M. Buscema

发表

M. Buscema, W. Tastle, W. J. Tastle, 2010, 2010 Annual Meeting of the North American Fuzzy Information Processing Society.

A BRIEF OVERVIEW AND INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

M. Buscema, 2002, Substance use & misuse.

Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data

M. Buscema, F. Pace, P. Dominici, 2005, European journal of gastroenterology & hepatology.

MetaNet: the theory of independent judges.

M. Buscema, 1998, Substance use & misuse.

Auto-contractive Maps, the H Function, and the Maximally Regular Graph (MRG): A New Methodology for Data Mining

Massimo Buscema, Pier L. Sacco, M. Buscema, 2010 .

Self-recurrent neural network.

M Buscema, M. Buscema, 1998, Substance use & misuse.

The Sonda Project: prevention, prediction, and psychological disorder.

M. Buscema, Massimo Buscema, 1997, Substance use & misuse.

The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy.

C Babiloni, M Buscema, P. Rossini, 2010, Current Alzheimer research.

Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals

M. Buscema, F. Coppedè, L. Migliore, 2013, PloS one.

The Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project

M. Buscema, P. Sacco, Giorgio Tavano Blessi, 2012 .

Introduction to artificial neural networks.

M. Buscema, E. Grossi, 2007, European journal of gastroenterology & hepatology.

Artificial Neural Networks for Early Prediction of Mortality in Patients with Non Variceal Upper GI Bleeding (UGIB)

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2008, Biomedical informatics insights.

Artificial neural networks accurately predict mortality in patients with nonvariceal upper GI bleeding.

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2011, Gastrointestinal endoscopy.

Artificial intelligence and outcome research

M. Buscema, E. Grossi, 2006 .

Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction.

Massimo Buscema, Enzo Grossi, A. Smerieri, 2013, Progress in biophysics and molecular biology.

A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems.

M. Buscema, E. Grossi, T. Gomiero, 2011 .

Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?

Massimo Buscema, Claudio Del Percio, Claudio Babiloni, 2008, Clinical Neurophysiology.

Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2002, Journal of the American Geriatrics Society.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.

M Thelen, M Buscema, H. Kauczor, 2003, Medical physics.

Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia.

M Buscema, F Perri, A Andriulli, 2003, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

The potential role played by artificial adaptive systems in enhancing our understanding of Alzheimer disease: The experience gained within Italian Interdisciplinary network on Alzheimer disease

Massimo Buscema, Enzo Grossi, M. Buscema, 2004 .

Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

M. Buscema, F. Muratori, E. Grossi, 2015, Neuropsychiatric disease and treatment.

Theory of Constraint Satisfaction Neural Networks

M. Buscema, 2013 .

NEW RECURRENT NEURAL ARCHITECTURES

M. Buscema, Stefano Terzi, Marco Breda, 2005 .

Artificial neural networks for drug vulnerability recognition and dynamic scenarios simulation.

M Buscema, M. Buscema, M. Intraligi, 1998, Substance use & misuse.

Determinants of metabolic syndrome in obese workers: gender differences in perceived job-related stress and in psychological characteristics identified using artificial neural networks

M. Buscema, A. Compare, E. Grossi, 2018, Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity.

Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2005, Journal of Translational Medicine.

Assessing Post-Radiotherapy Treatment Involving Brain Volume Differences in Children: An Application of Adaptive Systems Methodology

M. Buscema, W. Tastle, Arthur K. Liu, 2013 .

Self-reflexive networks.

M Buscema, M. Buscema, Massimo Buscema, 1998, Substance use & misuse.

Understanding Cultural Geography as a Pseudo-Diffusion Process: The Case of the Veneto Region

Massimo Buscema, Pier Luigi Sacco, Guido Ferilli, 2015 .

Artificial neural networks identify the predictive values of risk factors on the conversion of amnestic mild cognitive impairment.

Massimo Buscema, Enzo Grossi, Carlo Caltagirone, 2010, Journal of Alzheimer's disease : JAD.

