: A Multi-relational Naïve Bayes Classifier. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (PKDD, 2003)

Mining Model Trees with Regression and Splitting Nodes. - Annalisa Appice; Michelangelo Ceci; Floriana Esposito; Donato Malerba (SEBD, 2003)

Stepwise Model Tree Induction in a Multi-Relational Framework. - Annalisa Appice; Michelangelo Ceci; Donato Malerba; D. Sacchi (SEBD, 2003)

Discovery of spatial association rules in geo-referenced census data: A relational mining approach. - Annalisa Appice; Michelangelo Ceci; Antonietta Lanza; Francesca A. Lisi; Donato Malerba (Intell. Data Anal., 2003)

Empowering a GIS with inductive learning capabilities: the case of INGENS. - Donato Malerba; Floriana Esposito; Antonietta Lanza; Francesca A. Lisi; Annalisa Appice (Comput. Environ. Urban Syst., 2003)

A Data Mining Query Language for Knowledge Discovery in a Geographical Information System. - Donato Malerba; Annalisa Appice; Michelangelo Ceci (Database Support for Data Mining Applications, 2004)

Redundant feature elimination for multi-class problems. - Annalisa Appice; Michelangelo Ceci; Simon Alan Rawles; Peter A. Flach (ICML, 2004)

Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (PKDD, 2004)

Mining interesting spatial association rules: two case studies. - Annalisa Appice; Margherita Berardi; Michelangelo Ceci; Michele Lapi; Donato Malerba; Antonio Turi (SEBD, 2004)

Top-Down Induction of Model Trees with Regression and Splitting Nodes. - Donato Malerba; Floriana Esposito; Michelangelo Ceci; Annalisa Appice (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

Mining Relational Association Rules for Propositional Classification. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (AI*IA, 2005)

Analyzing Multi-level Spatial Association Rules Through a Graph-Based Visualization. - Annalisa Appice; Paolo Buono (IEA/AIE, 2005)

Spatial Clustering of Structured Objects. - Donato Malerba; Annalisa Appice; Antonio Varlaro; Antonietta Lanza (ILP, 2005)

Mining and Filtering Multi-level Spatial Association Rules with ARES. - Annalisa Appice; Margherita Berardi; Michelangelo Ceci; Donato Malerba (ISMIS, 2005)

Mining Model Trees from Spatial Data. - Donato Malerba; Michelangelo Ceci; Annalisa Appice (PKDD, 2005)

Propositionalization Through Relational Association Rules Mining. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (SEBD, 2005)

Relational Clustering with Discrete Spatial Structure. - Antonio Varlaro; Annalisa Appice; Antonietta Lanza; Donato Malerba; Giuseppe Guarnieri (SEBD, 2005)

Learning relational model trees. - Annalisa Appice (2005)

Mining Tolerance Regions with Model Trees. - Annalisa Appice; Michelangelo Ceci (ISMIS, 2006)

Supporting Visual Exploration of Discovered Association Rules Through Multi-Dimensional Scaling. - Margherita Berardi; Annalisa Appice; Corrado Loglisci; Pietro Leo (ISMIS, 2006)

Classifying Aggregated Data: a Symbolic Data Analysis Approach. - Annalisa Appice; Floriana Esposito; Donato Malerba (SEBD, 2006)

Classification of symbolic objects: A lazy learning approach. - Annalisa Appice; Claudia d'Amato; Floriana Esposito; Donato Malerba (Intell. Data Anal., 2006)

Spatial associative classification: propositional vs structural approach. - Michelangelo Ceci; Annalisa Appice (J. Intell. Inf. Syst., 2006)

Discovering Relational Emerging Patterns. - Annalisa Appice; Michelangelo Ceci; Carlo Malgieri; Donato Malerba (AI*IA, 2007)

Stepwise Induction of Multi-target Model Trees. - Annalisa Appice; Saso Dzeroski (ECML, 2007)

An Integrated Platform for Spatial Data Mining within a GIS Environment. - Annalisa Appice; Antonietta Lanza; Donato Malerba (ICDE Workshops, 2007)

Transductive Learning from Relational Data. - Michelangelo Ceci; Annalisa Appice; Nicola Barile; Donato Malerba (MLDM, 2007)

Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (PKDD, 2007)

