Francesco Marcelloni - Dipartimento di Ingegneria dell`Informazione

Transcript

CURRICULUM
Francesco Marcelloni
CONTACT INFORMATION
Full Professor
University of Pisa
Dipartimento di Ingegneria dell’Informazione (DII)
Largo Lucio Lazzarino, 1
56122 PISA, Italy
Tel.: +39 050 2217 678 (599)
Fax: +39 050 2217 600
Email: [email protected]
EDUCATION
November 1996 Ph.D. in Computer Engineering, DII, Pisa.
Dissertation: Molecule-oriented models and fuzzy logic-based methods in
software development.
Advisor: Prof. Beatrice Lazzerini, DII, Pisa.
November 1991 Laurea Degree (Master of Science) in Electronics Engineering, University of
Pisa.
Advisor: Prof. Beatrice Lazzerini, DII, Pisa.
CAREER
Feb. 2016
Full Professor in the sector 09/H1 – Information processing systems,
DII, University of Pisa, Italy
Jan. 2015
National Scientific Qualification as Full Professor in the sector 01/B1 –
Informatics.
Jan. 2015
National Scientific Qualification as Full Professor in the sector 09/H1 –
Information processing systems.
May 2014 - present
Member of the Management Board of the Pisa University Press.
Jan. 2014
National Scientific Qualification as Full Professor in the sector 01/B1 –
Informatics.
Dec. 2013
National Scientific Qualification as Full Professor in the sector 09/H1 –
Information processing systems.
Sep. 2012-Jan. 2015
Member of the Academic Senate of the University of Pisa.
Nov. 2009-Dec.2012
Erasmus Coordinator for the Faculty of Engineering of the University
of Pisa, Italy.
Nov. 2009- Dec.2012 Responsible for the Summer Course organized by the Faculty of Engineering of the University of Pisa in cooperation with the University of
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San Diego (USA) and the University of Illinois at Urbana-Champaign
(USA). Each year, during the summer period, 30-40 Italian students attend a five-weeks class at the two universities in USA and 30-40 American students attend a five-weeks class at the University of Pisa.
Nov. 2009- Dec.2012 President of the International Relation Commission of the Faculty of
Engineering of the University of Pisa, Italy.
Mar. 2004-present
Member of the board of the PhD Course in Information Engineering.
Feb. 2004-Nov. 2010 Vice-president of the Specialized Laurea Degree course in “Computer
Engineering for Enterprise Management”.
Jan. 2002-Jan. 2015
Associate Professor in the sector 09/H1 – Information processing systems, DII, University of Pisa, Italy.
Nov. 1996-Dec. 2001 Assistant Professor of Computer Engineering, DII, University of Pisa,
Italy.
Jun. 1997-Dec. 1997
Visiting Researcher, Department of Computer Science, University of
Twente, The Netherlands.
Nov. 1994-Sep. 1995 Visiting Researcher, Department of Computer Science, University of
Twente, The Netherlands.
CURRENT AUTHOR-LEVEL METRICS
Google Scholar
Number of citations: 2613
H-index: 26
I10-idex: 67
Scopus
Number of citations: 1640
H-index: 22
AWARDS AND FELLOWSHIPS
1995
University of Twente, Research Fellowship.
1996
Italian National Research Council, Research Fellowship.
PROJECTS AND FUNDINGS
Research Projects (approximately 1,000,000 Euros) as Coordinator of the Project or Principal Investigator for the Department
Mar 2015 – Mar 2016
“Metodologie e Tecnologie per lo Sviluppo di Servizi Informatici Innovativi per le Smart Cities” (Methodologies and technologies to develop novel services for the smart cities) funded by the University of
Pisa in the framework of “Progetti di Ricerca di Ateneo - PRA 2015”
(Coordinator).
Dec 2012-Dec 2014
“SMArt Transport for sustainable citY”, funded by the Tuscany Re-
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gion in the framework of Bando Unico R&S – 2012 (Principal Investigator).
Nov 2012-Nov 2014
Research grant given by the Tuscany region in the framework of call
"POR FSE 2007- 2013, asse IV Capitale Umano" for supporting the
50% of the total amount of a two-years research fellowship (assegno
di ricerca) on "SOcial Sensing (SOS)" (Coordinator).
May 2012-Nov 2012
“Un approccio basato sui processi per la re-ingegnerizzazione di software gestionali” (A process-based method for enterprise software reengineering), funded by TeamSystem Srl, Via Gagarin 205, Pesaro
(PU) (Coordinator).
Sept 2011-Jan 2012
“Analisi di standard e strumenti per la modellazione dei processi di
business” (Analysis of standards and tools for business process modelling), funded by TeamSystem Srl, Via Gagarin 205, Pesaro (PU)
(Coordinator).
May 2011-May 2012
“E-tutor: A low-cost system to monitor the use of electrical energy in
buildings”, funded by Fondazione Cassa di Risparmio di Lucca,
Lucca, Italy (Coordinator).
Nov. 2010-2012
“A platform for manufacturing process traceability in the leather supply chain”, MANUNET (ERA-NET COORDINATION ACTION), 7°
Seventh Framework Programme (FP7), funded by Tuscany Region
(Scientific Coordinator).
Mar 2010–Sept 2011
“Bio Custom Shoes toward Therapeutic Technology”, funded by Tuscany region (Principal Investigator).
Aug 2009 - Sep 2009
“Sviluppo di un prototipo di un sistema di rilevazione dei tempi di
lavorazione nel settore moda” (Development of a protype for measuring processing time in the fashion sector), Confeelettronica s.r.l., via
del Parlamento Europeo n. 13, Badia a Settimo (FI) (Coordinator).
Jun 2009 - Sep 2009
“Sviluppo di un sistema per ridurre i tempi di raccolta dei rifiuti urbani
ed il numero di automezzi utilizzati” (Development of a system based
on vehicle routing algorithms for garbage collection optimization),
funded by ESA SYSTEM Srl, via Traversagna, 48, Pisa (PI) (Coordinator).
Dec. 2008–Dec. 2010
“DAFNE – Diagnosi automatica basata su tecniche di intelligenza
computazionale delle cause di perdita di efficienza energetica in impianti fotovoltaici” (Automatic diagnosis of the causes of efficiency
loss in photovoltaic energy systems based on computational intelligence techniques), funded by Tuscany region (Coordinator).
Jan. 2008–Oct. 2009
“TRA.S.P – Tracce sulla pelle (An analysis on the applicability of a
traceability system for guaranteeing the “Made in Italy” production),
funded by Tuscany Region (Principal Investigator).
Jan. 2008–Jan. 2010
“Analysis and design of mobile value-added services”, funded by
Softec S.p.A. Sesto Fiorentino, Florence (Coordinator).
Jan. 2008–Sep. 2008
“Analisi dell’applicazione di reti di sensori all’interno di ambienti industriali” (Analysis of the applicability of wireless sensor networks in
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industrial environment), funded by TD Group S.p.A., Migliarino Pisano (PI) (Coordinator).
Jul. 2007–Feb. 2008
“Studio e applicazione di reti di sensori all’interno di imbarcazioni da
diporto” (Analysis of the applicability of wireless sensor networks inside yachts), funded by Acheo s.r.l. (Marina di Carrara), Elettrotecnica
Bertani (Romagnano – Massa), Bienne s.n.c. (Avenza – Massa), and
Saci Automazioni s.r.l. (Massarosa - Lucca) (Coordinator).
Apr. 2007–Feb. 2008
“Inno.Pro.Moda - Innovazione, progettazione, qualità e tracciabilità
per il sistema moda” (Innovation, designing, quality and traceability
in the fashion supply chain), funded by Tuscany region (Principal Investigator).
Jun. 2007–Jul. 2008
“Sviluppo di algoritmi di computational intelligence per l’individuazione della profondità di fondali marini in prossimità delle coste”,
(Development of computational intelligence algorithms to estimate the
sea depth in the neighbourhood of the coasts), funded by Flyby s.r.l.
(Livorno, Italy) (Coordinator).
Feb. 2007–Dec. 2007
“Development of model-driven architectures”, funded by SAGO s.p.a.
(Firenze, Italy) (Coordinator).
Jun. 2006–Jun. 2007
“Sviluppo di algoritmi per ridurre il consumo energetico in reti di sensori” (Development of algorithms for power saving in wireless sensor
networks), funded by TD Group S.p.A., Migliarino Pisano (PI) (Coordinator).
Jan. 2006–Dec. 2006
“Group Opportunities Alliance – A network for technological transfer”, funded by Tuscany region (Principal Investigator).
Jun. 2005–Jun. 2006
“Analisi di strumenti per la modellizzazione dei processi” (Analysis of
process modelling tools), funded by TD Group S.p.A., Migliarino Pisano (PI) (Coordinator).
Jan. 2005-Dec. 2005
“Modelli di Integrazione tra sistemi informativi” (Models for Information System Integration), funded by Multiconsulting s.r.l. (Prato, Italy)
(Coordinator).
Feb. 2005-Sep. 2005
“Studio di una architettura basata su SAP per la gestione del magazzino utilizzando tag a radiofrequenza” (A SAP-based architetture for
warehouse management using RFID tags), funded by AIVE (Venice)
(Coordinator).
Jan. 2005-Dec. 2005
“Criteri e metodi di gestione della sicurezza relativa ai sistemi informatici clinico-sanitari” (Methods for managing information system security), funded by SAGO s.p.a. (Firenze, Italy) (Coordinator).
Jul. 2004–Feb. 2005
“Sviluppo di un prototipo su piattaforma SAP per la valutazione di
alcuni indicatori di una Supply Chain previsti dal modello SCOR”
(Development of a prototype on SAP platform for assessing supply
chain indicators of the SCOR model), funded by AIVE (Venice)
(Coordinator).
Jan. 2003–Sep. 2003
“Un sistema per la personalizzazione dei portali Web” (A fuzzy hierarchical approach to Web personalization), funded by Ksolutions
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Spa, S. Martino Ulmiano (Pisa) (Coordinator).
Sep. 2002 – Mar. 2003 “Un sistema per l’analisi dei segnali prodotti da sensori olfattivi posti
all’interno di forni domestici” (A system for the analysis of signals
produced by olfactive sensors placed in a cooking oven), funded by
Whirpool Europe s.r.l., Varese (Italy) (Coordinator).
Jan. 2002–Dec. 2002
“Un sistema per la determinazione dei profili degli utenti di un portale
web” (A fuzzy logic-based system for determining a set of profiles of
web portal users), funded by Italia OnLine S.p.A., Pisa (Italy) (Principal Investigator).
Mar. 2001- Mar. 2002
“Sviluppo di un sistema per la classificazione ed il riconoscimento di
vari oli di oliva da segnali prodotti da sensori olfattivi e per la riduzione del drift presente in tali sensori” (Development of a system for
olive oil classification and recognition from signals produced by olfactive sensors and for reducing drift of these sensors), funded by
I.S.E. Ingegneria dei Sistemi Elettronici s.r.l. (PISA) (Coordinator).
Research Projects as Participant
Nov. 2012–Nov. 2015
FP7-ICT-funded project PacMan “Probabilistic and Compositional
Representations of Objects for Robotic Manipulation”, Project reference: 600918.
Jul. 2011 – 30 Jun 2014 Project Politer (Polo di Innovazione Tecnologie dell’ICT, delle Telecomunicazioni e della Robotica) – funded by Tuscany region in the
framework of call POR CREO FESR 2007-2013 Action 2.1.
Nov. 2011–Nov. 2013
Smart Building: Un sistema di ambient intelligence per l’ottimizzazione delle risorse energetiche in complessi di edifici (Smart Building: An ambient intelligent system for energy consumption optimization in complexes of buildings)”, funded by Sicily Region in the
framework of call “Obiettivo 4.1.1.1 del POR FESR 2007-2013”
Nov. 2006–Nov. 2009
FIRB Project “Adaptive Infrastructure for Decentralized Organization
(ArtDecO)”, funded by the Italian Ministry of University and Research (MIUR).
May. 2007–Mar. 2008
“GeoMon – Monitoraggio delle opere ingegneristiche e prove geotecniche tramite l’utilizzo delle reti di sensori wireless” (Wireless sensor
networks for monitoring applications), funded by the Sicily region.
Jan. 2007–Dec. 2007
“VirGoal - Sperimentazione dei modelli di Virtual Enterprise e Virtual
Organisation tramite progetti pilota” (Models for Virtual Enterprise
and Virtual Organisation), funded by the Tuscany region.
Dec. 2004–Dec. 2006
“SENSORNET – Reti di sensori per il monitoraggio ambientale”
(Sensor networks for environmental monitoring), funded by Fondazione Cassa di Risparmio di PISA (Pisa).
Mar. 2004– Mar. 2006 “CERERE - Metodologie e strumenti per una Banca Dati telematica a
supporto della qualità e della sicurezza dei prodotti agro-alimentari”
(Methods and tools to develop an information system for supporting
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quality and safety of food), funded by Fondazione Cassa di Risparmio
di PISA (Pisa).
Jan. 2003–Dec. 2003
“OpenLab - Progetto per la costituzione di una rete toscana di partner
per la promozione e lo sviluppo di prodotti e servizi basati su software
Open Source” (A Tuscan network for promotion and development of
products and services based on open source software), funded by the
Tuscany region.
Mar. 2001–Mar. 2003
“Classificazione e analisi di immagini iperspettrali in sistemi di telerilevamento” (Analysis and classification of hyperspectral images in
remote sensing systems), funded by the Italian Ministry of University
and Scientific and Technological Research.
May 2000–Dec. 2001
“Metodologie e tecniche di progetto di sistemi distribuiti” (Methods
and tools for distributed system design), funded by the Italian Ministry
of University and Scientific and Technological Research.
Mar. 1997–Mar. 2000
Esprit Project No. 25254, “Development and evaluation of processing
techniques based on artificial neural networks and fuzzy logic for
knowledge data extraction – application to olfactive sensor data processing” (acronym INTESA).
Jan. 1998–Dec. 1999
“Metodologie e strumenti di progetto di sistemi ad alte prestazioni per
applicazioni distribuite (MOSAICO)” (Design methodologies and
tools for distributed applications in high performance systems),
funded by the Italian Ministry of University and Scientific and Technological Research.
Mar 1996–Jun. 1997
“Architetture parallele e algoritmi per reti neuronali e loro applicazioni” (Parallel architectures and neural network algorithms), funded
by the Italian National Research Council.
Jan. 1995–Dec. 1995
“Metodologie e strumenti di progetto per sistemi distribuiti e paralleli”
(Methods and Tools for designing parallel and distributed systems),
funded by the Italian Ministry of University and Scientific and Technological Research (MURST 40%).
Mar. 1993–Mar. 1995
“Sistemi esperti e loro applicazioni” (Expert systems and their applications), funded by the Italian Ministry of University and Scientific
and Technological Research (MURST 60%).
Mar. 1993–Mar. 1994
“Architetture convenzionali e non convenzionali per sistemi distribuiti” (Conventional and non-conventional architectures for distributed systems), funded by the Italian Ministry of University and Scientific and Technological Research (MURST 40%).
ADVISING – CO-SUPERVISED PH.D. STUDENTS
Jan. 2001–Dec. 2003
Marco Cococcioni, who worked at the DII of the University of Pisa on
new approaches to fuzzy modelling and multiple classifiers fusion.
Jan. 2004-Dec. 2006
Michela Antonelli, who worked at the DII of the University of Pisa on
segmentation and reconstruction of lung volumes in CT images for detecting lung cancers.
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Jan. 2004-Dec. 2006
Mario G.C.A. Cimino, who worked at the DII of the University of Pisa
on the development of systems for food traceability.
Jan. 2005-Dec. 2007
Alessio Botta, who worked at the Lucca IMT on context adaptation of
fuzzy systems.
Jan. 2006-Dec. 2008
Pietro Ducange, who worked at the DII of the University of Pisa on
genetic fuzzy systems.
Jan. 2006-Dec. 2008
Massimo Vecchio, who worked at the Lucca IMT on data aggregation
and data compression algorithms for wireless sensor networks.
Jan. 2008-Dec. 2010
Alessandro Ciaramella who worked at the Lucca IMT on context-aware
approaches to service recommender for mobile devices.
ADVISING - SUPERVISED PH.D. STUDENTS
Nov. 2012-present
Armando Segatori, who is working at the DII of the University of Pisa
on new approaches to data mining on cloud computing.
SUPERVISED POST-DOCS (ASSEGNO DI RICERCA)
Apr 2008 - Apr 2012: Mario G.C.A. Cimino, who worked at the DII on context-aware service
recommenders
Feb 2009 – May 2014: Pietro Ducange, who worked at the DII on multi-objective evolutionary
fuzzy systems
CO-SUPERVISED POST-DOCS (ASSEGNO DI RICERCA)
Oct 2009 – Oct 2013: Michela Antonelli, who worked at the DII on multi-objective evolutionary fuzzy systems
May 2013 – May 2015 Eleonora D’Andrea who worked at the Centro Piaggio of the University
of Pisa on traffic and incident detection by using tweets and GPS traces.
July 2015-present
Francesco Pistolesi who is working at the DII on multi-objective optimization in smart micro-grid.
MANAGING AND ORGANIZATION ACTIVITY
2008
founds the Competence Centre on Mobile Value Added Services, supported by Softec s.p.a., at the Dipartimento di Ingegneria dell’Informazione of the University of Pisa. Currently, Francesco Marcelloni is the
head of the Centre.
2004-2006
organises and manages the course (60 credits) entitled “Design and
Management of Decision Support Systems”, funded by Tuscany region
(75,000 Euros).
2004-2006
Management of Enterprise Information Systems and e-Commerce Systems”, funded by Tuscany region (75,000 Euros).
2003-2005
Management of Enterprise Information Systems”, funded by Tuscany
region (68,438 Euros).
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2003
co-founds with Prof. Beatrice Lazzerini and Prof. Roberto Chiavaccini
the laboratory of Enterprise Information Systems at the Dipartimento di
Ingegneria dell’Informazione of the University of Pisa.
2002
co-founds with Prof. Beatrice Lazzerini the Computational Intelligence
Group at the Dipartimento di Ingegneria dell’Informazione of the University of Pisa.
WORKSHOP AND SPECIAL SESSION ORGANIZATIONS
22 Nov. 2011
Co-organization of the Workshop “Computational Intelligence for Personalization in Web Content and Service Delivery”, Cordoba, Spain.
30 Nov. 2010
Co-organization of the Workshop “Computational Intelligence for Personalization in Web Content and Service Delivery”, Cairo, Egypt.
29 Nov. 2009
Co-organization of the special session “Designing comprehensible intelligent systems”, within ISDA’09, Pisa, Italia.
04 Sept. 2003
Co-organization of the special session “Industrial applications of soft
computing”, Oxford, Great Britain, within the KES’2003 Conference.
17 Sept. 2002
Co-organization of the special session “Data clustering based on nonmetric distance measures”, Crema, Italia, within the KES’2002 Conference.
20 July 1998
Co-organization of the workshop “Automating the object-oriented software development”, Brussels, Belgium, within the ECOOP’98 Conference.
9 June 1997
Co-organization of the workshop “Modelling software processes and
artifacts”, Jyväskulä, Finland within the ECOOP’97 Conference.
KEYNOTE SPEAKER
“Multi-objective Evolutionary Learning of Fuzzy Rule-based Systems for Regression
Problems”, 5th IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems, April 15, 2011, Paris.