Comparison of four learning-based methods for predicting groundwater redox status

M. Buscema, M. Friedel, M. Close, 2020 .

Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles

M. Buscema, E. Grossi, L. Montanini, 2015, PloS one.

Is information being denied to the scientific community by the reductionist approach to data analysis in stroke related clinical trials?

M. Buscema, E. Grossi, C. Helgason, 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

38th CONGRESS OF THE EUROPEAN REGIONAL SCIENCE ASSOCIATION 28 AUGUST - 1 SEPTEMBER 1998 IN VIENNA THE RISK EVALUATION IN URBAN SUSTAINABILITY: A THRESHOLD METHODOLOGY AND A NEURAL NETWORK INVESTIGATION

M. Buscema, L. Diappi, 1998 .

The Traveling Salesman Problem as a new screening test in early Alzheimer’s disease: an exploratory study. Visual Problem-solving in AD

M. Buscema, E. Grossi, R. Savarè, 2005, Aging clinical and experimental research.

Geographical identification of Chianti red wine based on ICP-MS element composition.

R. Todeschini, M. Buscema, D. Ballabio, 2020, Food chemistry.

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

Enzo Grossi, Piero Antuono, David Snowdon, 2007, BMC neurology.

Complexity in Sustainability: an Investigation of the Italian Urban System trough Self-Reflexive Neural Networks

Massimo Buscema, Lidia Diappi, M. Buscema, 2004 .

A neural network investigation of the crucial facets of urban sustainability.

M. Buscema, L. Diappi, M. Ottanà, 1998, Substance use & misuse.

Possible contribution of advanced statistical methods (artificial neural networks and linear discriminant analysis) in recognition of patients with suspected atrophic body gastritis

M. Buscema, M. Intraligi, E. Grossi, 2005 .

Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data

M. Buscema, R. Fumagalli, E. Grossi, 2020, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.

Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

M. Buscema, R. Fumagalli, E. Grossi, 2020 .

Outbreaks source: A new mathematical approach to identify their possible location

M. Buscema, E. Grossi, Marco Breda, 2009 .

M. Buscema, R. Fumagalli, E. Grossi, 2020, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

M. Buscema, E. Grossi, G. Cornalba, 2020, European Radiology Experimental.

How to predict a high rate of inappropriateness for upper endoscopy in an endoscopic centre?

M. Buscema, C. Hassan, S. Morini, 2010, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

Innovative diagnostic tools for early detection of Alzheimer's disease

Yogesan Kanagasingam, Massimo Buscema, Karmele López-de-Ipiña, 2015, Alzheimer's & Dementia.

Squashing Theory: A Prediction Approach for Drug Behavior

M. Buscema, 1995 .

Multidimensional Similarities at a Global Scale: An Approach to Mapping Open Society Orientations

M. Buscema, P. Sacco, G. Ferilli, 2015, Social Indicators Research.

The Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project

M. Buscema, P. Sacco, Giorgio Tavano Blessi, 2011, Journal of Happiness Studies.

Applications of Mathematics in Models, Artificial Neural Networks and Arts

Pierluigi Contucci, Vittorio Capecchi, Bruno D'Amore, 2010 .

Theory: Foundations of Artificial Neural Networks

Massimo Buscema, M. Buscema, 1998 .

Unraveling the space grammar of terrorist attacks: A TWC approach

Massimo Buscema, Giulia Massini, Pier Luigi Sacco, 2018, Technological Forecasting and Social Change.

Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis.

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2008, World journal of gastroenterology.

Machine learning systems and precision medicine: A conceptual and experimental approach to single individual statistics

Giulia Massini, Enzo Grossi, Massimo Buscema, 2020 .

Auto Contractive Maps, H Function and Maximally Regular Graph - Application

Enzo Grossi, Massimo Buscema, Cathy M. Helgason, 2009 .

PST: a new evolutionary approach to topographic mapping

Massimo Buscema, Stefano Terzi, M. Buscema, 2006 .

Intelligent Data Mining in Law Enforcement Analytics

Massimo Buscema, William J. Tastle, M. Buscema, 2012 .