Inducing Multi-Target Model Trees in a Stepwise Fashion. - Annalisa Appice; Saso Dzeroski (SEBD, 2007)

On Homogeneity Evaluation and Seed Selection in Clustering Relational Data. - Antonio Varlaro; Annalisa Appice; Antonietta Lanza; Antonio Fittipaldi (SEBD, 2007)

Emerging Pattern Based Classification in Relational Data Mining. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (DEXA, 2008)

A Grid-Based Multi-relational Approach to Process Mining. - Antonio Turi; Annalisa Appice; Michelangelo Ceci; Donato Malerba (DEXA, 2008)

Geographic Knowledge Discovery in INGENS: An Inductive Database Perspective. - Annalisa Appice; Anna Ciampi; Antonietta Lanza; Donato Malerba; Antonella Rapolla; Luisa Vetturi (ICDM Workshops, 2008)

Top-Down Induction of Relational Model Trees in Multi-instance Learning. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (ILP, 2008)

Stepwise Induction of Logistic Model Trees. - Annalisa Appice; Michelangelo Ceci; Donato Malerba; Savino Saponara (ISMIS, 2008)

Discovering Emerging Patterns for Anomaly Detection in Network Connection Data. - Michelangelo Ceci; Annalisa Appice; Costantina Caruso; Donato Malerba (ISMIS, 2008)

Relational Classification based on Emerging Patterns. - Michelangelo Ceci; Annalisa Appice; Lucrezia Macchia; Donato Malerba (SEBD, 2008)

Distributed Discovery of Multi-Level Approximate Process Patterns. - Antonio Turi; Annalisa Appice; Michelangelo Ceci; Donato Malerba (SEBD, 2008)

Approximate Frequent Itemset Discovery from Data Stream. - Anna Ciampi; Fabio Fumarola; Annalisa Appice; Donato Malerba (AI*IA, 2009)

An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (Discovery Science, 2009)

A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams. - Fabio Fumarola; Anna Ciampi; Annalisa Appice; Donato Malerba (Discovery Science, 2009)

Novelty Detection from Evolving Complex Data Streams with Time Windows. - Michelangelo Ceci; Annalisa Appice; Corrado Loglisci; Costantina Caruso; Fabio Fumarola; Donato Malerba (ISMIS, 2009)

Relational Frequent Patterns Mining for Novelty Detection from Data Streams. - Michelangelo Ceci; Annalisa Appice; Corrado Loglisci; Costantina Caruso; Fabio Fumarola; Carmine Valente; Donato Malerba (MLDM, 2009)

A Relational Approach to Novelty Detection in Data Streams. - Annalisa Appice; Michelangelo Ceci; Corrado Loglisci; Costantina Caruso; Fabio Fumarola; Michele Todaro; Donato Malerba (SEBD, 2009)

Spatial Regression in the Transductive Setting. - Annalisa Appice; Michelangelo Ceci; Vincenzo Rizzi; Marco Romano; Donato Malerba (SEBD, 2009)

Mining preference relations to rank complex object. - Michelangelo Ceci; Annalisa Appice; Giuseppe De Giosa; Gianluigi Dileo; Alessandro Lallo; Donato Malerba (SEBD, 2009)

A relational approach to probabilistic classification in a transductive setting. - Donato Malerba; Michelangelo Ceci; Annalisa Appice (Eng. Appl. Artif. Intell., 2009)

Discovering Informative Syntactic Relationships between Named Entities in Biomedical Literature. - Annalisa Appice; Michelangelo Ceci; Corrado Loglisci (DBKDA, 2010)

Time-Slice Density Estimation for Semantic-Based Tourist Destination Suggestion. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (ECAI, 2010)

Online and Offline Trend Cluster Discovery in Spatially Distributed Data Streams. - Anna Ciampi; Annalisa Appice; Donato Malerba (MSM/MUSE, 2010)

Summarization for Geographically Distributed Data Streams. - Anna Ciampi; Annalisa Appice; Donato Malerba (KES (3), 2010)

Transductive learning for spatial regression with co-training. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (SAC, 2010)

Complex objects ranking: a relational data mining approach. - Michelangelo Ceci; Annalisa Appice; Corrado Loglisci; Donato Malerba (SAC, 2010)

Suggesting Tourist Destinations by means of Time-Slice Density Estimation. - Michelangelo Ceci; Annalisa Appice; Donato Malerba; Nicola Schirone; Nicola Davide Traversa; Valerio Valrosso (SEBD, 2010)

Clustering Spatio-Temporal Data Streams. - Anna Ciampi; Annalisa Appice; Donato Malerba; Giuseppe Saponaro; Domenico Triglione (SEBD, 2010)

Transductive Learning for Spatial Data Classification. - Michelangelo Ceci; Annalisa Appice; Donato Malerba (Advances in Machine Learning I, 2010)

Trend cluster based compression of geographically distributed data streams. - Anna Ciampi; Annalisa Appice; Donato Malerba; Pietro Guccione (CIDM, 2011)

Discovering process models through relational disjunctive patterns mining. - Corrado Loglisci; Michelangelo Ceci; Annalisa Appice; Donato Malerba (CIDM, 2011)

Global and Local Spatial Autocorrelation in Predictive Clustering Trees. - Daniela Stojanova; Michelangelo Ceci; Annalisa Appice; Donato Malerba; Saso Dzeroski (Discovery Science, 2011)

MBlab: Molecular Biodiversity Laboratory. - Corrado Loglisci; Annalisa Appice; Michelangelo Ceci; Donato Malerba; Floriana Esposito (IRCDL, 2011)

Space-Time Roll-up and Drill-down into Geo-Trend Stream Cubes. - Anna Ciampi; Annalisa Appice; Donato Malerba; Angelo Muolo (ISMIS, 2011)

Relational Mining in Spatial Domains: Accomplishments and Challenges. - Donato Malerba; Michelangelo Ceci; Annalisa Appice (ISMIS, 2011)

Relational Disjunctive Patterns Mining for Discovering Frequent Variants in Process Models. - Corrado Loglisci; Michelangelo Ceci; Annalisa Appice; Donato Malerba (SEBD, 2011)

Learning and Transferring Geographically Weighted Regression Trees across Time. - Annalisa Appice; Michelangelo Ceci; Donato Malerba; Antonietta Lanza (MSM/MUSE, 2011)

Trend Cluster Based Kriging Interpolation in Sensor Data Networks. - Pietro Guccione; Annalisa Appice; Anna Ciampi; Donato Malerba (MSM/MUSE, 2011)

A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets. - Annalisa Appice; Michelangelo Ceci; Antonio Turi; Donato Malerba (Intell. Data Anal., 2011)

Continuously Mining Sliding Window Trend Clusters in a Sensor Network. - Annalisa Appice; Donato Malerba; Anna Ciampi (DEXA (2), 2012)

Transductive Relational Classification in the Co-training Paradigm. - Michelangelo Ceci; Annalisa Appice; Herna L. Viktor; Donato Malerba; Eric Paquet; Hongyu Guo (MLDM, 2012)

Using Geographic Cost Functions to Discover Vessel Itineraries from AIS Messages. - Annalisa Appice; Donato Malerba; Antonietta Lanza (MSM/MUSE, 2012)

Trend cluster based interpolation everywhere in a sensor network. - Pietro Guccione; Anna Ciampi; Annalisa Appice; Donato Malerba; Angelo Muolo (SAC, 2012)

Integrating Trend Clusters for Spatio-temporal Interpolation of Missing Sensor Data. - Anna Ciampi; Annalisa Appice; Pietro Guccione; Donato Malerba (W2GIS, 2012)

Network regression with predictive clustering trees. - Daniela Stojanova; Michelangelo Ceci; Annalisa Appice; Saso Dzeroski (Data Min. Knowl. Discov., 2012)

Enhancing Regression Models with Spatio-temporal Indicator Additions. - Annalisa Appice; Sonja Pravilovic; Donato Malerba; Antonietta Lanza (AI*IA, 2013)

An Intelligent Technique for Forecasting Spatially Correlated Time Series. - Sonja Pravilovic; Annalisa Appice; Donato Malerba (AI*IA, 2013)

Predictive Regional Trees to Supplement Geo-Physical Random Fields. - Annalisa Appice; Sonja Pravilovic; Donato Malerba (CORES, 2013)

Process Mining to Forecast the Future of Running Cases. - Sonja Pravilovic; Annalisa Appice; Donato Malerba (NFMCP, 2013)

Dealing with spatial autocorrelation when learning predictive clustering trees. - Daniela Stojanova; Michelangelo Ceci; Annalisa Appice; Donato Malerba; Saso Dzeroski (Ecol. Informatics, 2013)

Using trend clusters for spatiotemporal interpolation of missing data in a sensor network. - Annalisa Appice; Anna Ciampi; Donato Malerba; Pietro Guccione (J. Spatial Inf. Sci., 2013)

Data Mining Techniques in Sensor Networks - Summarization, Interpolation and Surveillance. - Annalisa Appice; Anna Ciampi; Fabio Fumarola; Donato Malerba (Springer, 2014)

Wind Power Forecasting Using Time Series Cluster Analysis. - Sonja Pravilovic; Annalisa Appice; Antonietta Lanza; Donato Malerba (Discovery Science, 2014)

A Business Intelligence Solution for Monitoring Efficiency of Photovoltaic Power Plants. - Fabio Fumarola; Annalisa Appice; Donato Malerba (ISMIS, 2014)

Collective Inference for Handling Autocorrelation in Network Regression. - Corrado Loglisci; Annalisa Appice; Donato Malerba (ISMIS, 2014)

Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data. - Sonja Pravilovic; Annalisa Appice; Donato Malerba (ISMIS, 2014)

Network Regression in Collective Inference Setting. - Corrado Loglisci; Annalisa Appice; Antonella Montinari; Donato Malerba (SEBD, 2014)

Mining Cluster-Based Models of Time Series for Wind Power Prediction. - Sonja Pravilovic; Annalisa Appice; Antonietta Lanza; Donato Malerba (SEBD, 2014)

Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering. - Annalisa Appice; Donato Malerba (Data Min. Knowl. Discov., 2014)

Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (Fundam. Informaticae, 2014)

Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. - Annalisa Appice; Pietro Guccione; Donato Malerba; Anna Ciampi (Inf. Sci., 2014)

Mining complex patterns. - Annalisa Appice; Michelangelo Ceci; Corrado Loglisci; Elio Masciari; Giuseppe Manco 001 (J. Intell. Inf. Syst., 2014)

Very Short-Term Wind Speed Forecasting Using Spatio-Temporal Lazy Learning. - Annalisa Appice; Sonja Pravilovic; Antonietta Lanza; Donato Malerba (Discovery Science, 2015)

Discovering and Tracking Organizational Structures in Event Logs. - Annalisa Appice; Marco Di Pietro; Claudio Greco 002; Donato Malerba (NFMCP, 2015)

Summarizing numeric spatial data streams by trend cluster discovery. - Annalisa Appice; Anna Ciampi; Donato Malerba (Data Min. Knowl. Discov., 2015)

Iterative Hyperspectral Image Classification Using Spectral-Spatial Relational Features. - Pietro Guccione; Luigi Mascolo; Annalisa Appice (IEEE Trans. Geosci. Remote. Sens., 2015)

Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image Classification. - Annalisa Appice; Pietro Guccione (DS, 2016)

Anomaly detection in aerospace product manufacturing: Initial remarks. - Anna Maria Crespino; Angelo Corallo; Mariangela Lazoi; Donato Barbagallo; Annalisa Appice; Donato Malerba (RTSI, 2016)

Recent advances in mining patterns from complex data. - Annalisa Appice; Michelangelo Ceci; Corrado Loglisci; Giuseppe Manco 001; Elio Masciari (J. Intell. Inf. Syst., 2016)

Collective regression for handling autocorrelation of network data in a transductive setting. - Corrado Loglisci; Annalisa Appice; Donato Malerba (J. Intell. Inf. Syst., 2016)

Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system. - Annalisa Appice; Pietro Guccione; Donato Malerba (Mach. Learn., 2016)

A Co-Training Strategy for Multiple View Clustering in Process Mining. - Annalisa Appice; Donato Malerba (IEEE Trans. Serv. Comput., 2016)

Sampling Training Data for Accurate Hyperspectral Image Classification via Tree-Based Spatial Clustering. - Annalisa Appice; Sonja Pravilovic; Donato Malerba; Antonietta Lanza (AI*IA, 2017)

Using multiple time series analysis for geosensor data forecasting. - Sonja Pravilovic; Massimo Bilancia; Annalisa Appice; Donato Malerba (Inf. Sci., 2017)

A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data. - Annalisa Appice; Pietro Guccione; Donato Malerba (Pattern Recognit., 2017)

Relational Data Mining in the Era of Big Data. - Annalisa Appice; Michelangelo Ceci; Donato Malerba (A Comprehensive Guide Through the Italian Database Research, 2018)

Handling Multi-scale Data via Multi-target Learning for Wind Speed Forecasting. - Annalisa Appice; Antonietta Lanza; Donato Malerba (ISMIS, 2018)

Wind Speed Forecasting via Structured Output Learning. - Annalisa Appice; Antonietta Lanza; Donato Malerba (SEBD, 2018)

Leveraging correlation across space and time to interpolate geophysical data via CoKriging. - Sonja Pravilovic; Annalisa Appice; Donato Malerba (Int. J. Geogr. Inf. Sci., 2018)

Active learning via collective inference in network regression problems. - Annalisa Appice; Corrado Loglisci; Donato Malerba (Inf. Sci., 2018)

Towards mining the organizational structure of a dynamic event scenario. - Annalisa Appice (J. Intell. Inf. Syst., 2018)

Leveraging Shallow Machine Learning to Predict Business Process Behavior. - Annalisa Appice; Nicola Di Mauro; Donato Malerba (SCC, 2019)

Activity Prediction of Business Process Instances with Inception CNN Models. - Nicola Di Mauro; Annalisa Appice; Teresa M. A. Basile (AI*IA, 2019)

Exploiting the Auto-Encoder Residual Error for Intrusion Detection. - Giuseppina Andresini; Annalisa Appice; Nicola Di Mauro; Corrado Loglisci; Donato Malerba (EuroS&P Workshops, 2019)

Using Convolutional Neural Networks for Predictive Process Analytics. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (ICPM, 2019)

Empowering Change Vector Analysis with Autoencoding in Bi-temporal Hyperspectral Images. - Annalisa Appice; Nicola Di Mauro; Francesco Lomuscio; Donato Malerba (MACLEAN@PKDD/ECML, 2019)

Predictive Process Mining Meets Computer Vision. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (BPM (Forum), 2020)

Training in a Virtual Learning Environment: A Process Mining Approach. - Annalisa Appice; Pasquale Ardimento; Donato Malerba; Giuseppe Modugno; Diego Marra; Marco Mottola (EAIS, 2020)

Saliency Detection for Hyperspectral Images via Sparse-Non Negative-Matrix-Factorization and novel Distance Measures*. - Antonella Falini; Graziano Castellano; Cristiano Tamborrino; Francesca Mazzia; Rosa Maria Mininni; Annalisa Appice; Donato Malerba (EAIS, 2020)

Saliency Detection in Hyperspectral Images Using Autoencoder-Based Data Reconstruction. - Annalisa Appice; Francesco Lomuscio; Antonella Falini; Cristiano Tamborrino; Francesca Mazzia; Donato Malerba (ISMIS, 2020)

Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images. - Antonella Falini; Cristiano Tamborrino; Graziano Castellano; Francesca Mazzia; Rosa Maria Mininni; Annalisa Appice; Donato Malerba (LOD (1), 2020)

Applying Machine Learning to Predict Closing Prices in Stock Market: A Case Study. - Matteo Greco; Michele Spagnoletta; Annalisa Appice; Donato Malerba (MIDAS@PKDD/ECML, 2020)

Multi-Channel Deep Feature Learning for Intrusion Detection. - Giuseppina Andresini; Annalisa Appice; Nicola Di Mauro; Corrado Loglisci; Donato Malerba (IEEE Access, 2020)

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico. - Annalisa Appice; Yulia R. Gel; Iliyan Iliev; Vyacheslav Lyubchich; Donato Malerba (IEEE Access, 2020)

ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba; Giuseppe Modugno (IEEE Access, 2020)

Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification. - Annalisa Appice; Pietro Guccione; Emilio Acciaro; Donato Malerba (Appl. Intell., 2020)

Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection. - Annalisa Appice; Giuseppina Andresini; Donato Malerba (J. Intell. Inf. Syst., 2020)

Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (Complex Pattern Mining, 2020)

Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images. - Giuseppina Andresini; Annalisa Appice; Domenico Dell'Olio; Donato Malerba (AI*IA, 2021)

Leveraging Multi-view Deep Learning for Next Activity Prediction. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (ITBPM@BPM, 2021)

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection. - Giuseppina Andresini; Feargus Pendlebury; Fabio Pierazzi; Corrado Loglisci; Annalisa Appice; Lorenzo Cavallaro (AISec@CCS, 2021)

A Network Intrusion Detection System for Concept Drifting Network Traffic Data. - Giuseppina Andresini; Annalisa Appice; Corrado Loglisci; Vincenzo Belvedere; Domenico Redavid; Donato Malerba (DS, 2021)

Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems. - Francesco Paolo Caforio; Giuseppina Andresini; Gennaro Vessio; Annalisa Appice; Donato Malerba (DS, 2021)

AI meets Cybersecurity. - Annalisa Appice (FMEC, 2021)

FOX: a neuro-Fuzzy model for process Outcome prediction and eXplanation. - Vincenzo Pasquadibisceglie; Giovanna Castellano; Annalisa Appice; Donato Malerba (ICPM, 2021)

Dealing with Imbalanced Data in Multi-class Network Intrusion Detection Systems Using XGBoost. - Malik Al-Essa; Annalisa Appice (PKDD/ECML Workshops (2), 2021)

Keynote 4. - Annalisa Appice (SDS, 2021)

A Two-Step Network Intrusion Detection System for Multi-Class Classification (Discussion Paper). - Giuseppina Andresini; Annalisa Appice; Donato Malerba (SEBD, 2021)

GAN augmentation to deal with imbalance in imaging-based intrusion detection. - Giuseppina Andresini; Annalisa Appice; Luca De Rose; Donato Malerba (Future Gener. Comput. Syst., 2021)

Autoencoder-based deep metric learning for network intrusion detection. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (Inf. Sci., 2021)

Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets. - Annalisa Appice; Angelo Cannarile; Antonella Falini; Donato Malerba; Francesca Mazzia; Cristiano Tamborrino (J. Intell. Inf. Syst., 2021)

Nearest cluster-based intrusion detection through convolutional neural networks. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (Knowl. Based Syst., 2021)

Introduction to the special issue of the ECML PKDD 2021 journal track. - Annalisa Appice; Sergio Escalera; José A. Gámez 001; Heike Trautmann (Mach. Learn., 2021)

An XAI-based adversarial training approach for cyber-threat detection. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (DASC/PiCom/CBDCom/CyberSciTech, 2022)

XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (ISMIS, 2022)

Review Spam Detection using Multi-View Deep Learning Combining Content and Behavioral Features. - Giuseppina Andresini; Andrea Iovine; Roberto Gasbarro; Marco Lomolino; Marco de Gemmis; Annalisa Appice (itaDATA, 2022)

A multi-view deep learning approach for predictive business processes monitoring. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (SERVICES, 2022)

ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection. - Giuseppina Andresini; Annalisa Appice; Francesco Paolo Caforio; Donato Malerba; Gennaro Vessio (Expert Syst. Appl., 2022)

PROMISE: Coupling predictive process mining to process discovery. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Wil M. P. van der Aalst (Inf. Sci., 2022)

Leveraging autoencoders in change vector analysis of optical satellite images. - Giuseppina Andresini; Annalisa Appice; Daniele Iaia; Donato Malerba; Nicolò Taggio; Antonello Aiello (J. Intell. Inf. Syst., 2022)

A Multi-View Deep Learning Approach for Predictive Business Process Monitoring. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (IEEE Trans. Serv. Comput., 2022)

CENTAURO: An Explainable AI Approach for Customer Loyalty Prediction in Retail Sector. - Giuseppina Andresini; Annalisa Appice; Pasquale Ardimento; Andrea Antonio Brunetta; Antonio Giuseppe Doronzo; Giuseppe Ieva; Francesco Luce; Donato Malerba; Vincenzo Pasquadibisceglie (AI*IA, 2023)

GLORIA: A Graph Convolutional Network-Based Approach for Review Spam Detection. - Giuseppina Andresini; Annalisa Appice; Roberto Gasbarro; Donato Malerba (DS, 2023)

Striving for Simplicity in Deep Neural Models Trained for Malware Detection. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (PKDD/ECML Workshops (3), 2023)