“Multi-Objective Evolutionary Fuzzy Systems”, Third International Conference of Soft
Computing and Pattern Recognition (SoCPaR 2011), Dalian, China, October 14-16, 2011.
TUTORIALS
“Multi-Objective Evolutionary Fuzzy Systems”, 9th International Workshop on Fuzzy
Logic and Applications, August 29-31, 2011, Trani, Italy.
INVITED SPEAKER
“Traceability for Fashion Supply Chain”, Workshop “Made in Italy: Tracciabilità, qualità
del prodotto ed etica”, C.R.E.D, (Centro Risorse Educative e Didattiche), Scandicci, Florence, 10 ottobre 2007.
“Compression Algorithms for Wireless Sensor Networks”, First Workshop on Wireless
Sensor Networks for Real Life Applications, Palermo, May 5-6, 2008.
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“Assicurare trasparenza a produttori e consumatori” (On ensuring transparency to producers and consumers), Workshop “TRA.S.P. – TRAcce Sulla Pelle”, I-Place, Scandicci,
Florence, June 29, 2009.
“Perché una piattaforma come TRASP 2.0?” (Why a platform as TRASP 2.0?), Workshop
“Tracciabilità di Filiera, tradizione del saper fare e innovazione tecnologica per una nuova
competitività del Made in Italy", Florence, January 15, 2013.
“The Smarty Project”, Smart City Workshop, SMAU, Milan, October 23, 2013.
“Smart Transport and Smart Buildings for Sustainable City”, Workshop on Smart Semantic Cities, AI*IA 2014, December 12, 2014.
STEERING COMMITTEE MEMBER
Francesco Marcelloni is member of the steering Committee of the ISDA (Intelligent Systems Design and Applications) Conferences.
IEEE TASK FORCE MEMBER
Jan 2010-present. Member of the Task Force on Evolutionary Fuzzy Systems of the IEEE
Computational Intelligence Society.
INTERNATIONAL CONFERENCE CHAIR
Francesco Marcelloni has served as TPC co-chair of “The 9th International Conference
on Intelligent Systems Design and Applications”, Pisa, Nov. 30-Dec 2, 2009, sponsored
by IEEE Systems, Man and Cybernetics Society, IFSA, EUSFLAT, ENNS.
Francesco Marcelloni has served as General co-chair of “The 10th International Conference on Intelligent Systems Design and Applications”, Cairo (Egypt), Nov. 29-Dec 1,
2010, sponsored by IEEE Systems, Man and Cybernetics Society, IFSA, EUSFLAT,
ENNS.
Francesco Marcelloni has served as TPC co-chair of “The 11th International Conference
on Intelligent Systems Design and Applications”, Cordoba (Spain), Nov. 22-Nov 24,
2011, sponsored by IEEE Systems, Man and Cybernetics Society, IFSA, EUSFLAT,
ENNS.
Francesco Marcelloni has served as TPC chair of the 8th IEEE International Workshop on
Sensor Networks and Systems for Pervasive Computing, Lugano, Switzerland, March 1923, 2012.
Francesco Marcelloni serves as Tutorial chair of the 31st ACM/SIGAPP Symposium on
Applied Computing, Pisa (Italy), April 4-8, 2016.
ASSOCIATE EDITOR OF INTERNATIONAL JOURNALS
Francesco Marcelloni serves as associate editor of:
•
International Journal of Swarm Intelligence and Evolutionary Computation (OMICS
Publishing Group) since 2010.
•
International Journal of Sensor Networks and Data Communications (OMICS Publishing Group) since 2010.
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•
Information Sciences (Elsevier) since 2011.
•
Soft Computing (Springer) since 2013.
•
International Journal of Neural Networks and Advanced Applications, North Atlantic
University Union (NAUN) since 2013.
EDITORIAL BOARD OF INTERNATIONAL JOURNALS
Francesco Marcelloni serves in the Editorial Board of
•
International Journal On Advances in Software (IARIA) (Editorial Board) since 2012
ADVISORY BOARD MEMBER
Francesco Marcelloni serves as Advisory Board Member of the
•
International Journal of Computer Information Systems and Industrial Management
Applications since 2012.
EDITOR OF SPECIAL ISSUES
F. Herrera, F. Marcelloni, V. Loia, Special Issue on Intelligent Systems Design and Applications (ISDA 2009), International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, World Scientific, Vol 18, N. 4, 2010.
José Manuel Benítez, Vincenzo Loia, Francesco Marcelloni, Special Issue on Advances
in Intelligent Systems, International Journal of Hybrid Intelligent Systems, IOS Press, vol.
7, N. 4, 2010.
K. J. Cios, C. Romero, J.M. Benitez, F. Marcelloni, Special Issue on Intelligent Systems
Design and Applications (ISDA 2011), Integrated Computer-Aided Engineering, IOS
Press, vol. 20, N. 3, 2013, pp. 199.
F. Marcelloni, D. Puccinelli, A. Vecchio, Special Issue on “Sensing and Mobility in Pervasive Computing”, Journal of Ambient Intelligence and Humanized Computing,
Springer, to be published.
INTERNATIONAL PROGRAMME COMMITTEE MEMBER (SINCE 2007)
The 4th International Conference on Ubiquitous Intelligence and Computing (UIC-07), in
Cooperation with the IEEE Computer Society, Hong Kong, China, July 11-13, 2007.
The 2007 IEEE Congress on Evolutionary Computation (CEC), Singapore, September
25-28, 2007.
The Fourth IEEE International Workshop on Sensor Networks and Systems for Pervasive
Computing, Hong Kong, Asia's World City, March 17-21, 2008.
The Fifth IEEE International Workshop on Sensor Networks and Systems for Pervasive
Computing, Dallas, March 9-13, 2009.
The 19th International Conference on Artificial Neural Networks, Limassol, Cyprus, September 14-17, 2009.
The New Advances on Genetic Fuzzy Systems track of IFSA2009/EUSFLAT09 Conference, July 20-24, Lisbon, Portugal, 2009.
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The 4th International Workshop on Genetic and Evolutionary Fuzzy Systems, March 17
– 19, Mieres, Asturias (Spain), 2010.
The 23rd International Conference on Industrial, Engineering & Other Applications of
Applied Intelligent Systems, IEA/AIE 2010, June 1-4, Córdoba (Spain).
The 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, June 28 - July 2, 2010, Dortmund (Germany).
The 20th International Conference on Artificial Neural Networks (ICANN 2010), Thessaloniki, Greece, September 15-18, 2010.
The International Conference on Fuzzy Computation, Valencia, Spain, October 24-26,
2010.
The Third International Workshop on Wireless Sensor Networks, Paris, February 10,
2011.
The 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, Paris, April
11 – 15, 2011.
The International Conference on Soft Computing Models in Industrial and Environmental
Applications, Salamanca (Spain), April 6-8, 2011.
The 2011 International Symposium on Neural Networks, Guilin, China, May 29-June 1,
2011.
Workshop on Parallel Evolutionary Computation (PEC 2011), Istanbul, Turkey, July 4 –
8, 2011.
The First International Conference on Advances in Information Mining and Management
(IMMM 2011), Bournemouth, UK, July 17-22, 2011.
The 12th International Conference on Engineering Applications of Neural Networks
(EANN), Corfu, Greece, September 15 – 18, 2011.
The 7th International Conference on Artificial Intelligence Applications and Innovations
(AIAI 2011), Corfu, Greece, September 15 – 18, 2011.
The 9th International Workshop on Fuzzy Logic and Applications, Trani, Italy, August
29-31, 2011.
The 1st Annual Congress of u-World, “New Dimension of the Smart Planet in U-Era”,
Dalian, China, October 23-25, 2011.
The International Conference on Fuzzy Computation Theory and Applications, Paris,
France, October 24-26, 2011.
The World Congress on Information and Communication Technologies, Mumbai, India,
December 11-14, 2011.
The International Conference on Soft Computing for Problem Solving (SocProS 2011),
Roorkee, India, December 16-18, 2011.
The 13th Engineering Applications of Neural Networks Conference (EANN 2012), London, UK, September, 2012.
The 8th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2012), Peninsula of Chalkidiki, Greece, September 27 – 30, 2012.
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The 4th International Workshop on Wireless Sensor Networks Architectures, Deployments and Trends, Istanbul, Turkey, May 7-10, 2012.
The Second International Conference on Advances in Information Mining and Management (IMMM 2012), Venice, Italy, October 21-26, 2012.
The 7th International Conference on “Bio-Inspired Computing: Theories and Application,
(BIC-TA 2012)” Gwalior (India), December 14 - 16, 2012.
The 3rd International Conference on Innovations in Bio-Inspired Computing and Applications, Kaohsiung, Taiwan, September 19-21, 2012 (member of the Advisory Board).
The 12th International Conference on Hybrid Intelligent Systems (HIS'12), Pune, India,
December, 4 – 7, 2012 (member of the International Advisory Board).
The 4th World Congress on Nature and Bioinspired Computing (NaBIC 2012), Mexico
city, Mexico, November 5-9, 2012 (member of the International Advisory Board).
The Second International Conference on “Soft Computing for Problem Solving, 2012
(SocProS - 2012)”, JK Lakshmipat University, Jaipur, December 28 - 30, 2012.
The 6th IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems
(GEFS 2013), Singapore, April 15-19, 2013 (part of the Symposium Series on Computational Intelligence sponsored by the IEEE Computational Intelligence Society (IEEE
SSCI 2013)).
The 26th International Conference on Industrial, Engineering & Other Applications of
Applied Intelligent Systems, IEA/AIE 2013, Amsterdam (The Netherlands), June 17-21,
2013.
The Third International Conference on Advances in Information Mining and Management, IMMM 2013, Lisbon, Portugal, 17-22 November, 2013.
The 2013 IFSA World Congress and the NAFIPS Annual Meeting, Edmonton, Canada,
June 24-28, 2013.
The 14th International Conference on Engineering Applications of Neural Networks
(EANN 2013), Sithonia, Greece, September 19-22, 2013.
The 9th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2013), Paphos, Cyprus, September 26-28, 2013.
The 5th International Conference on Fuzzy Computation Theory and Applications, Algarve, Portugal, 20-22 September, 2013.
The 8th Conference of the European Society for Fuzzy Logic and Technology
(EUSFLAT-2013), Milan, Italy, 11-13 September 2013.
The 15th Conference of the Spanish Association for Artificial Intelligence, Madrid, Spain,
17-20 September, 2013.
DIDAMATICA 2013, Pisa, Italy, 7-9 May, 2013.
The 3rd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT
2013), Palermo, Italy, October 30-31, 2013.
The 10th International Workshop on Fuzzy Logic and Applications, Genova, Italy, November 19-22, 2013.
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The 8th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO13), Salamanca, Spain, September 11/13, 2013.
The 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, 10-13
September 2013.
The 13th International Conference on Hybrid Intelligent Systems (HIS ’13) (advisory
board), Tunis, Tunisia, 4 - 6 December, 2013.
The 5th International Conference on Soft Computing and Pattern Recognition 2013
(SoCPaR'13), Hanoi, Vietnam, 15 - 18 December, 2013.
The 27th International Conference on Industrial, Engineering and other Applications of
Applied Intelligent Systems (IEA/AIE-2014), Kaohsiung, Taiwan, June 3-6, 2014.
The “International Conference on Physiological Computing Systems” - PhyCS 2014, Lisbon, Portugal, 7-9 January, 2014.
The 3rd International Conference on Pattern Recognition Applications and Methods, Angers, Loire Valley, France, 6-8 March 2014.
The 5th International Workshop on Wireless Sensor Networks Architectures, Deployments and Trends, Dubai, UAE, March 30 – April 2, 2014.
The Fourth International Conference on Advances in Information Mining and Management, IMMM 2014, Paris, France, 20-24 July, 2014.
The 6th International Conference on Fuzzy Computation Theory and Applications, Rome,
Italy, 16-18 October, 2014.
The 15th Engineering Applications of Neural Networks Conference (EANN 2014), Sofia,
Bulgaria, 2014.
The 10th IFIP AIAI (Artificial Intelligence Applications and Innovations) Conference,
Rhodes Island, Greece, 19-22 September, 2014.
The 3rd workshop on Techniques and Applications for Mobile Commerce, Federated
Conference on Computer Science and Information Systems (FedCSIS), Warsaw, Poland,
September 7-10, 2014.
The 24th International Conference on Artificial Neural Networks, Hamburg, Germany,
15-19 September, 2014.
The 9th International Conference on Bio-inspired Computing: Theories and Applications
(BIC-TA 2014), Wuhan, China, October 16–19, 2014.
The 9th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 14), Bilbao, 25-27 June, 2014.
The First Euro-China Conference on Intelligent Data Analysis and Applications (ECC2014), Shenzhen, China, 13-15 June, 2014.
The 14th International Conference on Hybrid Intelligent Systems (HIS ’14) (advisory
board), Kuwait, 14 - 16 December, 2014.
The 4th International Conference on Pattern Recognition Applications and Methods, Lisbon, Portugal, 10-12 January, 2015.
The Fifth International Conference on Advances in Information Mining and Management,
IMMM 2015, Brussels, Belgium, 21-26 June, 2015.
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The Seventh International Conference on Future Computational Technologies and Applications, IARIA, Nice, France, March 22-27, 2015.
The Seventh International Conference on Advanced Cognitive Technologies and Applications, IARIA, Nice, France, March 22-27, 2015.
The 16th International Conference on Engineering Applications of Neural Networks,
Rhodes Island, Greece, September 2015.
The 4th IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT
2015), Madrid, Spain, April 14-15, 2015.
The 11th International Conference on Artificial Intelligence Applications and Innovations
(AIAI 2015), Bayonne/Biarritz, France, September 14-17, 2015.
The 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2015), Burgos, Spain, June 15-17, 2015.
International Conference on Image Analysis and Recognition, ICIAR 2015, Niagara Falls,
Canada, October 22-24, 2015.
International Conference of the North American Fuzzy Information Processing Society,
NAFIPS 2015, Redmond, WA, August 17 – 19, 2015.
The 7th Computer Science and Electronic Engineering Conference, CEEC 2015, University of Essex, Colchester (UK), September 24-27, 2015.
The 10th Bio-inspired Computing: Theories and Applications (BIC-TA 2015) Conference, Hefei, China, September 25-28, 2015.
The 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC2015),
Systems Science & Engineering track, Hong Kong, October 9-12, 2015.
The 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Istanbul,
Turkey, August 2-5, 2015.
The 7th International Conference on Nature and Biologically Inspired Computing 2015
(NaBIC’15), Pietermaritzburg, South Africa (December 1-3, 2015) (advisory board)
The 7th International Conference on Computational Aspects of Social Network 2015
(CASoN’15), Pietermaritzburg, South Africa (December 1-3, 2015) (advisory board)
The 3rd International Conference on Physiological Computing Systems (PhyCS 2016),
Lisbon, Portugal, 29-31 July, 2016.
The 2nd IEEE International Conference on Smart Computing (SMARTCOMP) Conference, St. Louis, Missouri, USA, 18-20 May, 2016.
The Eighth International Conference on Advanced Cognitive Technologies and Applications, COGNITIVE 2016, March 20 - 24, 2016 - Rome, Italy.
The Eighth International Conference on Future Computational Technologies and Applications, FUTURE COMPUTING 2016, March 20 - 24, 2016 - Rome, Italy
The Second International Symposium on Intelligent Systems Technologies and Applications (ISTA’16), September 21-24, 2016, Jaipur, India.
The International Conference on Image Analysis and Recognition, ICIAR 2016, July 1315, 2016 – Póvoa de Varzim, Portugal.
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The30th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE-2017) Conference, 2017, Arras, France.
REVIEWING ACTIVITY
International Journals
IEEE Transactions on Evolutionary Computation,
IEEE Transactions on Fuzzy Systems,
IEEE Transactions on Systems, Man, and Cybernetics (Parts B and C),
IEEE Transactions on Neural Networks,
IEEE Transactions on Knowledge and Data Engineering,
IEEE Transactions on Parallel and Distributed Systems,
ACM Transactions on Autonomous and Adaptive Systems,
ACM Computing Surveys,
IEEE Transactions on Industrial Informatics,
Pattern Recognition,
International Journal of Approximate Reasoning,
Fuzzy Sets and Systems,
Information Sciences,
International Journal of Neural Systems,
Knowledge-Based Systems
Soft Computing,
Computer Communications,
Pervasive and Mobile Computing,
Wireless Communications and Mobile Computing,
IEEE Transactions on Software Engineering,
IEE Electronics Letters,
Knowledge and Information Systems,
Pattern Recognition Letters,
Journal of Information Fusion,
Computers & Industrial Engineering: An international journal,
Chemical Engineering and Processing,
Sensors
International Projects
In 2002 and 2003, Francesco Marcelloni has served as referee for projects submitted to
the Council of Physical Sciences of the Netherlands Organization for Scientific Research
(NWO).
In 2011, Francesco Marcelloni has been member of the External Expert Panel (EEP)
for the COST (European Cooperation in Science and Technology) Trans-Domain Proposals (TDP), Call 2011-1.
National Projects
In 2011, Francesco Marcelloni has served as referee for projects submitted to the University of Trieste in the framework of the call “Young Researcher Support for Scientific Research”.
From October 2011 Francesco Marcelloni is in the pool of experts commissioned by Filas,
Page 15 of 55
Società Finanziaria Laziale di Sviluppo (currently Lazio Innova), to assess projects submitted to the Lazio Region in the framework of the calls “Progetti di innovazione delle
micro e piccole imprese”, “Sostegno agli spin-off da ricerca” e “Progetti di R&S in collaborazione da parte delle PMI del Lazio”.
From November 2011 to October 2012, Francesco Marcelloni has been a member of the
panel commissioned by MIUR to assess projects submitted in the framework of the call
“Programma Operativo Nazionale ‘Ricerca e Competitività’ (R&C) 2007-2013, Avviso
D.D. 713/Ric. del 29 Ottobre 2010 – Asse I – Sostegno ai mutamenti strutturali – Distretti
ad alta tecnologia e relative reti e Laboratori pubblico/private e relative reti”. In particular,
he has been the coordinator of the technical assessment of three projects.
From January 2013, he is acting as technical supervisor of the three projects.
In March 2013, Francesco Marcelloni has served as referee for projects submitted to the
MIUR in the framework of the PRIN programme.
From November 2015 serves as referee for projects submitted to the Puglia Region.
PHD COMMITTEE
9 Feb 2006
one of the three members of the committee for the final examination of 4
PhD students of the “Dottorato di Ricerca in Ingegneria Informatica Multimedialità e Telecomunicazioni, XVIII ciclo”, University of Florence, Italy.
25 May 2007
PhD students of the “Dottorato di Ricerca in Ingegneria dell’Informazione,
XIX ciclo”, University of Pisa, Italy.
Jun 2008
one of the five members of the international committee for the final examination of the PhD student José Luis Aznarte Mellado, University of Granada, Spain.
29 May 2009
PhD students of the “Dottorato di Ricerca in Ingegneria dell’Informazione,
XXI ciclo”, University of Pisa, Italy.
28 Feb 2012
PhD students of the “Dottorato di Ricerca in Informatica, X ciclo”, University of Salerno, Italy.
24 Mar 2014
PhD students of the “Dottorato di Ricerca in Ingegneria Informatica, Multimedialità e Telecomunicazioni, XXV ciclo”, University of Florence, Italy.
24 Mar 2014
PhD students of the “Dottorato di Ricerca in Informatica, Sistemi e Telecomunicazioni, indirizzo in Ingegneria Informatica, Multimedialità e Telecomunicazioni, XXVI ciclo”, University of Florence, Italy.
