Skip to main content

Abstract

This chapter focuses on Auto-Contractive Maps, which is a particularly useful ANN. Moreover, the relationship between Auto-Contractive Map (Auto-CM), which is the main topic of this monograph, its relationship to other ANNs and some illustrative example applications are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Reference

  1. Buscema, M., C. Helgason, and E. Grossi. 2008. Auto contractive maps, H function and maximally regular graph: Theory and applications. In Special session on “Artificial Adaptive Systems in Medicine: Applications in the Real World, NAFIPS 2008 (IEEE)”, New York, May 19–22, 2008.

    Google Scholar 

General References

  • Buscema, M. (ed.). 2007. Squashing Theory and Contractive Map Network. Semeion Technical Paper #32, Rome.

    Google Scholar 

  • Buscema, M. 2007. A novel adapting mapping method for emergent properties discovery in data bases: Experience in medical field. In 2007 IEEE International Conference on Systems, Man and Cybernetics (SMC 2007), Montreal, Canada, Octobre 7–10, 2007.

    Google Scholar 

  • Buscema, M., and E. Grossi. 2008. The Semantic Connectivity Map: An Adapting Self-organizing Knowledge Discovery Method in Data Bases. Experience in Gastro-oesophageal Reflux Disease. International Journal of Data Mining and Bioinformatics 2 (4).

    Google Scholar 

  • Buscema, M., E. Grossi, D. Snowdon, and P. Antuono. 2008. Auto-contractive Maps: An Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease. Current Alzheimer Research 5: 481–498.

    Google Scholar 

  • Licastro, F., E. Porcellini, M. Chiappelli, P. Forti, M. Buscema, et al. 2010. Multivariable Network Associated with Cognitive Decline and Dementia. International Neurobiology of Aging 1 (2): 257–269.

    Google Scholar 

  • Buscema, M., and E. Grossi (eds.). 2009. Artificial Adaptive Systems in Medicine, 25–47. Bentham e-books.

    Google Scholar 

  • Buscema, Massimo, and Pier L. Sacco. 2010. Auto-contractive Maps, the H Function, and the Maximally Regular Graph (MRG): A New Methodology for Data Mining. In Applications of Mathematics in Models, Artificial Neural Networks and Arts, ed. V. Capecchi, et al. Berlin: Springer.

    Google Scholar 

  • Grossi, Enzo, Giorgio Tavano Blessi, Pier Luigi Sacco, and Massimo Buscema. 2011. The Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project. Journal of Happiness Studies (Springer).

    Google Scholar 

  • Licastro, Federico, Elisa Porcellini, Paola Forti, Massimo Buscema, Ilaria Carbone, Giovanni Ravaglia, and Enzo Grossi. 2010. Multi Factorial Interactions in the Pathogenesis Pathway of Alzheimer’s Disease: A New Risk Charts for Prevention of Dementia. Immunity & Ageing 7 (Suppl 1): S4.

    Google Scholar 

  • Buscema, M., F. Newman, E. Grossi, and W. Tastle. 2010. Application of Adaptive Systems Methodology to Radiotherapy. In NAFIP, Toronto, Canada, July 12–14, 2010.

    Google Scholar 

  • Eller-Vainicher, C., V.V. Zhukouskaya, Y.V. Tolkachev, S.S. Koritko, E. Cairoli, E. Grossi, P. Beck-Peccoz, I. Chiodini, and A.P. Shepelkevich. 2011. Low Bone Mineral Density and Its Predictors in Type 1 Diabetic Patients Evaluated by the Classic Statistics and Artificial Neural Network Analysis. Diabetes Care 1–6.

    Google Scholar 

  • Gomiero, T., L. Croce, E. Grossi, L. De Vreese, M. Buscema, U. Mantesso, and E. De Bastiani. 2011. A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems. US-China Education Review A 2: 196–207.

    Google Scholar 

  • Buscema, M., S. Penco, and E. Grossi. 2012. A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph Theory. Neurology Research International 2012: 13, Article ID 478560.

    Google Scholar 

  • Grossi, E., A. Compare, and M. Buscema. 2012. The Concept of Individual Semantic Maps in Clinical Psychology: A Feasibility Study on a New Paradigm. Quality & Quantity International Journal of Methodology, August 04th, 2012.

    Google Scholar 

  • Coppedè, F., E. Grossi, M. Buscema, and L. Migliore. 2013. Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals. PLOS ONE 8 (8): e74012, 1–11.

