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De Falco
Name: Ivanoe
Surname: De Falco
Role: Research Director
Identification Number: 505
Phone: +39 0816139524
Location: Napoli
Fax: 081 6139531






Research director                                                                                         Via Pietro Castellino, 111

Institute for High-Performance Computing and Networking (ICAR)           80131 Naples, Italy

National Research Council of Italy (CNR)                                            




Ivanoe De Falco is a Research Director at the Institute for High-Performance Computing and Networking (ICAR), an institute making part of the National Research Council of Italy (CNR).

He received the M.Sc. degree “cum laude” in Electronic Engineering from the University of Naples “Federico II”, Italy, in 1987.

Since 1994, he works as a researcher for CNR (1994: researcher; 1998: confirmed researcher; 2007: senior researcher; 2021: research director).

Since 2020, Ivanoe De Falco is the responsible person of the Research Group on Innovative Models for Machine Learning (IMML) at ICAR-CNR. For a summary of the group research activities, goals, and application fields, please check the IMML site:

In 2021, he was listed in the top 2% researchers ranking from Stanford University:

From 2016 to 2019 he was the responsible person for the Laboratory of Computational Intelligence (CILab) at ICAR-CNR.

in 2013, Ivanoe De Falco received the Best Paper Award at the EvoIASP workshop for the paper “Adding chaos to Differential Evolution for Range Image Registration”.

In 2005, his paper “The eruptive activity of Vesuvius and its neural architecture” was selected in 2005 to represent CNR within CIVR 2001 – 2003 evaluation.




Ivanoe De Falco’s research activity focuses on Computational intelligence and Soft Computing, with specific, although non-exclusive, references to Evolutionary Computation (EC) and Swarm Intelligence (SI). Going into details, over the years he has both used already existing such algorithms and designed and implemented new algorithms. In both cases, the goal has been their application within the Artificial Intelligence area, particularly with reference to Machine Learning. Within this area, his research has very frequently aimed at the Interpretability of the solutions proposed.

Given his expertise in Parallel Computing, he has also designed and implemented parallel and distributed versions of Evolutionary Algorithms, and of Swarm Intelligence ones as well. This turns out important whenever the time factor is considered relevant, and also helps improve the search so as to find better-quality solutions with respect to the corresponding sequential algorithms.

He has applied these algorithms to many different types of problems, among which we can recall here at least optimization, data mining, classification, clustering, regression, time series forecasting.  From among the many application fields, at least e-health, tourism, middleware for distributed systems can be cited here.

Since the beginning of his research activity, Ivanoe De Falco has always worked taking as the primary goal the design of algorithms that could allow users to automatically extract explicit and easily-understandable knowledge from the application domain (Interpretable Machine Learning). This knowledge can be, for example, under the form of a set of IF-THEN rules for classification tasks, or an explicit expression linking the values of two or more parameters in regression, or an explicit expression connecting future values of a parameter to its past ones in forecasting tasks.

As the application fields, these have been numerous, because of the general-purposiveness of the EC and SI algorithms employed. Just to mention some of the most interesting, it should be cited at least the neuro-evolution, a field in which evolutionary and swarm intelligence algorithms are used to automatically find the best structure of an Artificial Neural Network (ANN) and the best values for its parameters. This is important for shallow ANNs, and becomes crucial for deep ANNs, due to their complexity in size.

Another important research activity worth mentioning here is that related to the real-time monitoring of a subject’s health conditions, with specific reference to the automatic management of vital signals gathered from the subject through sensors and smart devices. This has been investigated by Ivanoe De Falco with reference to diseases and problems as, e.g., Obstructive Sleep Apnea, Blood Hypertension, Daily Living Activity and Fall Assessment, Blood Glucose Prediction in Diabetes.

With reference to this latter, Ivanoe De Falco is participating in the “SmartCGM” International Cooperation project, coordinated by Antonio Della Cioppa (University of Salerno) and composed by research units from the Natural Computation Lab, DIEM, University di Salerno, the Research Group on Innovative Models for Machine Learning of the Institute for High Performing Computing and Networking (ICAR), CNR, and the Laboratory for Medical Informatics, NTIS Research Centre & the Department of Computer Science, University of West Bohemia (Czech Republic). The project aims at applying Interpretable Machine Learning methodologies to Diabetes with specific reference to subjects suffering from Type-I diabetes mellitus, at predicting blood glucose values, at extracting personalized models for each subject, and at gaining insights into the metabolic system dynamics.