26 May 2014
PhD students of the “Dottorato di Ricerca in Informatica, XXVI ciclo”,
Page 16 of 55
University of Bari, Italy.
INTERNATIONAL RESEARCH COLLABORATIONS
Francesco Marcelloni has collaborated with several researchers of other universities in the
world. In particular, the collaborations with Prof. Francisco Herrera (University of Granada,
Spain), Prof. Witold Pedrycz (University of Alberta, Canada), Prof. Hani Hagras (Universiy of
Essex, UK), Prof. Mehmet Aksit (University of Twente, The Netherlands), prof. Trevor Martin
(University of Bristol, United Kingdom), prof. Dan Stefanescu (Suffolk University, United
States) and prof. Dumitru Dumitrescu (Babes-Bolyai University, Cluj-Napoca, Romania) are
testified by a number of joint papers.
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TEACHING ACTIVITIES
UNIVERSITY OF PISA
2015-2016
Web Design (6 credits, Corso di Laurea in Ingegneria Informatica), Department
of Information Engineering, University of Pisa.
Business Intelligence (9 credits, Corso di Laurea Magistrale in Computer Engineering), Department of Information Engineering, University of Pisa.
Bioinspired computational methods: Biological data mining (6 credits, Corso
di Laurea Magistrale in Bionics Engineering) Department of Information Engineering, University of Pisa.
Systems and Technologies for Ambient Intelligence (3 credits, Corso di Laurea
Magistrale in Computer Engineering), Department of Information Engineering,
University of Pisa.
2014-2015
University of Pisa.
2013-2014
University of Pisa.
Data Mining for Smart Cities (3 credits, Master in Smart Cities), Department
2012-2013
Business Intelligence (6 credits, Corso di Laurea Magistrale in Ingegneria Informatica per la Gestione d’Azienda), Department of Information Engineering,
University of Pisa.
University of Pisa.
2011-2012
Web Design (6 credits, Corso di Laurea in Ingegneria Informatica), Faculty of
Engineering, University of Pisa.
Business Intelligence (6 credits, Corso di Laurea Magistrale in Ingegneria Informatica per la Gestione d’Azienda), Faculty of Engineering, University of
Pisa.
2010-2011
Web Design (6 credits, Corso di Laurea in Ingegneria Informatica), Faculty of
Engineering, University of Pisa.
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Computer Architectures (12 credits, Corso di Laurea in Ingegneria delle Telecomunicazioni), Faculty of Engineering, University of Pisa.
Multi-objective Evolutionary Fuzzy Rule-based Systems (2 credits, Master di
secondo livello in Elettroacustica Subacquea e Sue Applicazioni), Faculty of
Engineering, University of Pisa
2009-2010
Fundamentals of Computer Science (12 credits, Corso di Laurea in Ingegneria
Informatica), Faculty of Engineering, University of Pisa.
Information Systems (5 credits, Corso di Laurea Specialistica in Ingegneria Informatica per la Gestione d’Azienda), Faculty of Engineering, University of
Pisa.
2008-2009
Pisa.
Information Systems for Tourism (1 credit, Corso di Laurea Specialistica in
Progettazione e Gestione dei Sistemi Turistici Mediterranei), Campus Lucca,
Lucca.
Object-oriented development methods: theory and application, Sago S.p.A.
(Florence).
2007-2008
Pisa.
Lucca.
Neural Networks (2 credits, Master di secondo livello in Elettroacustica Subacquea e Sue Applicazioni), Faculty of Engineering, University of Pisa.
2006-2007
Pisa.
Lucca.
2005-2006
Pisa.
Fundamentals of Computer Architecture (3 credits, Corso di Laurea in
Page 19 of 55
Ingegneria Informatica), Faculty of Engineering, University of Pisa.
2004-2005
Elettronica), Faculty of Engineering, University of Pisa.
Ingegneria Elettronica), Faculty of Engineering, University of Pisa.
Computer Architectures (12 credits, Corso di Laurea in Ingegneria Informatica), Faculty of Engineering, University of Pisa.
2003-2004
Biomedica), Faculty of Engineering, University of Pisa.
Intelligent Decision Support Systems (10 credits, Corso di Laurea in Ingegneria
Gestionale), Faculty of Engineering, University of Pisa.
2002-2003
Biomedica), Faculty of Engineering, University of Pisa.
2001-2002
Computer Architectures (12 credits, Corsi di Laurea in Ingegneria Elettronica
ed Ingegneria delle Telecomunicazioni), Faculty of Engineering, University of
Pisa.
2000-2001
Fundamentals of Computer Architecture (6 credits, Corsi di Laurea in Ingegneria Elettronica ed Ingegneria Informatica), Faculty of Engineering, University
of Pisa.
Computer Architectures (12 credits, Corso di Laurea in Ingegneria delle Telecomunicazioni), Faculty of Engineering, University of Pisa.
1999-2000
Computer Architectures (4 credits, Corso di Laurea in Ingegneria Informatica),
Faculty of Engineering, University of Pisa.
1998-1999
Knowledge Engineering and Expert Systems (4 credits, Corso di Laurea in
Ingegneria Informatica), Faculty of Engineering, University of Pisa.
1997-1998
1996-1997
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During his stay at the University of Pisa, Francesco Marcelloni has supervised more than 100
Master theses.
OTHER UNIVERSITIES
3-7 Jun 2012
Short course (10 hours) on "Designing Fuzzy Rule-Based Systems: from heuristic approaches to multi-objective evolutionary fuzzy systems" in the
MÁSTER UNIVERSITARIO EN INVESTIGACIÓN EN INGENIERÍA EN
PROCESOS Y SISTEMAS at the UNIVERSIDAD DE VALLADOLID,
funded by the Spanish government in the framework of SUBVENCIONES
PARA LA MOVILIDAD DE PROFESORES VISITANTES EN MÁSTERES
OFICIALES CURSO ACADÉMICO 2011/2012
21-22 Sep 2013 Short course (8 hours) on "Designing Fuzzy Rule-Based Systems: from heuristic approaches to multi-objective evolutionary fuzzy systems" for PhD students
at the University of Essex (UK).
Page 21 of 55
RESEARCH ACTIVITY
Francesco Marcelloni’s research activity has focused on the following main fields:
•
computational intelligence and its engineering applications;
•
software development methods;
•
lot and process traceability infrastructures;
•
service recommenders;
•
data aggregation, data compression and node localization in wireless sensor networks;
•
expert systems;
•
medical image processing;
•
services for smart cities;
•
data mining algorithms for big data.
In the following, the main scientific results achieved in these fields are described in detail, referring to the papers where the results have been shown.
Computational Intelligence and its Engineering Applications
The research activity has focused on both some theoretical aspects of fuzzy expert systems,
neural networks, single-objective and multi-objective genetic fuzzy systems, fuzzy classifiers,
and on engineering applications of neural networks, fuzzy logic and evolutionary algorithms.
Fuzzy expert systems
A method for saving computation time in fuzzy expert systems composed of MISO (Multi-Input
Single-Output) rules has been proposed [J7] [C10]. The method replaces each MISO fuzzy rule
by an equivalent collection of Single-Input Single-Output (SISO) rules. The conclusion inferred
from a MISO rule can be computed as either the union or intersection of the conclusions inferred
from the equivalent SISO rules depending on whether the fuzzy implication operator is non-increasing or non-decreasing with respect to its first argument. This is of the utmost importance in
fuzzy reasoning applied to fuzzy systems with several MISO rules.
Further, fuzzy implication operators, which are extensions of the two-valued logic implication
operator and are non-decreasing with respect to their second argument, generic Sup-T composition operators, and minimum as aggregation operator have been carefully analysed. As regards
approximate reasoning with multiple rules, it has been proved that, if the fundamental requirement
for fuzzy reasoning is satisfied, then the fuzzy sets which partition the input and output universes
have to meet appropriate constraints. Finally, a sufficient condition defined on input fuzzy sets to
obtain a reasonable inference result has been provided [J6] [C8] [C9].
Finally, in [C64] an approach to complexity reduction of Mamdani-type Fuzzy Rule-Based Systems (FRBSs) based on removing logical redundancies has been proposed. First an FRBS is generated from data by applying a simplified version of the well-known Wang and Mendel method.
Then, the FRBS is represented as a multi-valued logic relation. Finally, the MVSIS, a tool for
circuit minimization and simulation, is applied to minimize the relation and consequently to reduce complexity of the associated FRBS. Unlike similar previous approaches proposed in the
literature, the use of MVSIS allows dealing with nondeterminism, that is, allows managing rules
with the same antecedent but different consequents. To allow nondeterminism guarantees to
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achieve a higher (or at least not lower) complexity reduction than the one achievable from removing nondeterminism as soon as it appears.
New neural network architectures
Three novel neural network architectures have been proposed. The first architecture exploits the
concept of receptive field and, in contrast to “standard” radial basis function (RBF) neural networks, offers a considerable level of flexibility as the resulting receptive fields are highly diversified and capable of adjusting themselves to the characteristics of the locally available experimental data [J40]. A design strategy of the novel architecture has been proposed. The strategy
comprises three basic phases and exploits the modelling capabilities of the contributing referential
multilayer perceptrons (RMLPs) that play a role of generalized receptive fields. In the first phase,
a “blueprint” of the network is formed by employing a specialized version of the commonly encountered Fuzzy C-Means (FCM) clustering algorithm, namely the Conditional (context-based)
FCM. In this phase the intent is to generate a collection of information granules (fuzzy sets) in
the space of input and output variables, narrowed down to some certain contexts. In the second
phase, based upon a global view at the structure, the input-output relationships are refined by
engaging a collection of RMLPs where each RMLP is trained by using the subset of data associated with the corresponding context fuzzy set. During training, each receptive field focuses on the
characteristics of these locally available data and builds a nonlinear mapping in a referential mode.
Finally, the connections of the receptive fields are optimized through global minimization of the
linear aggregation unit located at the output layer of the overall architecture.
The second architecture is based on the Morphogenetic Theory (MT) [J41]. Given a context H
defined by a set of M objects, each described by a set of N attributes, and a vector X of desired
outputs for each object, MT combines notions from formal concept analysis and tensor calculus
so as to generate a morphogenetic system (MS). The MS is defined by a set of weights s1, …, sN,
one for each attribute. Given H and X, weights are computed so as to generate the projection Y of
X on the space of the attributes with the minimum distance between Y and X. An MS can be
represented as a neuron, morphogenetic neuron (MN), with a number of synapses equal to the
number of attributes and synaptic weights equal to s1, …, sN. Unlike traditional neural network
paradigm, which adopts an iterative process to determine synaptic weights, in MT weights are
computed at once. A method to generate a morphogenetic neural network (MNN) for identification problems has been proposed. The method is based on extending appropriately and iteratively
the attribute space so as to reduce the error between desired output and computed output. By using
four well-known datasets, we show that an MNN can identify an unknown system with a precision
comparable to classical multi-layer perceptron with complexity similar to the MNN, but reducing
drastically the time needed to generate the neural network. Further, the structure of the MNN is
generated automatically by the method and does not require a trial-and-error approach often applied in classical neural networks.
The third architecture is a variant of the standard MLP aimed at managing interval-valued data
[C79]. The proposed MLP has interval-valued weights and biases, and is trained through a genetic
algorithm purposely designed to fit data with different levels of granularity. The modelling capabilities of the proposed MLP are illustrated by means of its application to both synthetic and real
datasets [J64].
Multi-objective evolutionary algorithms for fuzzy rule-based system generation
In the last years, the numerous successful applications of FRBSs to several different domains
have produced a considerable interest in methods to generate FRBSs from data. Most of the methods proposed in the literature, however, focus on performance maximization and omit to consider
Page 23 of 55
FRBS interpretability. Only recently, the problem of finding the right trade-off between performance and interpretability, in spite of the original nature of fuzzy logic, has arisen a growing
interest in methods which take both the aspects into account [C80][BC11]. In this context, a Pareto-based multi-objective evolutionary approach to generate a set of both Mamdani [C48] and
Takagi-Sugeno [C49] FRBSs from numerical data has been proposed. In particular, a variant,
denoted (2+2)M-PAES, of the well-known (2+2) Pareto Archived Evolutionary Strategy has been
introduced [J33]. This variant adopts the one-point crossover and two appropriately defined mutation operators, and determines an approximation of the optimal Pareto front by concurrently
minimizing the root mean squared error and the complexity. Complexity is measured as sum of
the conditions which compose the antecedents of the rules included in the FRBS. Thus, low values
of complexity correspond to Mamdani FRBSs characterized by a low number of rules and a low
number of input variables really used in each rule. This ensures a high comprehensibility of the
systems. The (2+2)M-PAES has been compared with several different evolutionary strategies
with optimal results [C54].
In [C63][J43], the (2+2)M-PAES has been integrated with the linguistic 2-tuple representation
model, proposed by the group of Prof. Francisco Herrera of the University of Granada, which
allows the symbolic translation of a label by only considering one parameter, so as to learn
concurrently rule bases and parameters of the membership functions of the associated linguistic
labels. Results have confirmed the effectiveness of this synergy, specially for (possibly highdimensional) datasets characterized by high values of the complexity measure.
In [J44][C65], the concepts of virtual and concrete rule bases have been introduced and
integrated with the (2+2)M-PAES. The virtual rule base is defined on linguistic variables, all
partitioned with a fixed maximum number of fuzzy sets, while the concrete rule base takes into
account, for each variable, a number of fuzzy sets as determined by the specific partition
granularity of that variable. Thus, the (2+2)M-PAES allows learning concurrently both rule base
and granularity of the uniform partitions of FRBSs.
In [C69][J45], the (2+2)M-PAES has been integrated with a piecewise linear transformation so
as to learn concurrently rule base and membership function parameters. In [J74], this approach
has been applied to learn both fuzzy rules and two-valued logic rules. Further, in [C68][C70], two
indexes based on the piecewise linear transformation have been defined for, respectively,
evaluating the partition integrity and the knowledge-base interpretability. The two indexes have
been used as objectives in the (2+2)M-PAES [J53][J54][C75].
The (2+2)M-PAES results to be very computationally heavy when applied to high-dimensional
large datasets. To reduce this problem, two solutions have been proposed. The first solution
exploits a co-evolutionary approach [J58][C74]. In the execution of the (2+2)M-PAES,
periodically, a single-objective genetic algorithm (SOGA) evolves a population of reduced TSs.
The SOGA aims to maximize a purposely-defined index which measures how much a reduced
TS is representative of the overall TS in the context of the multi-objective evolutionary learning
(MOEL). We tested our approach on a real world high dimensional dataset. We have shown that
the Pareto fronts generated by applying the MOEL with the overall and the reduced TSs are
comparable, although the use of the reduced TS allows saving on average the 90% of the
execution time. The second solution exploits a two-level rule selection (2LRS) [C78][C89]. The
2LRS aims to select a reduced number of rules from a previously generated rule base and a
reduced number of conditions for each selected rule. The 2LRS can be considered as a rule
learning in a constrained space. It follows that the search space results to be reduced with respect
to rule learning. In [J69], we tested the 2LRS approach on twenty-four classification benchmarks
and compared our results with the ones obtained by two similar state-of-the-art MOEA-based
Page 24 of 55
approaches and two well-known non-evolutionary classification algorithms, namely FURIA and
C4.5. Using non-parametric statistical tests, we showed that the 2LRS approach is able to generate
FRBCs with accuracy comparable to both the MOEA-based approaches, but using only 5% of the
number of fitness evaluations, and to FURIA and C4.5. The two solutions have been also applied
together in [C81][J62] for regression problems, achieving very significant results.
In [J52][BC6][C66], a simple but effective approach to fast identification of consequent
parameters of Takagi-Sugeno FRBSs, although in an approximated, suboptimal manner, has been
proposed. This approach results to be very useful when a multi-objective evolutionary algorithm
is used to generate a set of FRBSs and therefore a large number of consequent parameter
identifications is required.
In the framework of binary classifiers for imbalanced and cost-sensitive datasets, a threeobjective evolutionary algorithm to produce a Pareto front approximation composed of fuzzy rulebased classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms of
sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents
of the classifier rules) has been proposed [C57][C59]. The ROC convex hull method is used to
select the potentially optimal classifiers in the projection of the Pareto front approximation onto
the ROC plane. The method allows achieving very considerable recognition rates [J46]. Further,
the rules allow describing how the FRBCs reason during the classification task. The method has
been extended in [C92] by adding the parameter learning to the rule learning during the
evolutionary process. In [J68], we performed an experimental study by comparing this variant of
the multi-objective evolutionary FRBC with three evolutionary fuzzy classifiers purposely
designed to manage imbalanced datasets. By using non-parametric statistical tests, we showed
that our approach outperforms two of the comparison algorithms and results to be statistically
equivalent to the third, although with a lower number of rules. Further, it is statistically equivalent
in terms of accuracy to two state-of-the-art algorithms proposed to generate fuzzy rule-based
classifiers and to four approaches based on ensembles of non-fuzzy classifiers.
Feature and Instance selection in evolutionary fuzzy rule-based systems
The computational time required by evolutionary algorithms for generating fuzzy rule-based
models from data increases considerably with the increase of the number of instances in the
training set, mainly due to the fitness evaluation. Also, the amount of data typically affects the
complexity of the resulting model: a higher number of instances generally induces the generation
of models with a higher number of rules. Since the number of rules is considered one of the factors
which affect the interpretability of the fuzzy rule-based models, large datasets generally bring to
less interpretable models. Both these problems can be tackled and partially solved by reducing
the number of instances before applying the evolutionary process. In the literature several
algorithms of instance selection have been proposed for selecting instances without deteriorating
the accuracy of the generated models. In [C90][J63], the effectiveness of 36 training set selection
methods when combined with genetic fuzzy rule-based classification systems has been analysed.
Using 37 datasets of different sizes we have shown that some of these methods can considerably
help to reduce the computational time of the evolutionary process and to decrease the complexity
of the fuzzy rule-based models with a very limited decrease of their accuracy with respect to the
models generated by using the overall training set.
In [C97][J73], a novel approach to feature selection based on fuzzy mutual information has been
also proposed. The approach results to be particularly effective because it selects the features by
using the same partitions adopted for generating the fuzzy rule-based systems.
Fuzzy associative classifiers
Page 25 of 55
Associative classification models are based on two different data mining paradigms, namely
pattern classification and association rule mining. These models are very popular for building
highly accurate classifiers and have been employed in a number of real world applications. During
the last years, several studies and different algorithms have been proposed to integrate associative
classification models with the fuzzy set theory, leading to the so-called fuzzy associative
classifiers. In [J71], we have proposed a novel efficient fuzzy associative classification approach
based on a fuzzy frequent pattern mining algorithm. Fuzzy items are generated by discretizing the
input variables and defining strong fuzzy partitions on the intervals resulting from these
discretizations. Then, fuzzy associative classification rules are mined by employing a fuzzy
extension of the FP-Growth algorithm, one of the most efficient frequent pattern mining
algorithms. Finally, a set of highly accurate classification rules is generated after a pruning stage.
We tested our approach on seventeen real-world datasets and compared the achieved results with
the ones obtained by using both a non-fuzzy associative classifier, namely CMAR, and two recent
state-of-the-art classifiers, namely FARC-HD and D-MOFARC, based on fuzzy association rules.
Using non-parametric statistical tests, we have showed that our approach outperforms CMAR and
achieves accuracies similar to FARC-HD and D-MOFARC.
Random forests have proved to be very effective classifiers, which can achieve very high
accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with
uncertain data in decision tree learning, fuzzy random forests have not been particularly
investigated in the fuzzy community. In [C98], we have first proposed a simple method for
generating fuzzy decision trees by creating fuzzy partitions for continuous variables during the
learning phase. Then, we have discussed how the method can be used for generating forests of
fuzzy decision trees. Finally, we have shown how these fuzzy random forests achieve accuracies
higher than two fuzzy rule-based classifiers recently proposed in the literature. Also, we have
highlighted how fuzzy random forests are more tolerant to noise in datasets than classical crisp
random forests.
Context adaptation of fuzzy rule-based systems
Context adaptation is certainly a promising approach in the development of FRBSs [C60]. First,
an initial rule base is extracted from heuristic knowledge of the application domain. Meanings of
linguistic terms are defined so as to guarantee high interpretability of the FRBSs. Then, meanings
are adapted to a specific context through the use of operators that, using a set of known inputoutput patterns, appropriately modify the corresponding fuzzy sets. The choice of the specific
operators and their parameters is context-based and optimized so as to obtain a good
interpretability-accuracy tradeoff. In this framework, we have proposed a set of operators that,
starting from a given FRBS, adapt the FRBS to the specific context by adjusting the universes of
the input and output variables, and modifying the core, the support and the shape of the fuzzy sets
which compose the partitions of these universes [C51][C52][J36]. The operators are defined so
as to preserve ordering of the linguistic terms, universality of rules and interpretability of
partitions. The choice of the parameters used in the operators is performed by an evolutionary
optimization process aimed at maximizing the accuracy and preserving the interpretability of the
FRBS. Interpretability is measured by using a purposely-defined index [C56][J39].
The approach proposed in [J39] has been combined with the approach in [J33] to generate
Mamdani FRBSs by using a multi-objective cooperative co-evolutionary approach [C62]. This
approach evolves two separate populations (species), composed by individuals which,
respectively, encode rules and partitions. The dependencies between the species are managed by
selecting proper representatives that concur to compute the fitness of the other species.
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Application of computational intelligence to handwritten text recognition
A self-learning system, named BEATRIX, for off-line recognition of handwritten texts has been
designed and implemented [J1] [NJ2] [NC2]. The system integrates neural recognition with
context analysis techniques. It consists of three main interacting subsystems: the first is based on
an ensemble of neural networks and carries out an approximate pre-recognition of characters; the
second carries out a lexical and grammatical analysis of the recognised text. This analysis
produces hypotheses about words and sentences in order to correct errors made by the neural
networks. Once a sufficient number of words have been recognized, the third subsystem retrains
one of the neural networks with the hypotheses produced. This enhances the capacity of the
system to recognise the specific handwriting, without losing the capability to recognise other types
of handwriting.
Further, a novel fuzzy logic-based method for off-line recognition of isolated handwritten
characters has been proposed [J8] [C13] [C17]. The method exploits the following observation:
although a large variety of writing styles exist, the general shape of characters can be described
by reference models. A fuzzy approach is used to model character shape. First, uniform fuzzy
partitions are built on the horizontal and vertical axes of the character image. Then, a linguistic
representation of the character is generated in the two universes of the labels associated with the
horizontal and vertical fuzzy sets, respectively. Finally, a linguistic reference model of each
character is appropriately derived from the linguistic representations of the samples of the
character composing the training set. When an unknown character has to be recognised, its
linguistic representation is compared to the linguistic reference models of each character by using
a purposely-defined weighted distance. The character is recognised as the character associated
with the closest reference model in terms of the defined distance. The off-line recogniser of
isolated characters has been combined with context and statistics modules for automatic
recognition of handwritten sentences [C27].
This new method has been integrated with the ensemble of neural networks used in BEATRIX
[BC2] [C24] [C27], so as to allow the context analysis module to use two different and
independent recognisers. The integration has considerably improved the results achieved by
BEATRIX.
Application of computational intelligence to automatic odour recognition
Electronic noses integrate an array of a few sensors with partially overlapping sensitivities to
odours and a pattern recognition system. The physical and chemical reactions produced by the
sensors when stimulated by an odorant are appropriately transduced into electrical signals. Each
sensor responds differently to different odorants, and therefore, the output patterns from the sensor
array can be used by the pattern-recognition system for discriminating different kinds of odorants.
Three different methods have been developed for classification and recognition of signals
produced by olfactive sensors.
In the framework of the Esprit INTESA project, a novel non-linear pattern recognition method
completely based on fuzzy logic has been proposed [J3] [BC3] [C14] [C19] [C21] [C25]. To
model signal shape, firstly, a fuzzy partition is built on the time and sensor response spaces. Then,
a fuzzy model of the signal is generated in the space of the labels assigned to the fuzzy sets of the
fuzzy partition. More precisely, the area in the signal space, which is occupied by the signal, is
described in linguistic terms in such a way that only the major aspects of the signal shape are
retained in the model. To model signals in the label space rather than in the signal space
dramatically reduces the computation required to compare signals when an unknown odorant is
to be recognised. Linguistic models are compared by using a purposely-defined weighted
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distance. Weights take into account the ability of each sensor and of each part of the sensor
response in discriminating a specific odorant. Weights are automatically generated during the
training phase of the system: the closer the signals generated by a sensor in repeated experiments
with the same odorant, the higher the weight associated with the sensor for that odorant [J17].
The linguistic fuzzy modelling produces a normalisation of the signals so that they can be
analysed independently of their amplitude. This means that the information about the dynamic
range of the signal is lost. As such information can contribute to the classification task, this
information is exploited in an independent fuzzy classification system. More precisely, a fuzzy
model is built to represent the dynamic range of the sensor responses. The method has been used
as pattern recognition system of an electronic nose in environmental applications [J16], in food
package control [C22] and in quality and geographical discrimination of olive oils [C35].
The second method is based on a fuzzy hierarchical approach. The sensor responses are
represented by means of the coefficients of their Fast Fourier Transform (FFT) [J12] [C15]. A
feature reduction method is applied to reduce the feature space dimension. Then, an Unsupervised
Fuzzy Divisive Hierarchical Clustering (UFDHC) method is used to establish the optimal number
of clusters in the data set as well as the optimal cluster structure. The output of UFDHC is a binary
hierarchy of fuzzy classes that are adopted to build a supervised fuzzy hierarchical classifier.
The third method is based on a new evolutionary search and optimisation strategy [C20]. The
strategy forces the formation and maintenance of sub-populations of solutions. Sub-populations
co-evolve and converge towards different (sub-)optimal problem solutions. Only local
chromosome interactions are allowed in order to avoid migration between sub-populations
approximating different optimum points and to prevent the destruction of sub-populations. The
method has been applied for detecting the optimal number of clusters in a set of points which
represent signals generated by olfactory sensors.
Application of computational intelligence to two-dimensional shape recognition
A method for fuzzy classification and recognition of two-dimensional shapes, such as
handwritten characters, image contours, etc., has been proposed [J15]. The method is an evolution
of the method already described in the two previous subsections. A fuzzy model is derived for
each considered shape from a fuzzy description of a set of instances of this shape. A fuzzy
description of a shape instance, in its turn, exploits appropriate fuzzy partitions of the two
dimensions of the shape. These fuzzy partitions allow identifying and automatically associating
an importance degree with the relevant shape zones for classification and recognition purposes.
A genetic algorithm is used to automatically identify the (sub-)optimal partitions. The use of the
genetic algorithm improves performance obtained both on odour recognition and handwritten text
recognition.
Application of computational intelligence to combine outputs of multiple classifiers
The main goal of designing pattern recognition systems is to achieve the best classification
performance for the task at hand. Since there does not exist a unique classification scheme suitable
to any application domain, this objective is typically reached by developing several recognition
systems based on different classification schemes and selecting the one that obtains the best results
on an experimental assessment test. Although one of the systems would attain the best
performance, it has been experimentally observed that the sets of patterns misclassified by the
different classifiers are not overlapped. Several different techniques have been proposed in the
literature to combine outputs of multiple classifiers. In this framework, two new approaches have
been proposed. The first approach exploits the knowledge of the statistical behaviour of the single
classifiers on the training set to re-calculate a global recognition confidence degree based on the
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a-posteriori probability that the input pattern belongs to a given class conditioned by the specific
responses of the classifiers [C28]. Applying the Bayes’s theorem the classifier combiner can also
be easily adapted to a specific application. The second approach uses a fuzzy system as a variant
of the linear combiner [C41]. While the linear combiner associates a weight with each pair
(classifier, class), the proposed approach allows assigning a weight to the triple (classifier, class,
region of the classifier output space). Thus, the correlations between classifier outputs can be
properly considered. The method has been compared with other 10 techniques, showing optimal
results. The fuzzy rule-based system used as combiner employs Takagi-Sugeno rules, which are
characterised by having as consequent a linear combination of the inputs. To determine the
parameters constrained to be non-negative has been proposed a recursive solution to the NonNegative Least Square Problem which is more efficient in terms of memory and execution time
than the non-recursive one [J51].
Application of computational intelligence to automatic assembly planning
A genetic algorithm that generates and assesses assembly plans has been proposed [J4] [J5]
[C16]. An appropriately modified version of the well-known partially matched crossover, and
purposely defined mutation operators allow the algorithm to produce near-optimal assembly plans
starting from a randomly initialised population of (possibly non-feasible) assembly sequences.
The quality of a feasible assembly sequence is evaluated based on the following three optimisation
criteria: i) minimising the orientation changes of the product, ii) minimising the gripper
replacements, and iii) grouping technologically similar assembly operations. An evolution of the
genetic algorithm, where weights associated with the genes of the chromosomes are automatically
adapted, has considerably improved the quality of the solutions [J11].
Application of computational intelligence to feature selection
In pattern recognition tasks, patterns are generally described by a set of features. In order to
reduce the feature space dimension while maintaining acceptable classification accuracy, feature
selection methods are usually adopted. In this framework, two methods have been proposed. The
first method associates a weight with each feature by minimising an appropriate index defined in
terms of similarity between patterns of the training set [J20]. The weight measures the importance
of the corresponding feature in characterising the classes. Features associated with low weights
are considered irrelevant and therefore eliminated. The second method is based on a modified
fuzzy C-means algorithm with supervision (MFCMS) [C23] [J22]. The labelled patterns allow
MFCMS to accurately model the shape of each cluster and consequently to highlight the features,
which result to be particularly effective to characterize a cluster. These features are distinguished
by a low variance of their values for the patterns with a high membership degree to the cluster. If,
with respect to these features, the distance between the prototype of the cluster and the prototypes
of the other clusters is high, then these features have the property of discriminating between the
cluster and the other clusters. To take these two aspects into account, for each cluster and each
feature, a purposely defined index has been introduced: the higher the value of the index, the
higher the discrimination capability of the feature for the cluster. MFCMS is applied to the
training set considering all patterns as labelled. Then, the features which are associated, at least
for one cluster, with an index larger than a threshold are retained. MFCMS has been applied to
several real-world pattern classification benchmarks. The output produced by MFCMS is used by
a purposely defined version of the well-known k-nearest neighbours (k-NN) to recognise
unknown patterns. The combination MFCMS/k-NN has been applied to classify food packages
[J18] and has proved to be very effective to counteract the drift of olfactive sensors [J9].
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Application of computational intelligence to clustering and classification
The concept of dissimilarity between two patterns is usually expressed in terms of a distance
measure on the feature space. Several algorithms have been developed that also include means
(e.g., weighting schemes) to cope with mixed-type and/or different-scaled features. Distance
measures, however, may fail to model the dissimilarity concept when the data distribution does
not follow any known regular scheme, or whenever the dissimilarity between any two arbitrary
patterns depends on conceptual aspects that cannot be expressed in terms of some quantitative or
qualitative features. Refer, for example, to a 2D image that consists of distinguishable elements
(such as houses, cars, trees, etc.), which are not easily described by regular geometric forms. In
such cases, two points belonging to different elements can be closer than a pair of points belonging
to the same element. To overcome this problem, two methods have been proposed to extract the
similarity (dissimilarity) directly from data.
In the first method, some pairs of points with known dissimilarity value are used to teach a
dissimilarity relation to a feed-forward neural network [J24] [C31]. Once trained, the neural
network can associate a dissimilarity degree with each pair of points in the data set under
consideration. The dissimilarity computed by the neural network can be used by a classification
algorithm (for instance, k-NN). The combination neural network/k-NN has been experimented on
both synthetic and real data [J21] [C32]. The results have proved that the neural network is able
to learn the dissimilarity relation using a few pairs of points with known dissimilarity.
Alternatively, the dissimilarity produced by the neural network can be used to guide a relational
clustering algorithm. This approach can partition correctly data sets which are not easily managed
by classical clustering algorithms based on traditional spatial similarity [J28] [C31].
In the second method, some pairs of points with known dissimilarity value are used to build a
system composed of fuzzy rules [BC5][J30]. The rules are automatically generated by means of
a combination of a clustering algorithm and a genetic algorithm. The fuzzy system can associate
a dissimilarity degree with each pair of points in the data set. The dissimilarity relation has been
used to guide two different relational fuzzy clustering algorithms [C38] [C40]. The results
obtained by the fuzzy systems are comparable with the ones achieved by the neural network.
Unlike neural networks, however, fuzzy systems can provide a description of the dissimilarity
relation through the rules expressed in linguistic terms [C38].
As regards clustering, a new approach to transform a non-Euclidean dissimilarity relation into a
Euclidean relation has been proposed. The Euclidean relation maintains the same information as
the original non-Euclidean relation [C34]. The aim of this transformation is to allow the use of
the Relational Fuzzy C-Means (RFCM), one of the most efficient and reliable fuzzy relational
clustering algorithms. RFCM guarantees convergence and stability only if the dissimilarity
relation is Euclidean. Thanks to the proposed transformation, the RFCM algorithm can be applied
to any dissimilarity relation. The results obtained by the proposed approach on synthetic and real
data sets have been better than the Non-Euclidean RFCM algorithm, one of the most interesting
and used algorithms proposed in the literature to transform a non-Euclidean into a Euclidean
dissimilarity relation.
Further, a novel relational fuzzy clustering algorithm based on the classical fuzzy C-means
algorithm has been proposed [J27]. The algorithm has arisen from the observation that a relational
clustering problem can be transformed into an object clustering problem by considering the
relation strengths of an object with the other objects as the features of the object. Several
experiments have highlighted the good characteristics of the algorithm with respect to the most
popular relational fuzzy clustering algorithms.
In real applications, data sets are often noisy and affected by the presence of outliers. To manage
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adequately noise and outliers, robust clustering algorithms are adopted. Robust fuzzy C-means
(robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise
is modelled as a separate cluster and is characterized by a prototype that has a constant distance
δ from all data points. Distance δ determines the boundary of the noise cluster and therefore is
a critical parameter of the algorithm. Though some approaches have been proposed to
automatically determine the most suitable δ for the specific application, up to today an efficient
and fully satisfactory solution does not exist. A novel method has been proposed to compute the
optimal δ based on the analysis of the distribution of the percentage of objects assigned to the
noise cluster in repeated executions of the robust-FCM with decreasing values of δ [C42]. The
results obtained have been extremely interesting.
Application of computational intelligence to the estimation of the concentrations of optically
active constituents of the sea water.
A fuzzy model for the estimation of the concentrations of optically active constituents of the sea
water has been proposed and developed [J25] [CLI1] [C29] [C30]. As it is well-known in the
literature, the concentrations of some components, such as chlorophyll, dissolved organic matter
and suspended non-chlorophyllous particles of the sea water, modify the optical properties of the
pure sea water. Such concentrations can be therefore estimated by using a set of measures of
reflectance of the sea water performed by sensors on board satellites. As the concentration of each
constituent varies independently over a wide range of values, and the relation between the
reflectances and the constituents concentration is strongly non-linear, the estimation is quite
complex and difficult to perform adopting standard identification techniques. The relation
between reflectance and optically active constituents has been modelled by means of a set of fuzzy
rules automatically extracted from available data. The extraction process is performed through
the following two steps. First, an appropriately modified version of the FCM clustering algorithm
is applied to extract a compact initial rule-based model from the data. Then, the rules, which are
expressed in Takagi-Sugeno-Kang (TSK) style, are refined using a genetic algorithm. To preserve
the semantic properties of the initial model appropriate constraints on the partition of the input
space are forced during the genetic evolution. At the end of the optimisation process, the extracted
rules can be easily associated with a physical meaning, thus leading transparency to the rules
themselves. The effectiveness of the fuzzy approach is proved by using a set of estimates of
average subsurface reflectances over spectral channels centered around prefixed wavelengths in
the visible spectrum of MERIS, the new generation sensor which is on board the ESA-ENVISAT
satellite launched in March 2002.
TSK fuzzy rules used to model the relation between reflectance and optically active constituents
are first-order TSK rules, that is, the consequent part of the rule is a linear function. In the
literature, it has been proved that first-order TSK models are universal approximators. Actually,
this is true only if no constraint is enforced on the number of rules. Since the number of rules is
determined by the clustering algorithm based on the distribution of the points in the input/output
space, the desired approximation might not be achieved. To improve approximation accuracy,
quadratic functions instead of linear functions can be used as consequent parts of rules. To this
aim, a method for building TSK systems with quadratic consequent functions has been proposed.
The method has been used to model the relation between reflectance and optically active
constituents using the same data described previously. The results have proved that using
quadratic functions as consequent parts of TSK rules results in a considerable approximation
improvement [J32][C33].
In [J38][C49], the generation of the TSK-type FRBSs has been tackled by using the (2+2)MPAES described in the previous subsections. Accuracy and complexity are the two competitive
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objectives to be simultaneously optimized. TSK-type FRBSs are implemented as an artificial
neural network; by training the neural network, the parameters of the fuzzy model are adjusted.
In this way, the evolutionary optimization coarsely identifies the structure of the TSK-type
FRBSs, while the corresponding neural networks finely tune their parameters. As a result, a set
of TSK-type FRBSs with different trade-offs between accuracy and complexity is provided at the
end of the optimization process. The effectiveness of the approach has been shown by comparing
the results with those obtained on the ocean colour inverse problem by other techniques proposed
in the literature.
Application of computational intelligence to the automatic calibration of positron emission
tomograph detector modules.
Positron emission tomography (PET) is a powerful technology for examining parts of the body
by inserting radioactive elements (typically isotopes) into the vascular system and then looking
for concentrations of these tracers in various organs. As the isotope decays, it emits gamma rays
which are intercepted by a gamma detector, usually based on arrays of crystal elements. The
detector, in its turn, emits the so-called ‘scintillation photons’ that are converted into electrons by
the photocathode of a photomultiplier tube (PMT), which increases their number. Based on the
positional information of the electron ejection site from the photochatode, the PMT is able to map
the radiation intensity and its location in the body into an image. Of course, high sensitivity and
high spatial resolution are required especially for small animal studies. To achieve high resolution
we have to cope with the geometric distortions in the final image due to irregularities of the
optical-electronic system. This means that very high accuracy is required in determining the exact
correspondence between each pixel of the image produced by the scanner and the scintillating
crystal of the gamma ray detector that influences that pixel. A crystal detector calibration is
therefore indispensable. Typically, the calibration is performed by hand. To automatize crystal
detector calibration, a novel method based on a purposely-modified version of the classical selforganizing map (SOM) model has been proposed [J26] [C39]. The method has been proved on a
large number of images, providing considerable results.
Application of computational intelligence to identify web user profiles from access log.