    Google Scholar 

  • Street, M.E., M. Buscema, A. Smerieri, L. Montanini, and E. Grossi. 2013. Artificial Neural Networks, and Evolutionary Algorithms as a Systems Biology Approach to a Data-base on Fetal Growth Restriction. Progress in Biophysics and Molecular Biology, 1–6.

    Google Scholar 

  • Compare, A., E. Grossi, M. Buscema, C. Zarbo, X. Mao, F. Faletra, E. Pasotti, T. Moccetti, P.M.C. Mommersteeg, and A. Auricchio. 2013. Combining Personality Traits with Traditional Risk Factors for Coronary Stenosis: An Artificial Neural Networks Solution in Patients with Computed Tomography Detected Coronary Artery Disease. Cardiovascular Psychiatry and Neurology 2013: 9, Article ID 814967 (Hindawi Publishing Corporation).

    Google Scholar 

  • Buscema, M., V. Consonni, D. Ballabio, A. Mauri, G. Massini, M. Breda, and R. Todeschini. 2014. K-CM: A New Artificial Neural Network. Application to Supervised Pattern Recognition. Chemometrics and Intelligent Laboratory Systems 138: 110–119.

    Google Scholar 

  • Buscema, M., G. Massini, and G. Maurelli. 2014. Artificial Neural Networks: An Overview and Their Use in the Analysis of the AMPHORA-3 Dataset. Substance Use & Misuse, Early Online: 1–14.

    Google Scholar 

  • Gironi, M., B. Borgiani, E. Farina, E. Mariani, C. Cursano, M. Alberoni, R. Nemni, G. Comi, M. Buscema, R. Furlan, and Enzo Grossi. 2015. A Global Immune Deficit in Alzheimer’s Disease and Mild Cognitive Impairment Disclosed by a Novel Data Mining Process. Journal of Alzheimer’s Disease 43: 1199–1213.

    Google Scholar 

  • Drenos, F., E. Grossi, M. Buscema, and S.E. Humphries. 2015. Networks in Coronary Heart Disease Genetics as a Step Towards Systems Epidemiology. PLoS ONE 10 (5): e0125876. https://doi.org/10.1371/journal.pone.0125876.

  • Coppedè, F., E. Grossi, A. Lopomo, R. Spisni, M. Buscema, and Lucia Migliore. 2015. Application of Artificial Neural Networks to Link Genetic and Environmental Factors to DNA Methylation in Colorectal Cancer. Epigenomics 7 (2): 175–186.

    Google Scholar 

  • Narzisi, A., F. Muratori, M. Buscema, S. Calderoni, and E. Grossi. 2015. Outcome Predictors in Autism Spectrum Disorders Preschoolers Undergoing Treatment as Usual: Insights from an Observational Study Using Artificial Neural Networks. Neuropsychiatric Disease and Treatment 11: 1587–1599.

    Google Scholar 

  • Buscema, M., E. Grossi, L. Montanini, M.E. Street. 2015. Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles. PLoS ONE 10 (7): e0126020. https://doi.org/10.1371/journal.

  • Buscema, Paolo Massimo, Lara Gitto, Simone Russo, Andrea Marcellusi, Federico Fiori, Guido Maurelli, Giulia Massini, and Francesco Saverio Mennini. 2016. The Perception of Corruption in Health: AutoCM Methods for an International Comparison. Quality & Quantity. https://doi.org/10.1007/s11135-016-0315-4 (Springer).

  • Buscema, Massimo, Masoud Asadi-Zeydabadi, Weldon Lodwick, and Marco Breda. 2016. The H0 Function, a New Index for Detecting Structural/Topological Complexity Information in Undirected Graphs. Physica A 447: 355–378.

    Google Scholar 

  • Buscema, Paolo Massimo, Guido Maurelli, Francesco Saverio Mennini, et al. 2016. Artificial Neural Networks and Their Potentialities in Analyzing Budget Health Data: An Application for Italy of What-If Theory. Quality & Quantity. https://doi.org/10.1007/s11135-016-0329-y (Springer).

  • Buscema, Massimo, and Pier Luigi Sacco. 2016. MST Fitness Index and Implicit Data Narratives: A Comparative Test on Alternative Unsupervised Algorithms. Physica A 461: 726–746.

    Google Scholar 

  • Ferilli, Guido, Pier Luigi Sacco, Emanuele Teti, and Massimo Buscema. 2016. Top Corporate Brands and the Global Structure of Country Brand Positioning: An AutoCM ANN Approach. Expert Systems With Applications 66: 62–75.