Using Graph Neural Networks for the Detection and Explanation of Network Intrusions. - Ahmed Rafik El-Mehdi Baahmed; Giuseppina Andresini; Céline Robardet; Annalisa Appice (PKDD/ECML Workshops (3), 2023)

DARWIN : An online deep learning approach to handle concept drifts in predictive process monitoring. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (Eng. Appl. Artif. Intell., 2023)

SENECA: Change detection in optical imagery using Siamese networks with Active-Transfer Learning. - Giuseppina Andresini; Annalisa Appice; Dino Ienco; Donato Malerba (Expert Syst. Appl., 2023)

$ mathsf{SILVIA}$: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023)

STARDUST: A Novel Process Mining Approach to Discover Evolving Models From Trace Streams. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Nicola Fiorentino; Donato Malerba (IEEE Trans. Serv. Comput., 2023)

Editorial: AI meets cybersecurity. - Giuseppina Andresini; Annalisa Appice (J. Intell. Inf. Syst., 2023)

An AI framework to support decisions on GDPR compliance. - Filippo Lorè; Pierpaolo Basile; Annalisa Appice; Marco de Gemmis; Donato Malerba; Giovanni Semeraro (J. Intell. Inf. Syst., 2023)

An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images. - Vito Recchia; Giuseppina Andresini; Annalisa Appice; Gianpietro Fontana; Donato Malerba (DS (2), 2024)

LUPIN: A LLM Approach for Activity Suffix Prediction in Business Process Event Logs. - Vincenzo Pasquadibisceglie; Annalisa Appice; Donato Malerba (ICPM, 2024)

Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback Due to Bark Beetle Hotspots in Sentinel-2 Images. - Giuseppina Andresini; Annalisa Appice; Dino Ienco; Donato Malerba; Vito Recchia (IGARSS, 2024)

Enhancing Cyber-threat detection coupling Deep Neural Ensemble Learning with XAI. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Ital-IA, 2024)

Leveraging Sentinel-2 time series for bark beetle-induced forest dieback inventory. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (SAC, 2024)

Enhancing Next Activity Prediction with Adversarial Training of Vision Transformers. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (SEBD, 2024)

VINCENT: Cyber-threat detection through vision transformers and knowledge distillation. - Luca De Rose; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Comput. Secur., 2024)

A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)

JARVIS: Joining Adversarial Training With Vision Transformers in Next-Activity Prediction. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giovanna Castellano; Donato Malerba (IEEE Trans. Serv. Comput., 2024)

${ sf PROPHET}$PROPHET: Explainable Predictive Process Monitoring With Heterogeneous Graph Neural Networks. - Vincenzo Pasquadibisceglie; Raffaele Scaringi; Annalisa Appice; Giovanna Castellano; Donato Malerba (IEEE Trans. Serv. Comput., 2024)

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection. - Muhammad Imran; Annalisa Appice; Donato Malerba (Future Internet, 2024)

TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments. - Vincenzo Pasquadibisceglie; Annalisa Appice; Giuseppe Ieva; Donato Malerba (J. Intell. Inf. Syst., 2024)

PANACEA: a neural model ensemble for cyber-threat detection. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Mach. Learn., 2024)

DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images. - Giuseppina Andresini; Annalisa Appice; Dino Ienco; Vito Recchia (J. Intell. Inf. Syst., 2024)

Adversarial Training to Improve Accuracy and Robustness of a Windows PE Malware Detection Model. - Luca Lobascio; Giuseppina Andresini; Annalisa Appice; Donato Malerba (ITASEC/SERICS, 2025)

GANDALF: A LLM-based approach to map bark beetle outbreaks in semantic stories of Sentinel-2 images. - Vincenzo Pasquadibisceglie; Vito Recchia; Annalisa Appice; Donato Malerba; Giuseppe Fiameni (SAC, 2025)

Leveraging a large language model (LLM) to predict hospital admissions of emergency department patients. - Vincenzo Pasquadibisceglie; Annalisa Appice; Donato Malerba; Giuseppe Fiameni (Expert Syst. Appl., 2025)

OLIVANDER: a counterfactual-based method to generate adversarial Windows PE malware. - Luca De Rose; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Data Min. Knowl. Discov., 2025)