A further distinctive aspect of the research activity carried out by Ivanoe De Falco lies in his considering multi-objective problems, in which the goal is to automatically find solutions that can satisfy at the same time a set of contrasting objectives. This holds true even in the presence of constraints. Evolutionary /Swarm intelligence algorithms are very useful in facing these kinds of problems.




Ivanoe De Falco has authored on the above reported topics about 175 papers in international refereed journals, books and conferences, among which at least the following can be mentioned here: IEEE Transactions on Industrial Informatics  (JCR IF 2019: 9.112), Future Generation Computer Systems  (JCR IF 2019: 6.125), Information Sciences (JCR IF 2019: 5.910), Journal of Network and Computer Applications (JCR IF 2019: 5.570),  Applied Soft Computing (JCR IF 2019: 5.472), IEEE Journal of Biomedical and Health Informatics (JCR IF 2019: 5.223), Neural Computing and Applications (JCR IF 2019: 4.774), Journal of Biomedical Informatics (JCR IF 2019: 3.526), Soft Computing (JCR IF 2019: 3.050), International Journal of Medical Informatics (JCR IF 2019: 3.025).

The whole impact of his research is assessed by the following indicators, as of May 30, 2022:

– Google Scholar:

All: H-index: 26, citations: 2420, i10-index: 60

Since 2017: H-index: 17, citations: 935, i10-index: 28




He has been responsible for a CNR research unit in several projects related to Machine Learning and funded by Italian agencies, as:

  • “ORganization of Cultural HEritage for Smart Tourism and Real-time Accessibility (OR.C.HE.S.T.R.A.)” (PON)
  • “Parallel Problem Solving and heuristic optimization methods” (bilateral cooperation project with the Institute of Computer Science (IPI) of the Polish Academy of Sciences (PAN))
  • “Forecasting of extreme natural events through innovative computational techniques” (Region Campania)
  • “Phoenix” Cooperation project between IRSIP-CNR and the Italian Centre for Research in Aerospace (CIRA, Capua-CE)
  • International Cooperation Agreement between IRSIP-CNR and von Karman Institute for Fluid Dynamics, Rhode Saint Genese, Belgium

Moreover, he participated and is participating in many projects funded by Italian agencies.




Since 2009, he acts as an Associate Editor for Applied Soft Computing journal (Elsevier, JCR IF 2020: 6.725), for which he served as a member of the Editorial Board in 2004 – 2009.

Moreover, he has acted as a Guest Editor for several special issues in important journals, as:

  • IEEE Journal of Biological and Health Informatics (2020)
  • Computers (2021, 2020)
  • Sensors (2022, 2021)
  • Applied Soft Computing (2019)
  • Journal of Network and Computer Applications (2018).

Furthermore, he acted as an Editorial Board member for the Journal of Artificial Evolution and Applications (2008 – 2010).

Also, he was the co-editor for seven volumes of the Lecture Notes in Computer Science (Elsevier) between 2008 and 2016.




Ivanoe De Falco participates in many international organizations:

– Confederation of Laboratories for Artificial Intelligence Research in Europe” (CLAIRE)

– IEEE Systems, Man, and Cybernetics Society’s Technical Committee on Soft Computing

– IEEE ComSoc Special Interest Research Group on Big Data for e-Health

– IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing

– World Federation on Soft Computing (WFSC)

– EU COST action CA15140 “Improving Applicability of Nature-Inspired Optimisation




Ivanoe De Falco has been on the organizing committee of many important international conferences, workshops, and events as:

  • IEEE ICTS4eHealth conference (2022, 2021)
  • ACM NEWK workshop (2022, 2021, 2020)
  • ACM PDEIM workshop (2021,2020, 2019, 2018)
  • ICTS4eHealth Workshop (2020, 2019, 2018, 2017, 2016)
  • IEEE AIdSH workshop (2020)
  • IEEE AI4H:B2E special track (2019)
  • AI4Health workshop (2018)
  • IEEE MHAEC special session (2017)
  • IEEE PDEIM special session (2017)
  • SmartMedDev special session (2017, 2016)
  • EvoComNet workshop (2016, 2015, 2014, 2013)
  • Evostar International Conference (2009, 2008)
  • Third World Conference on Soft Computing (1998).




  • Giuseppe Trautteur, University of Naples “Federico II”, Italy (Artificial Intelligence)
  • Antonio Della Cioppa, University of Salerno, Italy (Machine Learning)
  • Giuseppe Luongo, University of Naples “Federico II”, Italy (AI for earth sciences)
  • Marek Tudruj, IPI-PAN, Poland (optimization for parallel computing)
  • Richard Olejnik, Université de Lile, France (optimization for parallel computing)
  • Tomas Koutny, University of West Bohemia, Czech Republic (Machine Learning for diabetes)




  1.     De Falco, I., De Pietro, G. and Sannino, G., 2022. Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach. Neural Computing and Applications, pp.1-11.
  2.     Sannino, G., De Pietro, G. and De Falco, I., 2021, December. Automatic Extraction of Interpretable Knowledge to Predict the Survival of Patients with Heart Failure. In 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 166-173). IEEE.
  3.     De Falco, I., Della Cioppa, A., Koutny, T., Scafuri, U., Tarantino, E. and Ubl, M., 2021, September. Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models. In 2021 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-6). IEEE.
  4.     Sannino, G., De Falco, I. and De Pietro, G., 2020. Non-invasive risk stratification of hypertension: A systematic comparison of machine learning algorithms. Journal of Sensor and Actuator Networks, 9(3), p.34.
  5.     De Falco, I., Della Cioppa, A., Giugliano, A., Marcelli, A., Koutny, T., Krcma, M., Scafuri, U. and Tarantino, E., 2019. A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives. Applied Soft Computing, 77, pp.316-328.
  6.     De Falco, I., Della Cioppa, A., Koutny, T., Krcma, M., Scafuri, U. and Tarantino, E., 2018. Genetic programming-based induction of a glucose-dynamics model for telemedicine. Journal of Network and Computer Applications, 119, pp.1-13.
  7.     Sannino, G., De Falco, I. and De Pietro, G., 2018. A continuous noninvasive arterial pressure (CNAP) approach for health 4.0 systems. IEEE Transactions on Industrial Informatics, 15(1), pp.498-506.
  8.     Sannino, G., De Falco, I. and De Pietro, G., 2017, May. Detection of falling events through windowing and automatic extraction of sets of rules: Preliminary results. In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) (pp. 661-666). IEEE.
  9.     Sannino, G., De Falco, I. and De Pietro, G., 2015. A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system. Applied Soft Computing, 34, pp.205-216.
  10. De Falco, I., De Pietro, G. and Sannino, G., 2015, August. On finding explicit rules for personalized forecasting of obstructive sleep apnea episodes. In 2015 IEEE International Conference on Information Reuse and Integration (pp. 326-333). IEEE.
  11. Sannino, G., De Falco, I. and De Pietro, G., 2014. Monitoring obstructive sleep apnea by means of a real-time mobile system based on the automatic extraction of sets of rules through differential evolution. Journal of biomedical informatics, 49, pp.84-100.
  12. Sannino, G., De Falco, I. and De Pietro, G., 2014. An automatic rules extraction approach to support osa events detection in an mhealth system. IEEE journal of biomedical and health informatics, 18(5), pp.1518-1524.
  13. De Falco, I., 2013. Differential evolution for automatic rule extraction from medical databases. Applied soft computing, 13(2), pp.1265-1283.
  14. De Falco, I., 2011, November. A differential evolution-based system supporting medical diagnosis through automatic knowledge extraction from databases. In 2011 IEEE International Conference on Bioinformatics and Biomedicine (pp. 321-326). IEEE.
  15. De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Discovering interesting classification rules with genetic programming. Applied Soft Computing, 1(4), pp.257-269.
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