    Google Scholar 

  • Caffarra, Paolo, Simona Gardini, Francesca Dieci, et al. 2013. The Qualitative Scoring MMSE Pentagon Test (QSPT): A New Method for Differentiating Dementia with Lewy Body from Alzheimer’s Disease. Behavioural Neurology 27: 213–220. https://doi.org/10.3233/ben-120319.

  • Campisi, Giuseppina, Martina Chiappelli, Massimo De Martinis, Vito Franco, Lia Ginaldi, Rosario Guiglia, Federico Licastro, and Domenico Lio. 2009. Pathophysiology of Age-Related Diseases. Immunity & Ageing 6: 12. https://doi.org/10.1186/1742-4933-6-12.

  • Coppedè, Fabio, Enzo Grossi, Francesca Migheli, and Lucia Migliore. 2010. Polymorphisms in Folate-Metabolizing Genes, Chromosome Damage, and Risk of Down Syndrome in Italian Women: Identification of Key Factors Using Artificial Neural Networks. BMC Medical Genomics 3: 42.

    Google Scholar 

  • De Benedetti, Stefano, Giorgio Lucchini, Alessandro Marocchi, Silvana Penco, Christian Lunetta, Stefania Iametti*, Elisabetta Gianazza, and Francesco Bonomi. 2015. Serum Metal Evaluation in a Small Cohort of Amyotrophic Lateral Sclerosis Patients Reveals High Levels of Thiophylic Species. Peptidomics 2: 29–34.

    Google Scholar 

  • di Ludovico, Alessandro. 2008. Experimental approaches to glyptic art using artificial neural networks. An investigation into the Ur III iconological context. In Proceedings of the 36th CAA Conference, Budapest, April 2–6, 2008.

    Google Scholar 

  • Di Ludovico, A., and G. Pieri. 2011. Artificial Neural Networks and Ancient Artefacts: Justifications for a Multiform Integrated Approach Using PST and Auto-CM Models. Archeologia e Calcolatori 22: 99–128.

    Google Scholar 

  • Gallucci, Maurizio, Pierpaolo Spagnolo, and Maria Aricò. 2016. Predictors of Response to Cholinesterase Inhibitors Treatment of Alzheimer’s Disease: Date Mining from the TREDEM Registry. Journal of Alzheimer’s Disease 50: 969–979. https://doi.org/10.3233/JAD-150747.

  • Gironi, M., et al. 2013. A Novel Data Mining System Points Out Hidden Relationships Between Immunological Markers in Multiple Sclerosis. Immunity & Ageing 10: 1.

    Google Scholar 

  • Grossi, E., S. Cazzaniga, S. Crotti, et al. 2014. The Constellation of Dietary Factors in Adolescent Acne: A Semantic Connectivity Map Approach. Journal of the European Academy of Dermatology and Venereology, December 2014. https://doi.org/10.1111/jdv.12878.

  • Knibbe, Ronald A., Mieke Derickx, Allaman Allamani, and Giulia Massini. 2014. Alcohol Consumption and Its Related Harms in the Netherlands Since 1960: Relationships with Planned and Unplanned Factors. Substance Use & Misuse, Early Online: 1–12.

    Google Scholar 

  • Smerieri, Arianna, Chiara Testa, Pietro Lazzeroni, et al. 2015. Di-(2-Ethylhexyl) Phthalate Metabolites in Urine Show Age-Related Changes and Associations with Adiposity and Parameters of Insulin Sensitivity in Childhood. PLoS ONE 10 (2): e0117831. https://doi.org/10.1371/journal.pone.0117831.

  • Street, Maria E., Enzo Grossi, Cecilia Volta, Elena Faleschini, and Sergio Bernasconi. 2008. Placental Determinants of Fetal Growth: Identification of Key Factors in the Insulin-Like Growth Factor and Cytokine Systems Using Artificial Neural Networks. BMC Pediatrics 8: 24. https://doi.org/10.1186/1471-2431-8-24.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paolo Massimo Buscema .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Buscema, P.M., Massini, G., Breda, M., Lodwick, W.A., Newman, F., Asadi-Zeydabadi, M. (2018). Auto-contractive Maps. In: Artificial Adaptive Systems Using Auto Contractive Maps. Studies in Systems, Decision and Control, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-75049-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75049-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75048-4

  • Online ISBN: 978-3-319-75049-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics