CURRICULUM Francesco Marcelloni - unipi.itinfo.iet.unipi.it/~france/CV/CVMarcelloni.pdfPage 1 of 55 CURRICULUM Francesco Marcelloni CONTACT INFORMATION Francesco Marcelloni Full Professor

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CURRICULUM

Francesco Marcelloni

CONTACT INFORMATION

Francesco Marcelloni 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 Engi-neering 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 at-tend a five-weeks class at the two universities in USA and 30-40 Amer-ican 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 sys-tems, 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 Prin-

cipal Investigator for the Department

Mar 2015 – Mar 2016 “Metodologie e Tecnologie per lo Sviluppo di Servizi Informatici In-novativi per le Smart Cities” (Methodologies and technologies to de-

velop 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 Inves-tigator).

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 soft-ware gestionali” (A process-based method for enterprise software re-

engineering), 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 mod-

elling), 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 sup-ply 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 Tus-cany 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 measu-

ring 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) (Coordi-nator).

Dec. 2008–Dec. 2010 “DAFNE – Diagnosi automatica basata su tecniche di intelligenza computazionale delle cause di perdita di efficienza energetica in im-pianti fotovoltaici” (Automatic diagnosis of the causes of efficiency

loss in photovoltaic energy systems based on computational intelli-

gence 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 in-dustriali” (Analysis of the applicability of wireless sensor networks in

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industrial environment), funded by TD Group S.p.A., Migliarino Pi-sano (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 in-

side 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 In-vestigator).

Jun. 2007–Jul. 2008 “Sviluppo di algoritmi di computational intelligence per l’individua-zione 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 sen-sori” (Development of algorithms for power saving in wireless sensor

networks), funded by TD Group S.p.A., Migliarino Pisano (PI) (Coor-dinator).

Jan. 2006–Dec. 2006 “Group Opportunities Alliance – A network for technological trans-fer”, 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 Pi-sano (PI) (Coordinator).

Jan. 2005-Dec. 2005 “Modelli di Integrazione tra sistemi informativi” (Models for Informa-

tion 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 magaz-zino 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 infor-matici clinico-sanitari” (Methods for managing information system se-

curity), 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 hie-

rarchical 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) (Prin-cipal 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 ridu-zione del drift presente in tali sensori” (Development of a system for

olive oil classification and recognition from signals produced by ol-

factive 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 refer-ence: 600918.

Jul. 2011 – 30 Jun 2014 Project Politer (Polo di Innovazione Tecnologie dell’ICT, delle Tele-comunicazioni 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’ottimiz-zazione delle risorse energetiche in complessi di edifici (Smart Build-ing: An ambient intelligent system for energy consumption optimiza-tion 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 Re-search (MIUR).

May. 2007–Mar. 2008 “GeoMon – Monitoraggio delle opere ingegneristiche e prove geotec-niche 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 Fonda-zione 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 tel-erilevamento” (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 pro-cessing” (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 Tech-nological Research.

Mar 1996–Jun. 1997 “Architetture parallele e algoritmi per reti neuronali e loro applica-zioni” (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 Tech-nological Research (MURST 40%).

Mar. 1993–Mar. 1995 “Sistemi esperti e loro applicazioni” (Expert systems and their appli-

cations), 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 dis-tribuiti” (Conventional and non-conventional architectures for dis-

tributed systems), funded by the Italian Ministry of University and Sci-entific 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 de-tecting 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 evolution-ary 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 opti-mization in smart micro-grid.

MANAGING AND ORGANIZATION ACTIVITY

2008 founds the Competence Centre on Mobile Value Added Services, sup-ported by Softec s.p.a., at the Dipartimento di Ingegneria dell’Informa-zione 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 organises and manages the course (60 credits) entitled “Design and Management of Enterprise Information Systems and e-Commerce Sys-tems”, funded by Tuscany region (75,000 Euros).

2003-2005 organises and manages the course (60 credits) entitled “Design and 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 Uni-versity of Pisa.

WORKSHOP AND SPECIAL SESSION ORGANIZATIONS

22 Nov. 2011 Co-organization of the Workshop “Computational Intelligence for Per-sonalization in Web Content and Service Delivery”, Cordoba, Spain.

30 Nov. 2010 Co-organization of the Workshop “Computational Intelligence for Per-sonalization in Web Content and Service Delivery”, Cairo, Egypt.

