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Paolo Massimo Buscema

Paolo Massimo Buscema

Department of Business, Law, Economics and Consumer Behaviour
Faculty of Communication
Paolo Massimo Buscema

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Computer scientist, expert in Artificial Neural Networks and Adaptive Artificial Systems. President and Director of SEMEION Centro Ricerche di Scienze della Comunicazione, Scientific Institution recognized (D.M. 12 November 1991) by the Italian Ministry of Education, University and Research (MIUR). Adjoint Full Professor at the Department of Mathematics and Statistics, University of Colorado, Denver (CO-USA), Advisory Board Member of the Center for Computational and Mathematical Biology (CCMB), University of Colorado, Denver (CO-USA).

Member of the Editorial Board of several international scientific journals. He has designed and developed new Artificial Intelligence models and algorithms, publishing over 250 scientific papers and 24 books.

He is the inventor of 18 patent ideas, which resulted in 28 international patents filed.

He has been scientific coordinator of several national and international projects as Scientific Director of Semeion, including:

  1. "Analysis for marine vessel route recognition" project, applying innovative machine learning models. European Union Naval Force Mediterranean (EUNAVFOR MED) Operation SOPHIA.
  2. Project "Network Centric Cognitive System", approved in the National Military Research Plan consisting in the study and application of intelligent systems and different models of machine learning to support the process of situation awareness. (Ministry of Defence).
  3. Project "Artificial Adaptive Systems to predict the magnitude of earthquakes", within the Project S3 "Short-term earthquake prediction and preparation", applying machine learning models and artificial neural networks, project funded by the National Institute of Geophysics and Volcanology (INGV).
  4. Italy-US bilateral project "Artificial Adaptive Systems in Medicine", whose Scientific Coordinator for the United States was Prof. Weldon Lodwick of the University Of Colorado, Denver. (Ministry of Foreign Affairs).
  5. European Project SPAN-CIP (Semantic Predictive Algorithm Network for Critical Infrastructure Protection) for the application of Artificial Intelligence systems (artificial neural networks and evolutionary algorithms) to critical infrastructures.
  6. Research program lasting 10 years for the design, study, analysis and verification of intelligent systems and computer procedures based on Artificial Neural Networks, aimed at application in the medical-pharmaceutical field, in the field of Imaging and biomedical instrumentation. (Bracco S.p.A.).

Project "The Metropolitan Police Service Central Drug Trafficking Database: Business Case" commissioned to Semeion by New Scotland Yard - Specialist Crime Directorate (London-UK).

 

MAIN AREAS OF SCIENTIFIC INTEREST

His current areas of scientific interest include:

  1. deep learning and "real deep learning" a methodology that uses a system composed of different types of neural networks collaborating in a set of metareti, to optimize the performance of individual networks;
  2. the analysis of electroencephalographic (EEG) signals in the diagnosis of autism and other disorders, through an original processing model that aims to identify the implicit (underlying) function in a multivariate set of signals, through the reduction of the temporal sequence to a set of spatial invariants.
  3. geographic profiling through a model that assigns meaning to a distribution of points in a space. The model has shown a particular effectiveness in being able to identify the place of origin of some phenomena such as, for example, identify the place of origin of epidemics by drawing the map of spread, starting from the first events that identify the onset of the phenomenon;
  4. the theory of Impossible Worlds, a system that is able to work with different datasets that are not connected to each other;
  5. the Data Matrix theory, an algebraic theory of nonlinear operators using special adaptive algorithms.

 

 

Although IULM University takes all necessary care to ensure the publication of correct, updated and complete information, it is not responsible for the contents of the curricula published online on the Portal www.iulm.it. The holder of this curriculum vitae is the exclusive guarantor and responsible for the correctness and truthfulness of the information contained therein.

LIBRI

  1. Buscema P.M, “L’Arte della Previsione – Intervista sull’Intelligenza Artificiale a cura di Vittorio Capecchi”, Mimesis Edizioni, Milano, 2020.
  2. Buscema M, Massini G, Breda M, Lodwick W A, Newman F, Asadi-Zeydabadi M, Artificial Adaptive Systems Using Auto Contractive Maps - Theory, Applications and Extensions. ISBN 978-3-319-75048-4. Springer, Marzo 2018.
  3. Buscema M, Tastle W. (Eds), Intelligent Data Mining in Law Enforcement Analytics, Springer, Gennaio 2013.
  4. Buscema M, Tastle W. (Ed), Data Mining Applications Using Artificial Adaptive Systems, Springer, 2013.
  5. Buscema M, Marina Ruggieri, Advanced Networks, Algorithms and Modeling for Earthquake Prediction, River Publishers (Eds), 2011.
  6. Buscema M, V. Capecchi, P. Contucci, B. D'Amore (Eds), Applications of Mathematics in Models, Artificial Neural Networks and Arts - Mathematics and Society, Springer, 2010.

ARTICOLI SCIENTIFICI

  1. Amato M, Buscema M, Massini G, Maurelli G, Grossi E, Frigerio B, Ravani A.L, Sansaro D, Daniela Coggi D, Ferrari C, Bartorelli A.L, Veglia F, Tremoli E, e BaldassarreD, Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study, Front. Cardiovasc. Med., 14 October 2021.
  2. Asadi-Zeydabadia M, Buscema M, Lodwick M, Massini G, Della Torre F, Newman F, Analysis of COVID-19 pandemic in USA, using Topological Weighted Centroid, Computers in Biology and Medicine (2021) 104670, Elsevier.
  3. Thomas Langer, Martina Favarato, Riccardo Giudici, Gabriele Bassi, Roberta Garberi, Fabiana Villa1, Hedwige Gay Anna Zeduri Sara Bragagnolo Alberto Molteni Andrea Beretta Matteo Corradin, Mauro Moreno, Chiara Vismara, Carlo Federico Perno, Massimo Buscema, Enzo Grossi, and Roberto Fumagalli, Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) in patients with influenza-like symptoms using only basic clinical data. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine (2020) 28:113. https:// doi.org/10.1186/s13049-020-00808-8
  4. Barbarito L,  Ferilli G, Buscema PM, Gli approcci agli studi di settore: dal modello struttura-comportamento- risultati alle reti neurali, L’industria (ISSN 0019-7416), Fascicolo 1, gennaio-marzo 2020, (doi: 10.1430/97173), Il Mulino – Rivisteweb.
  5. Gitto L, Massini G, Mennini FS, Mento C, Buscema PM,  Affective Symptoms And Postural Abnormalities As Predictors Of Headache: An Application Of Artificial Neural Networks, Neural Network World 1/2020, 1–26.
  6. Buscema M, Asadi-Zeydabadi M, Lodwick W, Nde Nembot A, Bronstein A, Newman F, (2020): Analysis of the Ebola Outbreak in 2014 and 2018 in West Africa and Congo by Using Artificial Adaptive Systems, Applied Artificial Intelligence, 2020, DOI: 10.1080/08839514.2020.1747770.
  7. Buscema M, Grossi E, Massini G, Breda M, Della Torre F, Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems, Computer Methods and Programs in Biomedicine, Elsevier, Marzo 2020. https://doi.org/10.1016/j.cmpb.2020.105401
  8. Bronzi B, Brilli C, Beone GM, Fontanella MC, Ballabio D, Todeschini R, Consonni V, Grisoni F, Parri F, Buscema M, Geographical identification of Chianti red wine based on ICP-MS element composition, Food Chemistry, Elsevier. https://doi.org/10.1016/j.foodchem.2020.126248, 21 Gennaio 2020.
  9. C. D’Amico N, Grossi E, Valbusa G, Rigiroli F, Colombo B, Buscema M, Fazzini D, Ali M, Malasevschi A, Cornalba G, Papa S, A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI, European Radiology Experimental, https://doi.org/10.1186/s41747-019-0131-4, Springer Open, Gennaio 2020.

Friedel MJ, Wilson SR, Close ME, Buscema M, Abraham P, Banasiak L, Comparison of four learning-based methods for predicting groundwater redox status, Journal of Hydrology 580 (2020) 124200

 

 

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