A web portal identifies a World Wide Web site which operates as a major starting point for users
when they connect to the web. Typically, web portals feature a suite of services, such as a search
engine, news, email, stock quotes, maps, forums, chat and shopping. Often, however, the wish to
reach each type of user leads web portal designers to insert too much information into each page,
in particular into the pages which constitute the entry points of the portal. Further, the
advertisements are periodically shown in the pages, without caring about the type of user. This
scenario demands a re-organization of the web portals and the web advertising to tailor them to
meet the users’ interests and thus to increase users’ satisfaction. The analysis of the users’ interests
is the main concern of the web usage mining. Typically, web usage mining tools are used to cluster
the users into different groups and generate common user profiles from the web access log. These
profiles may be extremely useful to re-design the web portal. Two different approaches have been
proposed to determine web user profiles. The former is based on an appropriately targeted version
of the well-known fuzzy C-means (FCM) algorithm [J29] [C36]. The latter exploits an
unsupervised fuzzy divisive hierarchical clustering algorithm [J31] [C37]. The two methods have
been compared with each other and with the association rules determined by the application of
the A-priori algorithm. Both methods produce meaningful and satisfactory partitions, identifying
similar profiles.
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Application of computational intelligence to determine a set of trade-offs between image quality
and compression in the JPEG algorithm
The JPEG algorithm is one of the most used tools for compressing images. The main factor
affecting the performance of the JPEG compression is the quantization process, which exploits
the values contained in two tables, called quantization tables. The compression ratio and the quality of the decoded images are determined by these values. Thus, the correct choice of the quantization tables is crucial to the performance of the JPEG algorithm. In [J47], a two-objective evolutionary algorithm is applied to generate a family of optimal quantization tables which produce
different trade-offs between image compression and quality. Compression is measured in terms
of difference in percentage between the sizes of the original and compressed images, whereas
quality is computed as mean squared error between the reconstructed and the original images. We
discuss the application of the proposed approach to well-known benchmark images and show how
the quantization tables determined by our method improve the performance of the JPEG algorithm
with respect to the default tables suggested in Annex K of the JPEG standard.
Application of computational intelligence for detecting faults in photovoltaic fields
An intelligent system for the automatic detection of faults in photovoltaic (PV) fields has been
proposed [C88]. The system exploits an FRBS consisting of first-order Takagi-Sugeno-Kang
rules. The FRBS provides an estimation of the instantaneous power production of the PV field in
normal functioning, i.e, when no fault occurs. The estimated power is compared with the power
actually produced by the real PV field and an alarm signal is generated if the difference between
the two powers is higher than a pre–fixed threshold. The FRBS has been trained using normal
functioning data collected from a PV plant simulator. Preliminary tests performed on simulated
data reproducing both normal and fault conditions have highlighted that the system can recognize
more than 90% of fault conditions, even when the test data are affected by a uniformly distributed
2% noise.
Application of computational intelligence for reducing power consumption in buildings
Recent studies have highlighted that a significant part of the electrical energy consumption in
residential and business buildings is due to an improper use of the electrical appliances. In this
context, an automated power management system - capable of reducing energy wastes while
preserving the perceived comfort level - would be extremely appealing. To this aim, we have
proposed GreenBuilding, a sensor-based intelligent system that monitors the energy consumption
and automatically controls the behavior of appliances used in a building. GreenBuilding has been
implemented as a prototype and has been experimented in a real household scenario [C82][C85].
The analysis of the results obtained in the experimentation confirms that a significant amount of
energy is wasted due to improper use of appliances. We show that this energy waste can be
eliminated (or drastically reduced) by using a simple energy conservation rule for each specific
appliance, or class of appliances.
GreenBuilding employs a sensor for each appliance. In order to reduce the cost of the infrastructure, a novel approach to extract the power consumption of a set of appliances from aggregate
measurements collected from a smart meter has been proposed [C91][J67]. The approach employs
finite state machines based on fuzzy transitions (FSMFT) and a novel disaggregation algorithm.
The FSMFTs are used to coarsely model how each type of appliance works. The disaggregation
algorithm exploits a database of FSMFTs for, at each meaningful variation of real and reactive
aggregate powers, hypothesizing possible configurations of active appliances. This set of configurations is concurrently managed by the algorithm which, whenever requested, outputs the configuration with the highest confidence with respect to the sequence of detected events. We have
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developed a prototype that implements the proposed approach and have tested it on an experimental scenario in which eleven appliances have been deployed and monitored for thirty minutes
[J67]. We have shown that at the end of the experiment, our prototype is able to disaggregate the
power signal measured by the smart meter, extracting the correct power consumption of each
single appliance.
Software development methods
In the framework of reuse and maintenance of object-oriented systems, a novel modularisation
unit, denoted molecule, has been introduced to overcome the limits that objects reveal in
partitioning and structuring large applications [C3] [C5]. The basic aims of the molecule are: i)
to overcome the limited modelling capacity of objects by inserting a higher abstraction level
entity; ii) to separate stable (application-independent) parts of a system from volatile parts
(application-dependent); iii) to reduce the intertwining of nested communications which typically
arise in traditional object-oriented systems and are difficult to disentangle for future maintainers.
A molecule-oriented language has been designed and implemented [C4]. Furthermore, a
molecule-oriented development method has been proposed [C6] [C7]. The method allows reusing
molecules contained in the library in each phase of the development process.
A new fuzzy logic-based method to develop object-oriented systems has been defined [J14]
[BC4] [C11] [C18] [C26]. The method allows reducing the effects of two problems which affect
traditional methods [J13]. The first problem arises from the use of two-valued logic which does
not provide an adequate means to capture the approximate and inexact nature of the software
development process. A quantitative evaluation of the effects produced by the lack of expressive
ability of two-valued logic has been proposed in [J23].
The second problem derives from the validity of a rule, which may largely depend on contextual
factors such as application domain, changes in user’s interest and technological advances. Unless
these relevant contextual factors are not modelled explicitly, the applicability of that rule cannot
be determined. Fuzzy logic allows reducing the two problems and further managing possible
inconsistencies, which arise along the overall development process [J19]. These inconsistencies
are desirable when, for instance, are alternative solutions to the same problem. Preserving
different design solutions allows performing a better choice and producing a better software.
Existing framework development practices span a considerable amount of refinement time, and
it is worthwhile to shorten this effort. The main reason of this extensive refinement is the lack of
an integrated approach to model domain knowledge related to the framework and to map the
identified domain models into an object-oriented framework. To overcome these problems, a
novel framework development method has been proposed [J10] [BC1] [C12]. First, the top-level
structure of frameworks is modelled by using the so-called knowledge graphs. Second, each node
is refined within a top-level knowledge graph into a sub-knowledge graph called knowledge
domain. The nodes in a knowledge domain, however, correspond to a particular specialization in
the domain and the relations typically represent generalization and specialization relations.
Finally, nodes in a knowledge domain which can be included together into the top-level
knowledge graph are identified. A set of semantically correct alternatives depicts here the
adaptability space. This is needed because specializations from different domains may enforce
constraints on each other. When a framework is instantiated as an application, each node in the
knowledge graph corresponds to a specialization of the related knowledge domain.
A novel methodology based on a declarative and device-independent approach for developing
event-driven mobile applications has been proposed in [J60]. The methodology relies on: (i) an
abstract mobile device based on the user interface markup language; (ii) a content adaptation
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mechanism based on user preferences; (iii) a context adaptation mechanism based on a standardized context of delivery; (iv) a uniform set of client-side APIs based on an interface object model;
(v) an efficient transformational model. More specifically, in the design phase, the application is
modeled as platform-independent on the abstract mobile device. In the execution phase, the application is automatically tailored to the specific platform on the basis of the content and context
adaptation mechanisms.
Lot and process traceability infrastructures
Using the method proposed in [J10] [BC1] [C12], a framework for food traceability has been
developed. The framework takes both lot tracing and tracking into account. Further, quality
aspects are also managed [C44]. First, a data model and a set of suitable patterns to encode generic
traceability semantics have been introduced [C46][J34]. Then, suitable technological standards to
define, register, and enable business collaborations have been discussed. Finally, a practical
implementation of a traceability system through a real world experience on food supply chains
has been shown. The enabling technologies used for business collaboration have been also
exploited to develop the service oriented architecture aimed at performing specific operations,
issued by distributed processes through different protocols (HTTP, POP3, RMI and SMS), on a
particular ERP system, namely the SAP R/3 system. The architecture results from the integration
of two different service-oriented technologies: Java Message Service and SAP Java Connector,
and uses Business Process Execution Language for web services choreography [C47].
The traceability framework has been integrated with a Business Modelling tool so as to develop
a framework for Business Process Management (BPM). More specifically, first a method to deal
with the BPM life cycle has been introduced. Then, a platform to support this life cycle has been
proposed [C67]. The platform comprises three basic modules: a visual BPMN-based designer, a
process tracing service, and a business process manager for, respectively, the design,
configuration and execution phases of the BPM life cycle. The proposed framework is particularly
useful to perform business simulations such as what-if analysis, and to provide an efficient
integration support within the supply-chain.
In [J55], an agent-based version of the framework has been proposed, in which cooperative
software agents find solutions to back-end tracing problems by self-organization. Such
cooperative agents are based on a business-process aware traceability model, and on a serviceoriented composition paradigm. Furthermore, an interface agent assists each user to carry out the
front-end tracking activities. Interface agents rely on the context-awareness paradigm to gain selfconfigurability and self-adaptation of the user interface, and on ubiquitous computing technology,
i.e., mobile devices and radio-frequency identification, to perform agile and automatic lot
identification. Real-world experiences on the fashion and wine [BC8] supply chains have been
also discussed.
Service Recommenders
Nowadays, a huge quantity of resources for mobile users are made available on the most important marketplaces. Further, handheld devices can accommodate plenty of these resources, such
as applications, documents and web pages, locally. Thus, to search for resources suitable for specific circumstances often requires a considerable effort and rarely brings to a completely satisfactory result. A tool able to recommend suitable resources at the right time in each situation would
be of great help for the mobile users and would make the use of the handheld devices less boring
and more attractive. To this aim, an efficient situation-aware resource recommender (SARR) has
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been proposed. SARR helps mobile users to timely locate resources proactively [J49][C72]. Situations are determined by a semantic reasoner that exploits domain knowledge expressed in terms
of ontologies and semantic rules. This reasoner works in synergy with a fuzzy engine, which is in
charge of handling the vagueness of some conditions in the semantic rules, computing a certainty
degree for each inferred situation. These degrees are used to rank the situations and consequently
to assign a priority to the resources associated with the specific situations. In [C76] the management of uncertainty is tackled by using a fuzzy ontology and in [C77] the fuzzy rules are tuned to
the single users by using a genetic fuzzy system.
An agent-oriented architecture, which adopts Semantic Web reasoning, fuzzy logic modelling,
and genetic algorithms to handle, respectively, situational/contextual inference, uncertain input
processing, and adaptation to the user’s behaviour, has been proposed in [J59] so as to provide
both functional and structural interoperability in an open environment. The architecture is evaluated by means of a real-world case study concerning resource recommendation.
SARR exploits a calendar to make a reference schedule. The calendar is a common tool for
business and not for personal use, and hence its availability cannot be guaranteed in many real
world scenarios. Further, a calendar represents an explicit input requested to the user. On the
contrary, context information should be collected in terms of implicit inputs, coming from
changes in the environment. To avoid the use of the calendar, a collaborative multi-agent scheme
structured into three levels of information processing has been proposed in [C86][J61]. The first
level is managed by a stigmergic paradigm, in which marking agents leave marks in the environment in correspondence to the position of the user. The accumulation of such marks enables the
second level, a fuzzy information granulation process, in which relevant events can emerge and
are captured by means of event agents. Finally, in the third level, a fuzzy inference process, managed by situation agents, deduces user situations from the underlying events. The proposed
scheme has been tested on three representative real scenarios, considering four different types of
situation. For each scenario, the scheme has proved to be able to recognize the four types of situation just approximately at the instants when these situations occur [J65].
Data aggregation, data compression and node localization in wireless sensor networks
Energy is a primary constraint in the design and deployment of wireless sensor networks
(WSNs), since sensor nodes are typically powered by batteries with a limited capacity. Energy
efficiency is generally achieved by reducing radio communication, for instance, limiting
transmission/reception of data as much as possible. To this aim, two possible solutions have been
investigated: data aggregation and data compression.
A novel distributed approach to data aggregation based on fuzzy numbers and weighted average
operators has been proposed. The approach aims to reduce data communication in WSNs when
we are interested in the estimation of an aggregated value such as maximum or minimum
temperature measured in the network. The basic point of the approach is that each node maintains
an estimate of the aggregated value. Based on this estimate, the node decides whether a new value
measured by the sensor on board the node or received through a message has to be propagated
along the network. A procedure to estimate the lifetime of the network through the datasheet of
the sensor node and the number of received and transmitted messages has been discussed. Further,
the application of the approach to the monitoring of the maximum temperature in a 100-node
simulated WSN and a 12-node real WSN has been shown. Finally, the estimates of the lifetimes
for both the WSNs have been computed [C55][J35].
As regards data compression, the limited resources available in a sensor node do not allow using
the large amount of compression algorithms proposed in the last years for completely different
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applications and different machines, but demand the development of specifically designed
solutions. Thus, a simple lossless entropy compression algorithm has been proposed. The
algorithm can be implemented in a few lines of code, requires very low computational power,
compresses data on the fly and uses a very small dictionary whose size is determined by the
resolution of the analog-to-digital converter [J37] [BC7]. To evaluate the effectiveness of the
algorithm, four temperature and relative humidity datasets collected by real wireless sensor
networks have been used. The proposed algorithm achieves considerable compression ratios in
all the datasets. Further, the algorithm outperforms two compression algorithms proposed
previously in the literature to be embedded in sensor nodes [J42].
In [J48][J50][C71], a lossy compression algorithm based on quantization of the differences
between consecutive signal samples has been proposed. The parameters of the quantizers are
determined by using a multi-objective evolutionary algorithm, adopting entropy and signal/noise
ratio as objectives. An approach for reconfiguring the compression parameters through cognitive
IoT technologies is also discussed in [C94].
To know the location of nodes plays an important role in many current and envisioned wireless
sensor network applications [BC9]. In this framework, we consider the problem of estimating the
locations of all the nodes of a network, based on noisy distance measurements for those pairs of
nodes in range of each other, and on a small fraction of anchor nodes whose actual positions are
known a priori. The methods proposed so far in the literature for tackling this non-convex problem
do not generally provide accurate estimates. The difficulty of the localization task is exacerbated
by the fact that the network is not generally uniquely localizable when its connectivity is not
sufficiently high. In order to alleviate this drawback, we have proposed a two-objective
evolutionary algorithm, which takes concurrently into account during the evolutionary process
both the localization accuracy and certain topological constraints induced by connectivity
considerations [C83][C84][J57]. The proposed method is tested with different network
configurations and sensor setups, and compared in terms of normalized localization error with
another metaheuristic approach, namely SAL, based on simulated annealing. The results show
that, in all the experiments, our approach achieves considerable accuracies and significantly
outperforms SAL, thus manifesting its effectiveness and stability. A study on the application of
different two-objective evolutionary algorithms has been also performed in [C87].
Expert Systems
A tool to build expert systems which replace the role of the examiner during an exam has been
designed and implemented. The main characteristic of this tool is an inference engine which can
submit queries with a different level of difficulty on the basis of the candidate’s previous answers
[J2] [C1] [NC1]. The exam can thus be dynamically adapted to suit the ability of the student, i.e.
by making it more difficult or easier as required. The levels of difficulty are automatically
associated with the queries on the basis of some initial statistics. In addition, the tool can
automatically assign a score to the levels and to the queries. Finally, the systems collect statistics
so as to measure the easiness and selectivity of each query and to evaluate the validity and
reliability of an exam.
An expert system for assisting operators in managing alarm situations has been developed [NJ1]
[C2]. The basic characteristics of the system are: i) a friendly interface to make the use of the
system easy even during an emergency; ii) manual updating of the data initially supplied by the
user, depending on the evolution of the incident; iii) automatic input and updating in real time of
the meteorological data. The last two characteristics allow the system to provide always updated
scenarios. The expert system has worked at the Solvay & Cie (Rosignano Solvay – ITALY) for
Page 37 of 55
several years.
Medical Image Processing
Lung cancer is the primary cause of death from malignancy in the United States, Europe, and a
number of other countries, due to the fact that this disease usually manifests itself at an advanced
stage. Early diagnosis is therefore highly desirable. Actually low-radiation-dose CT scans can
effectively be used for screening programs to detect lung cancer at an operable stage. However
this type of exam produces a large amount of data to be examined by radiologists. A Computer
Aided Diagnosis (CAD) system could support radiologists in their diagnosis, helping them to
detect lung lesions and to distinguish true nodules from other anatomical structures. In this
framework, a CAD system to automatically detect nodules in lung CT images and to automatically
perform the diagnosis of such nodules without requiring any interactive intervention by the
radiologist has been proposed [C43][C45][C73][C93]. As a novel characteristic, the system
widely exploits decision fusion methods, trying to emulate not a single radiologist, but a team of
radiologists [C50][C53][J56]. This is achieved by implementing each of the three phases (i.e.,
VOI extraction, nodule detection and nodule classification) by means of a set of different
techniques, and by adding a specific module at the end of each phase which appropriately
integrates the outputs of each group of techniques [C58][C61].
Services for smart cities
Air quality monitoring
Air quality continues to be a serious issue for public health, the environment and ultimately, the
economy of European countries. Poor air quality results in ill health and premature deaths and
damages ecosystems, crops and buildings. In [J70], we have presented U-Sense, a cooperative
sensing system for real-time and fine-grained air quality monitoring in urban areas. U-Sense
allows monitoring to occur in places where people spend the majority of their day-to-day lives.
U-Sense relies on low-cost sensor nodes, equipped with appropriate gas sensors, which can be
privately installed by citizens. The sensor nodes are powered by batteries which allow for flexible
deployment and easy relocation. Users can share their measurements using social networking
which enables cooperating sensing.
Efficient Urban Parking
In [BC10][C95], we have presented a system for the effective and efficient management of urban
parking, thus providing a small, yet relevant contribution to the implementation of a real Smart
City. Our system relies on the identification of each single parking slot but, unlike other
approaches proposed in the last years, it does not require dedicated sensors and/or infrastructure,
thus it can be regarded as a low-cost and low-effort solution. Indeed, it collects parking data from
a mobile application on the drivers’ mobile devices and possibly identifies each slot by QR codes
deployed on the single parking spots. The amount of data collected by the system on parking
occupancy allows inferring valuable information that can be used by local governments. For
instance, it will be possible to define appropriate pricing schemes so as to promote parking areas
not particularly occupied. The employment of an SOA design guarantees the integration of the
developed system with other existing services within a Smart City.
Real-Time Detection of Traffic from Twitter Stream Analysis
Social networks have been recently employed as a source of information for event detection,
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with particular reference to road traffic congestions and car accidents [C96]. In [J72], we have
presented a real-time monitoring system for traffic event detection from Twitter stream analysis.
The system fetches tweets from Twitter according to several search criteria, processes tweets, by
applying text mining techniques, and finally performs the classification of tweets. The aim is to
assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic
detection system was employed for real-time monitoring of several areas of the Italian road
network, allowing to detect traffic events almost in real-time, often before online traffic news web
sites. We employed the Support Vector Machine as classification model and we achieved an
accuracy of 95.75% by solving a binary classification problem (traffic vs. non-traffic tweets). We
were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem, and obtaining an accuracy of 88.89%.