29 Nov. 2009 Co-organization of the special session “Designing comprehensible in-telligent 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 non-metric distance measures”, Crema, Italia, within the KES’2002 Confer-ence.

20 July 1998 Co-organization of the workshop “Automating the object-oriented soft-ware development”, Brussels, Belgium, within the ECOOP’98 Confer-ence.

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 Sys-tems, 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, Flor-ence, 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 produc-ers 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 Seman-tic Cities, AI*IA 2014, December 12, 2014.

STEERING COMMITTEE MEMBER Francesco Marcelloni is member of the steering Committee of the ISDA (Intelligent Sys-tems 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 Confer-ence 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 19-23, 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 Pub-lishing 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 Ap-plications (ISDA 2009), International Journal of Uncertainty, Fuzziness and Knowledge-Based 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 Per-vasive 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, Sep-tember 14-17, 2009.

The New Advances on Genetic Fuzzy Systems track of IFSA2009/EUSFLAT09 Confer-ence, 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 Uncer-tainty in Knowledge-Based Systems, June 28 - July 2, 2010, Dortmund (Germany).

The 20th International Conference on Artificial Neural Networks (ICANN 2010), Thes-saloniki, 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), Lon-don, UK, September, 2012.

The 8th IFIP International Conference on Artificial Intelligence Applications and Innova-tions (AIAI 2012), Peninsula of Chalkidiki, Greece, September 27 – 30, 2012.

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The 4th International Workshop on Wireless Sensor Networks Architectures, Deploy-ments and Trends, Istanbul, Turkey, May 7-10, 2012.

The Second International Conference on Advances in Information Mining and Manage-ment (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 Appli-cations, 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 Computa-tional 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 Manage-ment, 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 Innova-tions (AIAI 2013), Paphos, Cyprus, September 26-28, 2013.

The 5th International Conference on Fuzzy Computation Theory and Applications, Al-garve, 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, No-vember 19-22, 2013.

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The 8th International Conference on Soft Computing Models in Industrial and Environ-mental 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, Lis-bon, Portugal, 7-9 January, 2014.

The 3rd International Conference on Pattern Recognition Applications and Methods, An-gers, Loire Valley, France, 6-8 March 2014.

The 5th International Workshop on Wireless Sensor Networks Architectures, Deploy-ments and Trends, Dubai, UAE, March 30 – April 2, 2014.

The Fourth International Conference on Advances in Information Mining and Manage-ment, 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 Environ-mental Applications (SOCO 14), Bilbao, 25-27 June, 2014.

The First Euro-China Conference on Intelligent Data Analysis and Applications (ECC-2014), 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, Lis-bon, 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 Appli-cations, IARIA, Nice, France, March 22-27, 2015.

The Seventh International Conference on Advanced Cognitive Technologies and Appli-cations, 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 Environ-mental 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, Univer-sity of Essex, Colchester (UK), September 24-27, 2015.

The 10th Bio-inspired Computing: Theories and Applications (BIC-TA 2015) Confer-ence, 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) Confer-ence, St. Louis, Missouri, USA, 18-20 May, 2016.

The Eighth International Conference on Advanced Cognitive Technologies and Applica-tions, COGNITIVE 2016, March 20 - 24, 2016 - Rome, Italy.

The Eighth International Conference on Future Computational Technologies and Appli-cations, FUTURE COMPUTING 2016, March 20 - 24, 2016 - Rome, Italy

The Second International Symposium on Intelligent Systems Technologies and Applica-tions (ISTA’16), September 21-24, 2016, Jaipur, India.

The International Conference on Image Analysis and Recognition, ICIAR 2016, July 13-15, 2016 – Póvoa de Varzim, Portugal.

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The30th International Conference on Industrial, Engineering & Other Applications of Ap-plied 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 Pro-posals (TDP), Call 2011-1.

National Projects

In 2011, Francesco Marcelloni has served as referee for projects submitted to the Univer-sity of Trieste in the framework of the call “Young Researcher Support for Scientific Re-search”. From October 2011 Francesco Marcelloni is in the pool of experts commissioned by Filas,

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Società Finanziaria Laziale di Sviluppo (currently Lazio Innova), to assess projects sub-mitted 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 colla-borazione 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 Mul-timedialità e Telecomunicazioni, XVIII ciclo”, University of Florence, It-aly.

25 May 2007 one of the three members of the committee for the final examination of 25 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 exam-ination of the PhD student José Luis Aznarte Mellado, University of Gra-nada, Spain.

29 May 2009 one of the three members of the committee for the final examination of 8 PhD students of the “Dottorato di Ricerca in Ingegneria dell’Informazione, XXI ciclo”, University of Pisa, Italy.

28 Feb 2012 one of the three members of the committee for the final examination of 4 PhD students of the “Dottorato di Ricerca in Informatica, X ciclo”, Uni-versity of Salerno, Italy.

24 Mar 2014 one of the three members of the committee for the final examination of 3 PhD students of the “Dottorato di Ricerca in Ingegneria Informatica, Mul-timedialità e Telecomunicazioni, XXV ciclo”, University of Florence, It-aly.

24 Mar 2014 one of the three members of the committee for the final examination of 3 PhD students of the “Dottorato di Ricerca in Informatica, Sistemi e Tele-comunicazioni, indirizzo in Ingegneria Informatica, Multimedialità e Tel-ecomunicazioni, XXVI ciclo”, University of Florence, Italy.

26 May 2014 one of the three members of the committee for the final examination of 3 PhD students of the “Dottorato di Ricerca in Informatica, XXVI ciclo”,

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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 En-gineering), 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 En-gineering, 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.







Data Mining for Smart Cities (3 credits, Master in Smart Cities), Department of Information Engineering, University of Pisa.


Business Intelligence (6 credits, Corso di Laurea Magistrale in Ingegneria In-formatica per la Gestione d’Azienda), Department of Information Engineering, 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 In-formatica 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 Tele-comunicazioni), 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 In-formatica per la Gestione d’Azienda), Faculty of Engineering, University of 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).




Neural Networks (2 credits, Master di secondo livello in Elettroacustica Subac-quea e Sue Applicazioni), Faculty of Engineering, University of Pisa.






Fundamentals of Computer Architecture (3 credits, Corso di Laurea in

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Ingegneria Informatica), Faculty of Engineering, University of Pisa.

2004-2005 Fundamentals of Computer Science (6 credits, Corso di Laurea in Ingegneria Elettronica), Faculty of Engineering, University of Pisa.

Fundamentals of Computer Architecture (6 credits, Corso di Laurea in Ingegneria Elettronica), Faculty of Engineering, University of Pisa.

Computer Architectures (12 credits, Corso di Laurea in Ingegneria Informat-ica), Faculty of Engineering, University of Pisa.


Fundamentals of Computer Science (6 credits, Corso di Laurea in Ingegneria 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.


Fundamentals of Computer Science (6 credits, Corso di Laurea in Ingegneria Biomedica), Faculty of Engineering, University of Pisa.


2001-2002 Fundamentals of Computer Architecture (6 credits, Corso di Laurea in Ingegneria Elettronica), Faculty of Engineering, University of Pisa.

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 Ingegne-ria Elettronica ed Ingegneria Informatica), Faculty of Engineering, University of Pisa.

Computer Architectures (12 credits, Corso di Laurea in Ingegneria delle Tele-comunicazioni), Faculty of Engineering, University of Pisa.

1999-2000 Computer Architectures (4 credits, Corso di Laurea in Ingegneria Informatica), Faculty of Engineering, University of Pisa.


Knowledge Engineering and Expert Systems (4 credits, Corso di Laurea in Ingegneria Informatica), Faculty of Engineering, University of Pisa.



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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 heu-ristic 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 heuris-tic approaches to multi-objective evolutionary fuzzy systems" for PhD students at the University of Essex (UK).

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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, refer-ring 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-in-creasing 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 composi-tion 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 Sys-tems (FRBSs) based on removing logical redundancies has been proposed. First an FRBS is gen-erated 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 re-duce 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 remov-ing 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 net-works, offers a considerable level of flexibility as the resulting receptive fields are highly diver-sified and capable of adjusting themselves to the characteristics of the locally available experi-mental 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 en-countered 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 associ-ated 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 identifica-tion 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 ap-plied 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 capa-bilities 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 meth-ods proposed in the literature, however, focus on performance maximization and omit to consider

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FRBS interpretability. Only recently, the problem of finding the right trade-off between perfor-mance 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 Pa-reto-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 mu-tation 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 high-dimensional) 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

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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 three-objective evolutionary algorithm to produce a Pareto front approximation composed of fuzzy rule-based 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

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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 input-output 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 Non-Negative 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)M-PAES 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 self-organizing 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 qual-ity of the decoded images are determined by these values. Thus, the correct choice of the quanti-zation tables is crucial to the performance of the JPEG algorithm. In [J47], a two-objective evo-lutionary 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 infrastruc-ture, 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 config-urations is concurrently managed by the algorithm which, whenever requested, outputs the con-figuration 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 experi-mental 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 standard-ized 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 ap-plication 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 service-oriented 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 self-configurability 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 im-portant 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 spe-cific circumstances often requires a considerable effort and rarely brings to a completely satisfac-tory 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]. Sit-uations 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 manage-ment 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 evalu-ated 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 environ-ment 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, man-aged 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 situ-ation 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

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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 multi-class 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 state-of-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, Pro-

ceedings 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, Pro-

ceedings 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 Ap-plications (ISDA 2009), International Journal of Uncertainty, Fuzziness and Knowledge-

Based Systems, World Scientific, Guest Editors’ Introduction, Vol 18, N. 4, 2010, pp. v-vi.

[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 Per-vasive 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 mul-tisensor 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 se-quences 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”, Inter-

national 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 pro-duce 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 char-acters”, 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 us-ing 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 Trans-

actions 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. 1007-1019.

[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 quanti-zation 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 learn-ing 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 Recog-

nition, 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 conse-quents”, Pattern Recognition, Elsevier, Vol. 40, n. 10, 2007, pp. 2846-2860.

[J33] M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, “A Pareto-based multi-objec-tive evolutionary approach to the identification of Mamdani fuzzy systems,” Soft Com-

puting - 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 sys-tems 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 percep-trons 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 rule-based systems”, IEEE Transactions on Fuzzy Systems, Vol. 17, No. 5, 2009, pp. 1106-1122.

[J44] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Learning Concurrently Partition Granularities and Rule Bases of Mamdani Fuzzy Systems in a Multi-Objective Evolu-tionary 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 Com-pression 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 Jour-

nal 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 Non-Negative Least Squares Algorithm for Sequential Data”, International Journal for Nu-

merical 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 Inter-pretability Index,” Soft Computing - A Fusion of Foundations, Methodologies and Ap-

plications, 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, Meth-

odologies 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 Appli-

cations, Vol. 14, 2011, pp. 295-310.

[J57] M. Vecchio, R. López Valcarce, F. Marcelloni, “A Two-Objective Evolutionary Ap-proach based on Topological Constraints for Node Localization in Wireless Sensor Net-works”, Applied Soft Computing, Elsevier, Vol. 12, N. 7, 2012, pp. 1891-1901.

[J58] M. Antonelli, P. Ducange, F. Marcelloni, “Genetic Training Instance Selection in Multi-Objective Evolutionary Fuzzy Systems: A Co-evolutionary Approach,” IEEE Transac-

tions 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 Rule-Based Approach for Managing Situation-Awareness,” Expert Systems with Applica-

tions, Elsevier, Vol. 39, N. 12, 2012, pp. 10796-10811.

[J60] M.G.C.A. Cimino, F. Marcelloni, “An Efficient Model-Based Methodology for Devel-oping 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/s12652-012-0126-y.

[J62] M. Antonelli, P. Ducange, F. Marcelloni, "An Efficient Multi-Objective Evolutionary Fuzzy System for Regression Problems", International Journal of Approximate Reason-

ing, Elsevier, Vol. 54, N. 9, 2013, pp. 1434-1451.

[J63] M. Fazzolari, B. Giglio, R. Alcalà, F. Marcelloni, F. Herrera, “A study on the Applica-tion of Instance Selection Techniques in Genetic Fuzzy Rule-Based Classification Sys-tems: Accuracy-Complexity Trade-Off”, Knowledge-Based Systems, Vol. 54, 2013, pp. 32-41.

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[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 Ma-chines 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 Evolu-tionary 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 Classifica-tion 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 Sys-

tems, 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 Se-lection 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 Classi-fication 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

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domain knowledge”, in: Building Application Frameworks: Object-Oriented Founda-

tions 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 recog-nition of handwritten texts”, in: Recent Research Developments in Pattern Recogni-

tion, Vol. 1, Transworld Research Network, Kerala, India, 2000, pp. 199-218.