Wi-Fi based localization using external constraints
Wi-Fi based localization enables detection of users’ position in indoor spaces by means of
wireless networking infrastructure. The positive aspects of this solution include the reuse of
already deployed systems and thus its reduced costs. On the negative side, Wi-Fi based
localization is not particularly accurate, because the common operating conditions are far from
the ideal ones. In [C100], we have proposed to use external constraints for improving the accuracy
of Wi-Fi based localization. A set of known schedules is used to restrict the estimated position of
the user to a single room. The schedule for a given user is automatically selected from a set of
possible ones by observing user’s movements with coarse-grained resolution. We have applied
our solution in an academic campus where students move from one classroom to another for
attending lectures. The schedule of lectures is known and can be used to remove localization
ambiguities of a Wi-Fi based system.
Data Mining Algorithms for Big Data
In the context of big data, some data mining algorithm has been developed. In [J75], a distributed
association rule-based classification scheme shaped according to the MapReduce programming
model has been proposed. The scheme mines classification association rules (CARs) using a
properly enhanced, distributed version of the well-known FP-Growth algorithm. Once CARs have
been mined, the proposed scheme performs a distributed rule pruning. The set of survived CARs
is used to classify unlabelled patterns. The memory usage and time complexity for each phase of
the learning process are discussed, and the scheme is evaluated on seven real-world big datasets
on the Hadoop framework, characterizing its scalability and achievable speedup on small
computer clusters. The proposed solution for associative classifiers turns to be suitable to
practically address big datasets even with modest hardware support. Comparisons with two stateof-the-art distributed learning algorithms are also discussed in terms of accuracy, model
complexity, and computation time.
In [C99], a distributed version of the fuzzy associative classifier proposed in [J71] has been
proposed, pointing out its scalability. This version can manage big datasets with a modest
hardware support.
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PUBLICATIONS
Volumes
[V1] Abraham, J. M. Benitez Sánchez, F. Herrera, V. Loia, F. Marcelloni, S. Senatore, Proceedings of ISDA’09, IEEE Computer Society Press, Pisa, ITALY, 2009.
[V2] A.E. Hassanien, A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, T-P Hong, Proceedings of ISDA’10, IEEE Press, Cairo, Egypt, 2010.
[V3] S. Ventura, A. Abraham, K. Cios, C. Romero, F. Marcelloni, J.M. Benítez, E. Gibaja,
Proceedings of ISDA’11, IEEE Press, Cordoba, Spain, 2011.
Special Issues
[S1] F. Herrera, F. Marcelloni, V. Loia, Special Issue on Intelligent Systems Design and Applications (ISDA 2009), International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, World Scientific, Guest Editors’ Introduction, Vol 18, N. 4, 2010, pp. vvi.
[S2] José Manuel Benítez, Vincenzo Loia, Francesco Marcelloni, Special Issue on Advances
in Intelligent Systems, International Journal of Hybrid Intelligent Systems, IOS Press,
Guest Editors’ Introduction, vol. 7, N. 4, 2010, p. 237.
[S3] K. J. Cios, C. Romero, J.M. Benitez, F. Marcelloni, Special Issue on Intelligent Systems
Design and Applications (ISDA 2011), Integrated Computer-Aided Engineering, IOS
Press, vol. 20, N. 3, 2013, pp. 199.
[S4] F. Marcelloni, D. Puccinelli, A. Vecchio, Special Issue on “Sensing and Mobility in Pervasive Computing”, Journal of Ambient Intelligence and Humanized Computing,
Springer, vol. 5, N. 3, pp. 263-264.
International Journals
[J1]
B. Lazzerini, F. Marcelloni, L.M. Reyneri, “Beatrix: a self-learning system for off-line
recognition of handwritten texts”, Pattern Recognition Letters, vol. 18, n. 6, Elsevier,
1997, pp. 583-594.
[J2]
G. Frosini, B. Lazzerini, F. Marcelloni, “Performing automatic exams”, Computers &
Education, vol. 31, n. 3, Pergamon, 1998, pp. 281-300.
[J3]
B. Lazzerini, A. Maggiore, F. Marcelloni, “Classification of odour samples from a multisensor array using a new linguistic fuzzy method”, IEE Electronics Letters, vol. 34, n.
23, IEE, 1998, pp. 2229-2231.
[J4]
G. Dini, F. Failli, B. Lazzerini, F. Marcelloni, “Generation of optimized assembly sequences using genetic algorithms”, Annals of the CIRP – Manufacturing Technology,
Hallwag Publ Ltd, Berne, Switzerland, downloadable from Elsevier, vol. 48, n. 1, 1999,
pp. 17-20.
[J5]
B. Lazzerini, F. Marcelloni, “Assembly planning based on genetic algorithms”, International Journal of Knowledge-Based Intelligent Engineering Systems, IOS Press, vol.
3, n. 4, 1999, pp. 200-204.
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[J6]
B. Lazzerini, F. Marcelloni, “Some considerations on input and output partitions to produce meaningful conclusions in fuzzy inference”, Fuzzy Sets and Systems, Elsevier, vol.
113, n. 2, 2000, pp. 221-235.
[J7]
B. Lazzerini, F. Marcelloni, “Reducing computation overhead in MISO fuzzy systems”,
Fuzzy Sets and Systems, vol. 113, n. 3, Elsevier, 2000, pp. 485-496.
[J8]
B. Lazzerini, F. Marcelloni, “A linguistic fuzzy recogniser of off-line handwritten characters”, Pattern Recognition Letters, Elsevier, vol. 21, n. 4, 2000, pp. 319-327.
[J9]
B. Lazzerini, F. Marcelloni, “Counteracting drift of olfactory sensors by appropriately
selecting features”, IEE Electronics Letters, vol. 36, n. 6, IEE, 2000, pp. 509-510.
[J10] M. Aksit, F. Marcelloni, B. Tekinerdogan, “Developing object-oriented frameworks using domain models”, ACM Computing Surveys, vol. 32, n. 1es, ACM, 2000, pp. 1-5.
[J11] B. Lazzerini, F. Marcelloni, “A genetic algorithm for generating optimal assembly
plans”, Artificial Intelligence in Engineering, Elsevier, vol. 14, n. 4, 2000, pp. 319-329.
[J12] D. Dumitrescu, B. Lazzerini, F. Marcelloni, “A fuzzy hierarchical classification system
for olfactory signals”, Pattern Analysis and Applications, Springer-Verlag, vol. 3, n. 4,
2000, pp. 325-334.
[J13] F. Marcelloni, M. Aksit, “Improving object-oriented methods by using fuzzy logic”,
ACM Applied Computing Review, vol. 8, n. 2, 2000, pp. 14-23.
[J14] M. Aksit, F. Marcelloni, “Deferring elimination of design alternatives in object-oriented
methods”, Concurrency and Computation – Practice and Experience, John Wiley &
Sons, Inc., vol. 13, n. 14, 2001, pp. 1247-1279.
[J15] B. Lazzerini, F. Marcelloni, “A fuzzy approach to 2-D shape recognition”, IEEE Transactions on Fuzzy Systems, vol. 9, n. 1, 2001, pp. 5-16.
[J16] F. Di Francesco, B. Lazzerini, F. Marcelloni, G. Pioggia, “An electronic nose for odour
annoyance assessment”, Atmospheric Environment, Pergamon, vol. 35, n. 7, 2001, pp.
1225-1234.
[J17] B. Lazzerini, A. Maggiore, F. Marcelloni, “FROS: a fuzzy logic-based recogniser of
olfactory signals”, Pattern Recognition, Pergamon, vol. 34, n. 11, 2001, pp. 2215-2226.
[J18] F. Marcelloni, “Recognition of olfactory signals based on supervised fuzzy c-means and
k-NN algorithms”, Pattern Recognition Letters, Elsevier, vol. 22, n. 9, 2001, pp. 10071019.
[J19] F. Marcelloni, M. Aksit, “Leaving inconsistency using fuzzy logic”, Information and
Software Technology, Elsevier, vol. 43, n. 12, 2001, pp.725-741.
[J20] B. Lazzerini, F. Marcelloni, “Feature selection based on similarity”, IEE Electronics
Letters, vol. 38, n. 3, 2002, pp. 121-122.
[J21] B. Lazzerini, F. Marcelloni, “Classification based on neural similarity”, IEE Electronics
Letters, vol. 38, n. 15, 2002, pp. 810-812.
[J22] F. Marcelloni, “Feature selection based on a modified fuzzy C-means algorithm with
supervision”, Information Sciences, Elsevier, vol. 151, 2003, pp. 201-226.
[J23] F. Marcelloni, M. Aksit, “Fuzzy logic-based object-oriented methods to reduce quantization error and contextual bias problems in software development”, Fuzzy Sets and
Systems, Elsevier, vol. 145, n. 1, 2004, pp. 57-80.
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[J24] P. Corsini, B. Lazzerini, F. Marcelloni, “A fuzzy relational clustering algorithm based
on a dissimilarity measure extracted from data,” IEEE Transactions on Systems, Man
and Cybernetics Part B, vol. 34, n. 1, 2004, pp. 775-782.
[J25] M. Cococcioni, G. Corsini, B. Lazzerini, F. Marcelloni, “Approaching the ocean color
problem using fuzzy rules”, IEEE Transactions on Systems, Man and Cybernetics Part
B, vol. 34, n. 3, 2004, pp. 1360-1373.
[J26] B. Lazzerini, F. Marcelloni, G. Marola, “Calibration of positron emission tomograph
detector modules using a new neural method,” IEE Electronics Letters, vol. 40, n. 6,
2004, pp.360-361.
[J27] P. Corsini, B. Lazzerini, F. Marcelloni, “A new fuzzy relational clustering algorithm
based on the fuzzy C-means algorithm”, Soft Computing - A Fusion of Foundations,
Methodologies and Applications, Springer, vol. 9, n. 6, 2005, pp. 439-447.
[J28] P. Corsini, B. Lazzerini, F. Marcelloni, “Combining supervised and unsupervised learning for data clustering,” Neural Computing and Applications, Springer Verlag, vol. 15,
n. 3-4, 2006, pp. 289-297.
[J29] P. Corsini, F. Marcelloni, “A fuzzy system for profiling Web portal users from Web
access log”, Journal of Intelligent & Fuzzy Systems, IOS Press, Vol. 17, n. 5, 2006, pp
503-516.
[J30] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “A novel approach to fuzzy clustering
based on a dissimilarity relation extracted from data using a TS System”, Pattern Recognition, Elsevier, Vol. 39, n. 11, 2006, pp. 2077-2091.
[J31] B. Lazzerini, F. Marcelloni, “A hierarchical fuzzy clustering-based system to create user
profiles”, Soft Computing - A Fusion of Foundations, Methodologies and Applications,
Springer Verlag, Vol. 11, n. 2, 2007, pp. 157-168.
[J32] M. Cococcioni, B. Lazzerini, F. Marcelloni, “Estimating the concentration of optically
active constituents of sea water by Takagi-Sugeno models with quadratic rule consequents”, Pattern Recognition, Elsevier, Vol. 40, n. 10, 2007, pp. 2846-2860.
[J33] M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, “A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems,” Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Verlag,
Vol. 11, n. 11, 2007, pp. 1013-1031.
[J34] A. Bechini, M.G.C.A Cimino, F. Marcelloni, A. Tomasi, “Patterns and technologies for
enabling supply chain traceability through collaborative e-business,” Information and
Software Technology, Elsevier, Vol. 50, n. 4, 2008, 342-359.
[J35] S. Croce, F. Marcelloni, M. Vecchio, “Reducing power consumption in wireless sensor
networks using a novel approach to data aggregation,” The Computer Journal, Oxford
University Press, Vol. 51, n. 2, 2008, pp. 227-239.
[J36] A. Botta, B. Lazzerini, F. Marcelloni, “Context adaptation of Mamdani fuzzy rule-based
systems”, International Journal of Intelligent Systems, Wiley, Vol. 23, No. 4, 2008, pp.
397-418.
[J37] F. Marcelloni, M. Vecchio, “A simple algorithm for data compression in wireless sensor
networks”, IEEE Communications Letters, IEEE, Vol. 12, n. 6, 2008, pp. 411-413.
[J38] M. Cococcioni, G. Corsini, B. Lazzerini, F. Marcelloni, “Solving the ocean color inverse
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problem by using evolutionary multi-objective optimization of neuro-fuzzy systems”,
International Journal of Knowledge-Based and Intelligent Engineering Systems, IOS
Press, Vol. 12, N. 5-6, 2008, pp. 339-355.
[J39] A. Botta, B. Lazzerini, F. Marcelloni, D. Stefanescu, “Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability
index”, Soft Computing - A Fusion of Foundations, Methodologies and Applications,
Springer Verlag, Vol. 13, N. 5, 2009, pp. 437-449.
[J40] M.G.C.A. Cimino, W. Pedrycz, B. Lazzerini, F. Marcelloni, “Using multilayer perceptrons as receptive fields in the design of neural networks”, Neurocomputing, Elsevier,
Vol. 72, 2009, pp. 2536-2548.
[J41] F. Marcelloni, Germano Resconi, Pietro Ducange, “Morphogenetic approach to system
identification”, International Journal of Intelligent Systems, Wiley, Vol. 24, 2009, pp.
955-975.
[J42] F. Marcelloni, M. Vecchio, “An efficient lossless compression algorithm for tiny nodes
of monitoring wireless sensor networks”, The Computer Journal, Oxford University
Press, Vol. 52, N. 8, 2009, pp. 969-987.
[J43] R. Alcalà, P. Ducange, F. Herrera, B. Lazzerini and F. Marcelloni, “A multi-objective
evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy rulebased systems”, IEEE Transactions on Fuzzy Systems, Vol. 17, No. 5, 2009, pp. 11061122.
[J44] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Learning Concurrently Partition
Granularities and Rule Bases of Mamdani Fuzzy Systems in a Multi-Objective Evolutionary Framework”, International Journal of Approximate Reasoning, Elservier, Vol.
50, No. 7, 2009, pp. 1066-1080.
[J45] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Multi-objective Evolutionary
Learning of Granularity, Membership Function Parameters and Rules of Mamdani
Fuzzy Systems”, Evolutionary Intelligence, Springer, vol. 2, N. 1-2, 2009, pp. 21-37.
[J46] P. Ducange, B. Lazzerini, F. Marcelloni, “Multi-objective Genetic Fuzzy Classifiers for
Imbalanced and Cost-sensitive Datasets”, Soft Computing - A Fusion of Foundations,
Methodologies and Applications, Springer, vol. 14, N. 10, 2010, pp. 713-728.
[J47] B. Lazzerini, F. Marcelloni, M. Vecchio, “A Multi-Objective Evolutionary Approach to
Image Quality/Compression Trade-Off in JPEG Baseline Algorithm”, Applied Soft
Computing, Elsevier, vol. 10, 2010, pp. 548-561.
[J48] F. Marcelloni, M. Vecchio, “Enabling Energy-Efficient and Lossy-Aware Data Compression in Wireless Sensor Networks By Multi-Objective Evolutionary Optimization”,
Information Sciences, vol. 180, pp. 1924-1941, 2010.
[J49] A. Ciaramella, M.G.C.A. Cimino, B. Lazzerini and F. Marcelloni, “A Situation-Aware
Resource Recommender based on Fuzzy and Semantic Web Rules,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, World Scientific, Vol. 18,
N. 4, 2010, pp. 411-430.
[J50] F. Marcelloni, M. Vecchio, “A Two-Objective Evolutionary Approach to Design Lossy
Compression Algorithms for Tiny Nodes of Wireless Sensor Networks”, Evolutionary
Intelligence, Springer, Vol. 3, N. 3-4, 2010, pp. 137-153.
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[J51] G. Bombara, M. Cococcioni, B. Lazzerini, F. Marcelloni, “S-NNLS: an Efficient NonNegative Least Squares Algorithm for Sequential Data”, International Journal for Numerical Methods in Biomedical Engineering, Wiley, vol. 27, N. 5, 2011, pp. 770-773.
[J52] M. Cococcioni, B. Lazzerini, F. Marcelloni, “On Reducing Computational Overhead in
Multi-Objective Genetic Takagi-Sugeno Fuzzy Systems”, Applied Soft Computing,
Elsevier, Vol. 11, N. 1, 2011, pp. 675-688.
[J53] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Learning Concurrently Data
and Rule Bases of Mamdani Fuzzy Rule-based Systems by Exploiting a Novel Interpretability Index,” Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer, Vol. 15, 2011, pp. 1981-1998.
[J54] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Learning Knowledge Bases of
Multi-Objective Evolutionary Fuzzy Systems by Simultaneously Optimizing Accuracy,
Complexity and Partition Integrity”, Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer, Vol. 15, N. 12, 2011, pp. 2335-2354.
[J55] M.G.C.A. Cimino, F. Marcelloni, “Autonomic Tracing of Production Processes with
Mobile and Agent-based Computing”, Information Sciences, Elsevier, Vol. 181, 2011,
pp. 935-953.
[J56] M. Antonelli, M. Cococcioni, B. Lazzerini, F. Marcelloni, “Computer-Aided Detection
of Lung Nodules based on Decision Fusion Techniques,” Pattern Analysis and Applications, Vol. 14, 2011, pp. 295-310.
[J57] M. Vecchio, R. López Valcarce, F. Marcelloni, “A Two-Objective Evolutionary Approach based on Topological Constraints for Node Localization in Wireless Sensor Networks”, Applied Soft Computing, Elsevier, Vol. 12, N. 7, 2012, pp. 1891-1901.
[J58] M. Antonelli, P. Ducange, F. Marcelloni, “Genetic Training Instance Selection in MultiObjective Evolutionary Fuzzy Systems: A Co-evolutionary Approach,” IEEE Transactions on Fuzzy Systems, Vol. 20, N. 2, 2012, pp. 276-290.
[J59] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, A. Ciaramella, “An Adaptive RuleBased Approach for Managing Situation-Awareness,” Expert Systems with Applications, Elsevier, Vol. 39, N. 12, 2012, pp. 10796-10811.
[J60] M.G.C.A. Cimino, F. Marcelloni, “An Efficient Model-Based Methodology for Developing Device-Independent Mobile Applications,” Journal of Systems Architecture,
Elsevier, Vol. 58, 2012, pp. 286-304.
[J61] G. Castellano, M.G.C.A. Cimino, A.M. Fanelli, B. Lazzerini, F. Marcelloni, M.A.
Torsello, “A Collaborative Situation-Aware Scheme based on an Emergent Paradigm
for Mobile Resource Recommenders,” Journal of Ambient Intelligence and Humanized
Computing, Springer, Vol. 4, N. 4, 2013, pp. 421-437, DOI: 10.1007/s12652-012-0126y.
[J62] M. Antonelli, P. Ducange, F. Marcelloni, "An Efficient Multi-Objective Evolutionary
Fuzzy System for Regression Problems", International Journal of Approximate Reasoning, Elsevier, Vol. 54, N. 9, 2013, pp. 1434-1451.
[J63] M. Fazzolari, B. Giglio, R. Alcalà, F. Marcelloni, F. Herrera, “A study on the Application of Instance Selection Techniques in Genetic Fuzzy Rule-Based Classification Systems: Accuracy-Complexity Trade-Off”, Knowledge-Based Systems, Vol. 54, 2013, pp.
32-41.
Page 44 of 55
[J64] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, W. Pedrycz, “Genetic Interval Neural
Networks for Granular Data Regression”, Information Sciences, Vol. 257, 2014, pp.
313-330.
[J65] G. Castellano, M.G.C.A. Cimino, A.M. Fanelli, B. Lazzerini, F. Marcelloni, M.A.
Torsello, "A Multi-agent System for Enabling Collaborative Situation Awareness via
Position-based Stigmergy and Neuro-fuzzy Learning", Neurocomputing, Elsevier, Vol.
135, 2014, pp. 86-97.
[J66] M. Vecchio, R. Giaffreda, F. Marcelloni, “Adaptive Lossless Entropy Compressors for
Tiny IoT Devices”, IEEE Transactions on Wireless Communications, Vol. 13, N. 2,
2014, pp. 1088-1100.
[J67] P. Ducange, F. Marcelloni, M. Antonelli, “A Novel Approach based on Finite State Machines with Fuzzy Transitions for Non-Intrusive Home Appliance Monitoring,” IEEE
Transactions on Industrial Informatics, Vol. 10, N. 2, 2014, pp. 1185-1197.
[J68] M. Antonelli, P. Ducange, F. Marcelloni, “An Experimental Study on Evolutionary
Fuzzy Classifiers Designed for Managing Imbalanced Datasets,” Neurocomputing,
Elsevier, Vol. 146, pp. 125-136.
[J69] M. Antonelli, P. Ducange, F. Marcelloni, “A Fast and Efficient Multi-Objective Evolutionary Learning Scheme for Fuzzy Rule-based Classifiers,” Information Sciences,
Elsevier, Vol. 283, pp. 36-54.
[J70] G. Anastasi, P. Bruschi, F. Marcelloni, “‘U-Sense’, A Cooperative Sensing System for
Monitoring Air Quality in Urban Areas”, ERCIM News, ISSN 0926-4981, July 2014,
No. 98, pp.34-35.
[J71] M. Antonelli, P. Ducange, F. Marcelloni, A. Segatori, “A Novel Associative Classification Model based on a Fuzzy Frequent Pattern Mining Algorithm,” Expert Systems with
Applications, Elsevier, Vol. 42, 2015, pp. 2086-2097.
[J72] E. D’Andrea, P. Ducange, B. Lazzerini, F. Marcelloni, “Real-Time Detection of Traffic
from Twitter Stream Analysis,” IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 4, 2015, DOI 10.1109/TITS.2015.2404431, pp. 2269-2283.
[J73] M. Antonelli, P. Ducange, F. Marcelloni, A. Segatori, “On the Influence of Feature Selection in Fuzzy Rule-based Regression Model Generation,” Information Sciences,
Elsevier, Vol. 329, 2016, DOI:10.1016/j.ins.2015.09.045, pp. 649-669.
[J74] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Multi-Objective Evolutionary
Design of Granular Rule-based Classifiers,” Granular Computing, Elsevier, accepted
for publication.
[J75] A. Bechini, F. Marcelloni, A. Segatori, “A MapReduce Solution for Associative Classification of Big Data,” Information Sciences, Elsevier, Vol. 332, 2016, DOI:
10.1016/j.ins.2015.10.041, pp. 33-55.
[J76] M. Antonelli, D. Bernardo, H. Hagras, F. Marcelloni, “Multi-Objective Evolutionary
Optimization of Type-2 Fuzzy Rule-based Systems for Financial Data Classification,”
IEEE Transactions on Fuzzy Systems, accepted with minor revision.
Book chapters
[BC1]
M. Aksit, B. Tekinerdogan, F. Marcelloni, L. Bergmans, “Deriving frameworks from
Page 45 of 55
domain knowledge”, in: Building Application Frameworks: Object-Oriented Foundations of Framework Design, M. E. Fayad, D. C. Schmidt e R E. Johnson, Eds., John
Wiley & Sons, Inc., 1999, pp. 169-198.
[BC2]
B. Lazzerini, F. Marcelloni, L.M. Reyneri, “A neuro-fuzzy system for off-line recognition of handwritten texts”, in: Recent Research Developments in Pattern Recognition, Vol. 1, Transworld Research Network, Kerala, India, 2000, pp. 199-218.
[BC3]
G. Tselentis, F. Marcelloni, T. Martin, L.Sensi, “Odour Classification based on Computational Intelligence Techniques”, in: Advances in Computational Intelligence and
Learning, Methods and Applications, H-J. Zimmermann, G. Tselentis, M. van
Someren, G. Dounias, Eds., International Series in Intelligent Technologies, Kluwer
Academic Publishers, pp. 383-399, 2002.
[BC4]
F. Marcelloni, M.Aksit, “Automating Software Development Process Using Fuzzy
Logic”, in: Soft Computing in Software Engineering, E. Damiani, L. Jain, M. Madravio (Eds), Springer-Verlag, Collana: Studies in Fuzziness and Soft Computing, Vo.
159, pp. 97-124, 2004.
[BC5]
M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Fuzzy clustering based on dissimilarity relations extracted from data”, J. V. de Oliveira, W. Pedrycz (Eds), Advances in
Fuzzy Clustering and Its Applications, John Wiley and Sons, Chichester, England,
2007, pp. 265-283.
[BC6]
M. Cococcioni, B. Lazzerini, F. Marcelloni, “Towards Efficient Multi-objective Genetic Takagi-Sugeno Fuzzy Systems for High Dimensional Problems”, Computational
Intelligence in Expensive Optimization Problems, Springer Series Studies in Evolutionary Learning and Optimization (ELO), Springer-Verlag, 2010, pp. 397-422.
[BC7]
F. Marcelloni, M. Vecchio, “Enabling compression in tiny wireless sensor nodes”,
Wireless Sensor Networks, Suraiya Tarannum (Ed.), ISBN: 978-953-307-325-5, ITech Education and Publishing KG, Kirchengasse 43/3, 1070 Vienna, Austria, EU,
2011, pp. 1-20.
[BC8]
M.G.C.A. Cimino, F. Marcelloni, “Enabling traceability in the wine supply chain”,
Methodologies and Technologies for Networked Enterprises, Springer Service Science
series, New York, NY, vol. 7200, 2012, pp. 433-449.
[BC9]
F. Marcelloni, M. Vecchio, “Exploiting Multi-Objective Evolutionary Algorithms for
Designing Energy-efficient Solutions to Data Compression and Node Localization in
Wireless Sensor Networks,” in Evolutionary based solutions for green computing,
Studies in Computational Intelligence, Springer, New York, NY, Vol. 432, 2013, pp.
227-255.
[BC10] A. Bechini, F. Marcelloni, A. Segatori, “Low-Effort Support to Efficient Urban Parking in a Smarty City Perspective”, in Advances onto the Internet of Things, Advances
in Intelligent Systems and Computing Series, Springer, Vol. 260, 2014, pp 233-252.
[BC11] M. Antonelli, P. Ducange, F. Marcelloni, “Multi-objective Evolutionary Design of
Fuzzy Rule-based Systems”, Handbook on Computational Intelligence, Edited by P.
Angelov, World Scientific, 2016, pp. 627-662.
National Journals
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[NJ1] G. Frosini, B. Lazzerini, F. Marcelloni, “Un sistema esperto per la gestione degli allarmi”, Rivista di Informatica, Vol. XXIV, N. 1, January-March 1994, pp. 43-60.
[NJ2] B. Lazzerini, F. Marcelloni, L.M. Reyneri, “Integrazione di tecniche neuronali e di analisi del contesto per il riconoscimento automatico del testo manoscritto”, Rivista di Informatica, Vol. XXVIII, N. 1, January-April, 1998, pp. 55-73.
Books
[B1] G. Frosini, F. Marcelloni, Regole di corrispondenza Assembler-C++, ETS, 1999.
[B2] G. Frosini, F. Marcelloni, Calcolatori Elettronici - Volume III - Regole di Corrispondenza
tra C e Assembler, 2002.
Chapters in National Books
[CLI1] M. Cococcioni, G. Corsini, B. Lazzerini, F. Marcelloni, “On the use of neural networks
and fuzzy rules in the ocean colour analysis”, in Analysis and Classification of Remotely
Sensed Hyperspectral Images, pp. 89-107, ETS, Pisa, 2004.
Monographs
[M1] F. Marcelloni, Guida all’uso di Advisor-2, Servizio Editoriale Universitario di Pisa,
Pisa, Gennaio 1995.
[M2] P. Corsini, L. De Dosso, B. Lazzerini, F. Marcelloni, “Note sugli insiemi Fuzzy e sugli
algoritmi di Clustering”, Servizio Editoriale Universitario di Pisa, Pisa, Gennaio 2003.
[M3]
M.G.C.A. Cimino, F. Marcelloni, "INNO.PRO.MODA: innovazione progettazione
qualità e tracciabilità per il sistema moda", ed. Pacini, Pisa 2008.
[M4] M.G.C.A. Cimino, F. Consigli, C. Di Sacco, R. Giannotti, P. Lanari, F. Marcelloni,
"TRA.S.P: tracce sulla pelle", ed. La Lastra, Firenze 2009.
International Conferences
[C1] G. Frosini, B. Lazzerini, F. Marcelloni, “A tool for building expert systems which carry
out academic exams”, Proceedings of AI-ED 93 World Conference on Artificial Intelligence in Education, Edinburgh, Scotland, 23-27 August 1993, pp. 298-305.
[C2] G. Frosini, B. Lazzerini, F. Marcelloni, “Processing alarms with a rule-based expert system”, Proceedings of EWAIC '93 East-West Conference on Artificial Intelligence, Moscow, Russia, 7-9 September 1993, pp. 268-272.
[C3] A. Belkhelladi, B. Lazzerini, F. Marcelloni, “A new approach to modularization of large
object-oriented systems”, Proceeding of Joint Modular Languages Conference, Ulm,
Germany, 28-30 September 1994, pp. 421-430.
[C4] F. Marcelloni, A. Maggiore, “The molecule-oriented language MOL”, Proceeding of
Gronics ‘95, Groningen, Netherlands, 24 February 1995, pp. 11-18.
[C5] A. Belkhelladi, B. Lazzerini, A. Maggiore, F. Marcelloni, “Improving reusability in object-oriented programming: The Molecole”, Proceedings of Thirteenth IASTED Int.
Conf. on Applied Informatics, Innsbruck, Austria, 21-23 February 1995, pp. 421-424.
[C6] A. Belkhelladi, B. Lazzerini, A. Maggiore, F. Marcelloni, “Molecule-oriented design”, in:
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New Computing Techniques in Physics Research IV, World Scientific, Proceedings of
the Fourth International Workshop on Software Engineering Artificial Intelligence and
Expert Systems for High Energy and Nuclear Physics, Pisa, Italy, 3-8 April, 1995, pp.
95-100.
[C7] A. Belkhelladi, B. Lazzerini, A. Maggiore, F. Marcelloni, “Molecules as buiding blocks”,
Proceedings of EUROMICRO 95, Como, Italy, 4-7 September 1995, IEEE Press, pp.
564-571.
[C8] F. Marcelloni, “On inferring reasonable conclusions for fuzzy reasoning with multiple
rules”, IPMU ‘96 - Information Processing and Management of Uncertainty in
Knowledge-Based Systems, Granada, Spain, 1-5 July, 1996, pp. 1351-1356.
[C9] B. Lazzerini, F. Marcelloni, “Reasonable conclusions in fuzzy reasoning”, IEEE Eighth
International Conference on Tools for Artificial Intelligence, Toulouse, France, 16-19
November 1996, IEEE Press, pp. 440-442.
[C10] B. Lazzerini, F. Marcelloni, “Improving performance of MISO fuzzy systems”, Second
International ICSC Symposium on fuzzy logic and applications ISFL'97, 12-14 February, 1997, pp. 82-88.
[C11] F. Marcelloni, M. Aksit, “Applying fuzzy logic techniques in object-oriented software
development”, Jyväskylä, Finland, 9 – 13 June, 1997, in: ECOOP’97 Workshop Reader,
Lecture Notes in Computer Science 1357, Springer Verlag, pp. 295-298.
[C12] M. Aksit, F. Marcelloni, B. Tekinerdogan, K. van den Berg, P. van den Broek, “Active
software artifacts”, Jyväskylä, Finland, 9 – 13 June, 1997, in: ECOOP’97 Workshop
Reader, Lecture Notes in Computer Science 1357, Springer Verlag, pp. 307-310.
[C13] G. Frosini, B. Lazzerini, A. Maggiore, F. Marcelloni, “A fuzzy classification based system for handwritten character recognition”, KES’98, Adelaide, Australia, IEEE Press,
21-23 April, 1998, pp. 61-65.
[C14] F. Di Francesco, B. Lazzerini, A. Maggiore, F. Marcelloni, D. De Rossi, “Electronic
nose based on linguistic fuzzy classification”, EUFIT’98, Aachen, Germany, 7-10 September, 1998, pp. 1211-1215.
[C15] D. Dumitrescu, B. Lazzerini, F. Marcelloni, “A fuzzy hierarchical approach to odour
classification”, Workshop on Virtual Intelligence - Dynamic Neural Networks, KTH
Stockholm, Sweden, SPIE Proceedings Series, Vol. 3728, 22-26 June, 1998, pp. 384395.
[C16] B. Lazzerini, F. Marcelloni, G. Dini, F. Failli, “Assembly planning based on genetic
algorithms”, NAFIPS’99, New York, USA, IEEE Press, 10-12 June, 1999, pp. 482-486.
[C17] B. Lazzerini, F. Marcelloni, “Fuzzy classification of handwritten characters”,
NAFIPS’99, New York, USA, IEEE Press, 10-12 June, 1999, pp. 566-570.
[C18] F. Marcelloni, M. Aksit, “Reducing quantization error and contextual bias problems in
software development processes by applying fuzzy logic”, NAFIPS’99, New York,
USA, IEEE Press, 10-12 June, 1999, pp. 268-272.
[C19] B. Lazzerini, A. Maggiore, F. Marcelloni, “A new linguistic fuzzy approach to recognition of olfactory signals”, 1999 International Joint Conference on Neural Networks,
Washington, USA, IEEE Press, 10-16 July, 1999, pp. 3225-3229.
[C20] D. Dumitrescu, B. Lazzerini, F. Marcelloni, “Olfactory signal classification based on
Page 48 of 55
evolutionary programming”, 1999 International Joint Conference on Neural Networks,
Washington, USA, IEEE Press, 10-16 July, 1999, pp. 313-316.
[C21] F. Di Francesco, B. Lazzerini, F. Marcelloni, G. Pioggia, “Sniffer: An electronic nose”,
KES’99, Adelaide, Australia, IEEE Press, 31 August – 1 September, 1999, pp. 195-198.
[C22] G. Tselentis, F. Marcelloni, T. P. Martin, L. Sensi, “Odour classification based on computational intelligence techniques”, COIL’2000 – Symposium on Computational Intelligence and Learning, Chios, Greece, 22-23 June, 2000, pp. 178-189.
[C23] G. Frosini, B. Lazzerini, F. Marcelloni, “A modified fuzzy C-means algorithm for feature selection”, NAFIPS’2000, Atlanta, USA, IEEE Press, 13-15 July, 2000, pp. 148152.
[C24] B. Lazzerini, F. Marcelloni, L.M. Reyneri, “Neuro-fuzzy off-line recognition of handwritten sentences”, KES’2000, Brighton, UK, IEEE Press, 30 August – 1 September,
2000, pp. 440-443.
[C25] A. Cremoncini, F. Di Francesco, B. Lazzerini, F. Marcelloni, T. Martin, S.A.McCoy, L.
Sensi, G. Tselentis, “Electronic noses using "intelligent" processing techniques”, ISOEN
2000, 7th International Symposium on Olfaction & Electronic Nose, Brighton, UK, 2024 July 2000, pp. 94-99.
[C26] F. Marcelloni, “Delaying Inconsistency Resolution Using Fuzzy Logic”, Workshop on
Softcomputing applied to Software Engineering (SCASE-01), Enschede, The Netherlands, 8-9 February, 2001, pp.1-8.
[C27] B. Lazzerini, F. Marcelloni, V. La Rosa, “Combining Context, Statistics and Fuzzy
Modelling for Automatic Recognition of Handwritten Sentences”, KES 2001, Osaka,
Japan, 6-8 September, 2001, pp. 1595-1599.
[C28] M. Cococcioni, G. Frosini, B. Lazzerini, F. Marcelloni, “A new approach to combining
outputs of multiple classifiers”, NAFIPS 2002, New Orleans, USA, IEEE Press, 27-29
June, 2002, pp. 400 –405.
[C29] M. Cococcioni, G. Corsini, M. Diani, R. Grasso, B. Lazzerini, F. Marcelloni, “Automatic extraction of fuzzy rules from Meris data to identify sea water optically active
constituent concentration”, NAFIPS 2002, New Orleans, USA, IEEE Press, 27-29 June,
2002, pp. 546 –551.
[C30] G. Corsini, M. Diani, R. Grasso, B. Lazzerini, F. Marcelloni, M. Cococcioni, “A fuzzy
model for the retrieval of the sea water optically active constituents concentration from
MERIS data”, IGARSS '02, Toronto, Canada, IEEE Press, vol. 1, 24-28 June, 2002, pp.
98-100.
[C31] P. Corsini, B. Lazzerini, F. Marcelloni, “Clustering based on a dissimilarity measure
derived from data”, KES 2002, IOS Press, Crema, Italy, 16-18 September, 2002, pp.
885-889.
[C32] B. Lazzerini, F. Marcelloni, “k-NN algorithm based on Neural Similarity”, IEEE International Conference on Artificial Intelligence Systems, Gelendgik, Black Sea Coast,
Russia, IEEE Computer Press, 5-10 September, 2002, pp. 67-70.
[C33] M. Cococcioni, B. Lazzerini, F. Marcelloni, “Second-order Takagi-Sugeno model to
identify sea water optically active constituent concentrations from Meris data”, International Fuzzy Systems Association Worlds Congress (IFSA 2003), Istanbul, Turkey, 29
Page 49 of 55
June – 2 July, 2003, pp. 208-211.
[C34] P. Corsini, L. De Dosso, B. Lazzerini, F. Marcelloni, “Relational clustering starting from
non-Euclidean dissimilarity relations”, International Fuzzy Systems Association Worlds
Congress (IFSA 2003), Istanbul, Turkey, 29 June – 2 July, 2003, pp. 212-215.
[C35] M. Cococcioni, B. Lazzerini, F. Marcelloni, “An artificial olfactory system for quality
and geographical discrimination of olive oils”, KES 2003, Springer Verlag, University
of Oxford, UK, 3-5 September, 2003, LNAI 2774, pp. 647-653.
[C36] P. Corsini, L. De Dosso, B. Lazzerini, F. Marcelloni, “A system based on a modified
version of the FCM algorithm for profiling Web users from access log”, EUSFLAT
2003, Zittau, Germany, 10-12 September, 2003, pp. 725-729.
[C37] B. Lazzerini, F. Marcelloni, M. Cococcioni, “A system based on hierarchical fuzzy clustering for web users profiling”, IEEE SMC 2003 Proceedings, IEEE, Washington, USA,
5-8 October, 2003, pp. 1995-2000.
[C38] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Relational clustering based on a dissimilarity relation extracted from data by a TS model”, IEEE SMC 2003 Proceedings,
IEEE, Washington, USA, 5-8 October, 2003, pp. 3194-3199.
[C39] B. Lazzerini, F. Marcelloni, G. Marola, S. Galigani, “Neural network-based calibration
of positron emission tomograph detector modules”, ESANN’2004, Bruges, Belgium, 2830 April, 2004, pp. 269-274.
[C40] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “A Novel Approach to Robust Fuzzy
Clustering of Relational Data”, NAFIPS 2004, Banff, Canada, IEEE Press, 27-30 June,
2004, pp. 90-94.
[C41] M. Cococcioni, B. Lazzerini, F. Marcelloni, “A TSK Fuzzy Model for Combining Outputs of Multiple Classifiers”, NAFIPS 2004, Banff, Canada, IEEE Press, 27-30 June,
2004, pp. 871-875.
[C42] M.G.C.A. Cimino, G. Frosini, B. Lazzerini, F. Marcelloni, “On the noise distance in
robust fuzzy C-means”, International Conference On Computational Intelligence, Istanbul, Turkey, 17-19 December 2004, pp. 361-364.
[C43] M. Antonelli, G. Frosini, B. Lazzerini, F. Marcelloni, “Lung nodule detection in CT
scans”, International Conference On Computational Intelligence, Istanbul, Turkey, 1719 December 2004, pp. 365-368.
[C44] A. Bechini, M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, A. Tomasi, “A general
framework for food traceability", The 2005 International Symposium on Applications
and the Internet, IEEE Press, Trento, Italy, 31 January – 4 February, 2005, pp. 366-369.
[C45] M. Antonelli, B. Lazzerini, F. Marcelloni, “Segmentation and reconstruction of the lung
volume in CT images”, 20th Annual ACM Symposium on Applied Computing, ACM
Press, Santa Fe, New Mexico, 13 –17 March, 2005, pp. 255-259.
[C46] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, A. Tomasi, “Cerere: an information system supporting traceability in the food supply chain”, The First IEEE International
Workshop on Service oriented Solutions for Cooperative Organizations (SoS4CO '05),
IEEE CEC 2005, Munich, Germany, 19 July, 2005, pp. 90-98.
[C47] A. Botta, B. Lazzerini, F. Marcelloni, “Integrating service-oriented technologies to support business processes”, The First IEEE International Workshop on Service oriented
Page 50 of 55
Solutions for Cooperative Organizations (SoS4CO '05), IEEE CEC 2005, Munich, Germany, 19 July, 2005, pp. 37-42.
[C48] M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, M. Vecchio, “Identification of
Mamdani Fuzzy Systems based on a Multi-Objective Genetic Algorithm”, AI*IA 2005
Workshop on Evolutionary Computation, Milan, Italy, 20 September, 2005, pp. 1-10.
[C49] M. Cococcioni, P. Guasqui, B. Lazzerini, F. Marcelloni, “Identification of TakagiSugeno Fuzzy Systems based on Multi-Objective Genetic Algorithms”, International
Workshop on Fuzzy Logic and Applications, Crema, Italy, 15-17 September 2005, Lecture Notes in Artificial Intelligence 3849, Springer, pp. 172-177.
[C50] M. Antonelli, G. Frosini, B. Lazzerini, F. Marcelloni “Automated Detection of Pulmonary Nodules in CT Scans”, CIMCA 2005, Vienna, Austria, 28-30 November 2005,
IEEE, vol. 2, pp. 799-803.
[C51] A. Botta, B. Lazzerini, F. Marcelloni, “New Operators for Context Adaptation of
Mamdani Fuzzy Systems”, 7th International FLINS Conference on Applied Artificial
Intelligence, Applied Artificial Intelligence, World Scientific, Genoa, Italy, 29-31 August, 2006, pp. 35-42.
[C52] A. Botta, B. Lazzerini, F. Marcelloni, “Context Adaptation of Mamdani Fuzzy Systems
through New Operators Tuned by a Genetic Algorithm”, IEEE World Congress on Computation Intelligence, IEEE, Vancouver, Canada, 16-21 July, 2006, pp. 7832-7839.
[C53] M. Antonelli, G. Frosini, B. Lazzerini and F. Marcelloni, “A CAD System for Lung
Nodule Detection based on an Anatomical Model and a Fuzzy Neural Network”,
NAFIPS 2006, IEEE, Montreal, Canada, 3-6 June, 2006, pp. 469-474.
[C54] M. Cococcioni, P. Ducange, B. Lazzerini and F. Marcelloni, “A Comparison of MultiObjective Evolutionary Algorithms in Fuzzy Rule-Based Systems Generation”, NAFIPS
2006, IEEE, Montreal, 3-6 June, Canada, 2006, pp. 463-468.
[C55] B. Lazzerini, F. Marcelloni, M. Vecchio, S. Croce, E. Monaldi, “A Fuzzy Approach to Data
Aggregation to Reduce Power Consumption in Wireless Sensor Networks”, NAFIPS
2006, IEEE, Montreal, Canada, 3-6 June, 2006, pp. 457-462.
[C56] A. Botta, B. Lazzerini, F. Marcelloni, D. Stefanescu, “Exploiting fuzzy ordering relations to preserve interpretability in context adaptation of fuzzy systems”, FUZZ-IEEE
2007, IEEE, London, Imperial College, London, UK, 23-26 July, 2007, pp. 1137-1142.
[C57] M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, “Evolutionary multi-objective
optimization of fuzzy rule-based classifiers in the ROC space”, FUZZ-IEEE 2007,
IEEE, London, Imperial College, London, UK, 23-26 July, 2007, pp. 782-787.
[C58] M. Antonelli, M. Cococcioni, G. Frosini, B. Lazzerini, F. Marcelloni, “Modelling a
Team of Radiologists for Lung Nodule Detection in CT Scans”, KES 2007, Lecture
Notes in AI 4692, Springer-Verlag, Vietri sul Mare, Italy, 12-14 September, 2007, pp.
303-310.
[C59] M. Cococcioni, P. Ducange, B. Lazzerini and F. Marcelloni, “A New Multi-Objective
Evolutionary Algorithm based on Convex Hull for Binary Classifier Optimization”,
IEEE Congress on Computational Intelligence (CEC), IEEE, Singapore, 25-28 September, 2007, pp. 3150-3156.
[C60] A. Botta, B. Lazzerini, F. Marcelloni and D. Stefanescu, “A Survey of Approaches to
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Context Adaptation of Fuzzy Systems”, The 10th IASTED International Conference on
Intelligent Systems and Control ISC 2007, Cambridge, Massachusetts, USA, 19 – 21
November, 2007.
[C61] M. Antonelli, M. Cococcioni, B. Lazzerini, F. Marcelloni, D. Stefanescu, “A multi-classifier system for pulmonary nodule classification”, The 21th IEEE International Symposium on Computer-Based Medical Systems, IEEE, Jyväskylä, Finland, 17-19 June,
2008, pp. 587-589.
[C62] A. Botta, P. Ducange, B. Lazzerini, F. Marcelloni, “A Multi-Objective Cooperative Coevolutionary Approach to Mamdani Fuzzy System Generation”, IPMU 2008, Malaga,
Spain, 22-27 June, 2008, pp. 1143-1150.
[C63] P. Ducange, R. Alcalà, F. Herrera, B. Lazzerini and F. Marcelloni, “Knowledge Base
Learning of Linguistic Fuzzy Rule-Based Systems in a Multi-objective Evolutionary
Framework”, The 3rd International Workshop on Hybrid Artificial Intelligence Systems,
Springer, Lecture notes in Computer Science, vol. 5271, Burgos, Spain, 24-26 September, 2008, pp. 747-754.
[C64] M. Cococcioni, L. Foschini, B. Lazzerini, F. Marcelloni, “Complexity Reduction of
Mamdani Fuzzy Systems through Multi-valued Logic Minimization”, IEEE International Conference on Systems, Man, and Cybernetics (SMC 2008), Singapore, IEEE
press, 12-15 October, 2008, pp. 1782-1787.
[C65] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “A Multi-objective Genetic Approach to Concurrently Learn Partition Granularity and Rule Bases of Mamdani Fuzzy
Systems”, 8th International Conference on Hybrid Intelligent Systems, Barcelona,
Spain, IEEE press, 10-12 September, 2008, pp. 278-283.
[C66] M. Cococcioni, B. Lazzerini, F. Marcelloni, “Fast Multiobjective Genetic Rule Learning
Using an Efficient Method for Takagi-Sugeno Fuzzy Systems Identification”, 8th International Conference on Hybrid Intelligent Systems, Barcelona, Spain, IEEE press, 1012 September, 2008, pp. 272-277.
[C67] A. Ciaramella, M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Using BPMN and Tracing for Rapid Business Process Prototyping Environments”, 11th International Conference on Enterprise Information Systems (ICEIS 2009), Milan, Italy, 6-10 May, 2009,
accepted for publication.
[C68] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “A Three-Objective Evolutionary Approach to Generate Mamdani Fuzzy Rule-Based Systems”, 4th International
Conference on Hybrid Artificial Intelligence Systems, Salamanca, Spain, 10-12 June,
2009, Lectures Notes in Computer Science, accepted for publication.
[C69] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Learning Concurrently Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Systems”, IFSA-EUSFLAT 2009, Lisbon, Portugal, 2009, pp. 1033-1038.
[C70] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Exploiting a New Interpretability Index in the Multi-Objective Evolutionary Learning of Mamdani Fuzzy Rule-Based
Systems”, ISDA’09, IEEE, Pisa, Italy, 30 November – 2 December, 2009, pp. 115-120.
[C71] F. Marcelloni, M. Vecchio, “A Multi-objective Evolutionary Approach to Data Compression in Wireless Sensor Networks”, ISDA’09, IEEE, Pisa, Italy, 30 November – 2
December, 2009, pp. 402-407.
Page 52 of 55
[C72] A. Ciaramella, M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Situation-Aware Mobile Service Recommendation with Fuzzy Logic and Semantic Web”, ISDA’09, IEEE,
Pisa, Italy, 30 November – 2 December, 2009, pp. 1037-1042.
[C73] S. Lioba Volpi, M. Antonelli, B. Lazzerini, F. Marcelloni, D.C. Stefanescu, “Segmentation and reconstruction of the lung and the mediastinum volumes in CT images”, IEEE
2nd International Symposium on Applied Sciences in Biomedical and Communication
Technologies (ISABEL), Bratislava, Slovak Republic, November 24 - 27, 2009, pp. 1-6.
[C74] M. Antonelli, P. Ducange and F. Marcelloni, “Exploiting a coevolutionary approach to
concurrently select training instances and learn rule bases of Mamdani fuzzy systems”,
2010 IEEE International Conference on Fuzzy Systems, Barcellona (Spagna), 18-23
July, 2010.
[C75] M. Antonelli, P. Ducange, B. Lazzerini and F. Marcelloni, “Exploiting a Three-Objective Evolutionary Algorithm for Generating Mamdani Fuzzy Rule-Based Systems”,
2010 IEEE International Conference on Fuzzy Systems, Barcellona (Spagna), 18-23
July, 2010.
[C76] A. Ciaramella, M.G.C.A. Cimino, F. Marcelloni, U. Straccia, “Combining Fuzzy Logic
and Semantic Web to Enable Situation-Awareness in Service Recommendation,” 21st
International Conference on Database and Expert Systems Applications (DEXA’10),
Lecture Notes in Computer Science 6261, 30 Agosto – 3 Settembre, 2010, pp. 31-45.
[C77] A. Ciaramella, M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Using Context History
to Personalize a Resource Recommender via a Genetic Algorithm”, ISDA’10, IEEE,
Cairo, Egypt, 29 Novembre – 1 Dicembre, 2010, pp. 965-970.
[C78] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Multi-objective Evolutionary
Generation of Mamdani Fuzzy Rule-Based Systems based on Rule and Condition Selection,” 5th IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems,
Parigi, Francia, 12-15 Aprile, 2011, pp. 47-53.
[C79] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, W. Pedrycz, “Granular Data Regression
with Neural Networks,” 9th International Workshop on Fuzzy Logic and Applications,
Trani, Italia, 29-31 Agosto, Lecture Notes in Computer Science, Vol. 6857, 2011, pp.
172-179.
[C80] P. Ducange, F. Marcelloni, “Multi-objective Evolutionary Fuzzy Systems,” 9th International Workshop on Fuzzy Logic and Applications, Trani, Italia, 29-31 Agosto, Lecture
Notes in Computer Science, Vol. 6857, 2011, pp. 83-90.
[C81] M. Antonelli, P. Ducange, F. Marcelloni, “A New Approach to Handle High Dimensional and Large Datasets in Multi-objective Evolutionary Fuzzy Systems,” 2011 IEEE
International Conference on Fuzzy Systems, Taipei, Taiwan, 27-30 Giugno, 2011, pp.
1286-1293.
[C82] F. Corucci, G. Anastasi, F. Marcelloni, “A WSN-based Testbed for Energy Efficiency
in Buildings”, IEEE Symposium on Computers and Communications, Kerkyra, Corfu,
Greece, 2011, pp. 990-993.
[C83] M. Vecchio, R. López Valcarce, F. Marcelloni, “An Effective Metaheuristic Approach
to Node Localization in Wireless Sensor Networks,” 8th IEEE International Conference
on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2011), Valencia, Spain, Ottobre
17-22, 2011, pp. 143-145.
Page 53 of 55
[C84] M. Vecchio, R. Lopez-Valcarce, and F. Marcelloni, “Solving the node localization problem in WSNs by a two-objective evolutionary algorithm and gradient descent,” 3rd
World Congress on Nature and Biologically Inspired Computing (NaBIC2011), IEEE,
Salamanca, Spain, Ottobre 19-21, 2011, pp. 143-148.
[C85] Anastasi, F. Corucci, F. Marcelloni, “An Intelligent System for Electrical Energy Management in Buildings,” 11th International Conference on Intelligent Systems Design
and Applications, IEEE, Cordoba, Spain, November 22-24, 2011, pp. 702-707.
[C86] M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, G. Castellano, A.M. Fanelli, M.A.
Torsello, “A collaborative situation-aware scheme for mobile service recommendation,”
11th International Conference on Intelligent Systems Design and Applications, IEEE,
Cordoba, Spain, November 22-24, 2011, pp. 130-135.
[C87] M. Vecchio, R. Lopez-Valcarce, and F. Marcelloni, “A study on the application of different two-objective evolutionary algorithms to the node localization problem in wireless sensor networks,” 11th International Conference on Intelligent Systems Design and
Applications, IEEE, Cordoba, Spain, November 22-24, 2011, pp. 1008-1013.
[C88] P. Ducange, M. Fazzolari, F. Marcelloni, B. Lazzerini, “An intelligent system for detecting faults in photovoltaic fields,” 11th International Conference on Intelligent Systems Design and Applications, IEEE, Cordoba, Spain, November 22-24, 2011, pp. 13411346.
[C89] M. Antonelli, P. Ducange, F. Marcelloni, “Multi-objective evolutionary rule and condition selection for designing fuzzy rule-based classifiers,” 2012 IEEE International Conference on Fuzzy Systems, Brisbane, Australia, June 10-15, 2012, pp. 794-800.
[C90] B. Giglio, F. Marcelloni, M. Fazzolari, R. Alcala, F. Herrera, “A case study on the application of instance selection techniques for genetic fuzzy rule-based classifiers,” 2012
IEEE International Conference on Fuzzy Systems, Brisbane, Australia, June 10-15,
2012, pp. 920-927.
[C91] P. Ducange, F. Marcelloni, D. Marinari, “An algorithm based on finite state machines
with fuzzy transitions for non-intrusive load disaggregation,” Second IFIP Conference
on Sustainable Internet and ICT for Sustainability, Pisa, Italia, October 4-5, 2012,
ISBN: 978-3-901882-46-3, pp. 1-5.
[C92] M. Antonelli, P. Ducange, F. Marcelloni, A. Segatori, “Evolutionary Fuzzy Classifiers
for Imbalanced Datasets: An Experimental Comparison”, 2013 IFSA World Congress
and NAFIPS Annual Meeting, Edmonton, Canada, June 24-28, 2013, pp. 13-18.
[C93] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “A CAD System for Detecting
Lung Nodules in CT Scans based on Multi-Objective Evolutionary Fuzzy Classifiers”,
Medical Imaging Using Bio-inspired Soft Computing, Brussels, Belgium, 2013.
[C94] M. Vecchio, S. Sasidharan, F. Marcelloni, R. Giaffreda, “Reconfiguration of Environmental Data Compression Parameters through Cognitive IoT Technologies”, Workshop
on Internet of Things Communications and Technologies (IoT 2013) within the 9th
IEEE International Conference on Wireless and Mobile Computing, Networking and
Communications, Lyon, France, October 7-9, 2013, pp. 141-146.
[C95] A. Bechini, F. Marcelloni, A. Segatori, “A Mobile Application Leveraging QR-Codes
to Support Efficient Urban Parking”, The Third IFIP Conference on Sustainable Internet
and ICT for Sustainability, Palermo, Italy, October 30-31, 2013 (demo paper).
Page 54 of 55
[C96] G. Anastasi, M. Antonelli, A. Bechini, S. Brienza, E. D’Andrea, D. De Guglielmo, P.
Ducange, B. Lazzerini, F. Marcelloni, A. Segatori “Urban and Social Sensing for Sustainable Mobility in Smart Cities”, The Third IFIP Conference on Sustainable Internet
and ICT for Sustainability, Palermo, Italy, October 30-31, 2013 (WIP paper).
[C97] M. Antonelli, P. Ducange, F. Marcelloni, “Feature Selection based on Fuzzy Mutual
Information”, 10th International Workshop on Fuzzy Logic and Applications, WILF
2013, Genoa, Italy, 19-23 November, 2013, Lecture notes in Computer Science, Vol.
8256, pp. 36-43.
[C98] A. D. De Matteis, F. Marcelloni and A. Segatori, “A New Approach to Fuzzy Random
Forest Generation,” 2015 IEEE International Conference on Fuzzy Systems, Istanbul,
Turkey, 3-6 July, 2015, pp. 1-8.
[C99] P. Ducange, F. Marcelloni and A. Segatori, “A MapReduce-based Fuzzy Associative
Classifier for Big Data”, 2015 IEEE International Conference on Fuzzy Systems, Istanbul, Turkey, 3-6 July, 2015, pp. 1-8.
[C100] G. Ciavarrini, F. Marcelloni, A. Vecchio, “Improving Wi-Fi based localization using
external constraints”, 9th International Conference on Next Generation Mobile Applications, Services and Technologies 2015, Cambridge, 9-11 September 2015.
[C101] M. Cococcioni, B. Lazzerini, F. Marcelloni, F. Pistolesi, “Solving the Environmental
Economic Dispatch Problem with Prohibited Operating Zones in Microgrids using
NSGA-II and TOPSIS”, ACM SAC 2016, Pisa, 4-8 April, 2016.
National Conferences
[NC1] G. Frosini, B. Lazzerini, F. Marcelloni, “Un sistema esperto per condurre gli esami di
profitto”, Atti di Didamatica '93, Genova, 14-16 April 1993, pp. 65-79.
[NC2] B. Lazzerini, F. Marcelloni, L.M. Reyneri, E. Rossi, L. Schiuma, “Il Sistema BEATRIX
per il riconoscimento automatico di testi manoscritti”, Atti Congresso Annuale AICA,
Palermo 21-23 September 1994, pp. 1303-1318.
PhD Thesis
[TD1] F. Marcelloni, “Molecule-oriented models and fuzzy logic-based methods in software
development”.
I hereby state that the statements made above are true to the best of my knowledge and belief. For further information, please feel free to contact me.
Pisa, 1 February 2016
Page 55 of 55

Francesco Marcelloni - Dipartimento di Ingegneria dell`Informazione

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