[BC3] G. Tselentis, F. Marcelloni, T. Martin, L.Sensi, “Odour Classification based on Com-putational 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. Madra-vio (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 dissimilar-ity 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 Ge-netic Takagi-Sugeno Fuzzy Systems for High Dimensional Problems”, Computational Intelligence in Expensive Optimization Problems, Springer Series Studies in Evolu-tionary 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, I-Tech 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 Park-ing 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 al-larmi”, 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 ana-lisi del contesto per il riconoscimento automatico del testo manoscritto”, Rivista di In-

formatica, 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 Intelli-

gence 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 sys-tem”, Proceedings of EWAIC '93 East-West Conference on Artificial Intelligence, Mos-cow, 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 ob-ject-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 Febru-ary, 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 sys-tem 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 Sep-tember, 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. 384-395.

[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 recogni-tion 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

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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 com-putational intelligence techniques”, COIL’2000 – Symposium on Computational Intelli-

gence 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 fea-ture selection”, NAFIPS’2000, Atlanta, USA, IEEE Press, 13-15 July, 2000, pp. 148-152.

[C24] B. Lazzerini, F. Marcelloni, L.M. Reyneri, “Neuro-fuzzy off-line recognition of hand-written 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, 20-24 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 Nether-lands, 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, “Auto-matic 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 Inter-

national 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”, Interna-

tional Fuzzy Systems Association Worlds Congress (IFSA 2003), Istanbul, Turkey, 29

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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 clus-tering 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 dis-similarity 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, 28-30 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 Out-puts 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, Is-tanbul, 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, 17-19 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 sys-tem 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 sup-port business processes”, The First IEEE International Workshop on Service oriented

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Solutions for Cooperative Organizations (SoS4CO '05), IEEE CEC 2005, Munich, Ger-many, 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 Takagi-Sugeno Fuzzy Systems based on Multi-Objective Genetic Algorithms”, International

Workshop on Fuzzy Logic and Applications, Crema, Italy, 15-17 September 2005, Lec-ture Notes in Artificial Intelligence 3849, Springer, pp. 172-177.

[C50] M. Antonelli, G. Frosini, B. Lazzerini, F. Marcelloni “Automated Detection of Pulmo-nary 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 Au-gust, 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 Com-

putation 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 Multi-Objective 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 rela-tions 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 Septem-ber, 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-clas-sifier system for pulmonary nodule classification”, The 21th IEEE International Sym-

posium 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 Co-evolutionary 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 Septem-ber, 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 Interna-

tional 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 Ap-proach 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 Inter-

national Conference on Hybrid Intelligent Systems, Barcelona, Spain, IEEE press, 10-12 September, 2008, pp. 272-277.

[C67] A. Ciaramella, M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Using BPMN and Trac-ing for Rapid Business Process Prototyping Environments”, 11th International Confer-

ence 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 Evolution-ary 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 Granu-larity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Sys-tems”, IFSA-EUSFLAT 2009, Lisbon, Portugal, 2009, pp. 1033-1038.

[C70] M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, “Exploiting a New Interpretabil-ity 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 Com-pression in Wireless Sensor Networks”, ISDA’09, IEEE, Pisa, Italy, 30 November – 2 December, 2009, pp. 402-407.

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[C72] A. Ciaramella, M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni, “Situation-Aware Mo-bile 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, “Segmen-tation 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-Objec-tive 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 Se-lection,” 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 Interna-

tional 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 Dimen-sional 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.

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[C84] M. Vecchio, R. Lopez-Valcarce, and F. Marcelloni, “Solving the node localization prob-lem 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 Man-agement 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 dif-ferent two-objective evolutionary algorithms to the node localization problem in wire-less 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 de-tecting faults in photovoltaic fields,” 11th International Conference on Intelligent Sys-

tems Design and Applications, IEEE, Cordoba, Spain, November 22-24, 2011, pp. 1341-1346.

[C89] M. Antonelli, P. Ducange, F. Marcelloni, “Multi-objective evolutionary rule and condi-tion selection for designing fuzzy rule-based classifiers,” 2012 IEEE International Con-

ference 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 ap-plication 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 Environ-mental 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).

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[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 Sus-tainable 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, Istan-bul, 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 Appli-cations, 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 infor-mation, please feel free to contact me.

Pisa, 1 February 2016

Francesco Marcelloni

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