Meta Net: A New Meta-Classifier Family

Massimo Buscema, Stefano Terzi, William J. Tastle, 2013 .

Auto-Contractive Maps: an artificial adaptive system for data mining. An application to Alzheimer disease.

David C. Snowdon, M Buscema, M. Buscema, 2008, Current Alzheimer research.

The general philosophy of Artificial Adaptive Systems

M. Buscema, M. Buscema, 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

Back propagation neural networks.

M Buscema, M. Buscema, 1998, Substance use & misuse.

Application of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer.

Massimo Buscema, Enzo Grossi, Lucia Migliore, 2015, Epigenomics.

Networks in Coronary Heart Disease Genetics As a Step towards Systems Epidemiology

Massimo Buscema, Fotios Drenos, Enzo Grossi, 2015, PloS one.

Detection of an Autism EEG Signature From Only Two EEG Channels Through Features Extraction and Advanced Machine Learning Analysis

M. Buscema, E. Grossi, G. Valbusa, 2020, Clinical EEG and neuroscience.

The “MS-ROM/IFAST” Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy

Massimo Buscema, Enzo Grossi, Francesca Della Torre, 2019, Clinical EEG and neuroscience.

Combining Personality Traits with Traditional Risk Factors for Coronary Stenosis: An Artificial Neural Networks Solution in Patients with Computed Tomography Detected Coronary Artery Disease

M. Buscema, A. Compare, E. Grossi, 2013, Cardiovascular psychiatry and neurology.

Multivariable network associated with cognitive decline and dementia

Massimo Buscema, Enzo Grossi, Federico Licastro, 2010, Neurobiology of Aging.

Recognition of patients with cardiovascular disease by artificial neural networks

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2004, Annals of medicine.

Introduction: Health and Life

Massimo Buscema, M. Buscema, 1998 .

Artificial neural networks and complex social systems. I. Theory.

M Buscema, M. Buscema, 1998, Substance use & misuse.

Multi factorial interactions in the pathogenesis pathway of Alzheimer’s disease: a new risk charts for prevention of dementia

M. Buscema, E. Porcellini, F. Licastro, 2010, Immunity & Ageing.

The transferal of evidences derived from clinical research to single patient level: Automatic distinction of normal elderly vs. mild cognitive impairment subjects by resting EEG data processed by IFAST, a novel intelligent system

P.M. Rossini, M. Buscema, E. Grossi, 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

What kind of ‘world order’? An artificial neural networks approach to intensive data mining

Massimo Buscema, Pier Luigi Sacco, Guido Ferilli, 2017 .

Multidimensional Similarities at a Global Scale: An Approach to Mapping Open Society Orientations

Massimo Buscema, Pier Luigi Sacco, Guido Ferilli, 2016 .

Gene-gene and gene - clinical factors interaction in acute myocardial infarction: a new detailed risk chart.

M. Buscema, E. Porcellini, F. Licastro, 2010, Current pharmaceutical design.

Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity

M. Buscema, E. Grossi, L. Vigna, 2019, Journal of the American College of Nutrition.

Artificial adaptive systems and predictive medicine

Enzo Grossi, Massimo Buscema, M. Buscema, 2010, 2010 Annual Meeting of the North American Fuzzy Information Processing Society.

The contribution of Artificial Adaptive System to limit the influence of systematic errors in the definition of the kinematic behavior of an extremely-slow landslide

Massimo Buscema, Weldon A. Lodwick, Donatella Dominici, 2016 .

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

M. Buscema, E. Grossi, S. Penco, 2012, Neurology research international.

A New Risk Chart of Acute Myocardial Infarction in Men by an Innovative Algorithm: A Pilot Study

M. Buscema, E. Porcellini, F. Licastro, 2015 .

A global immune deficit in Alzheimer's disease and mild cognitive impairment disclosed by a novel data mining process.

Massimo Buscema, Enzo Grossi, Raffaello Nemni, 2014, Journal of Alzheimer's disease : JAD.

Self-reflexive networks: Theory · topology · applications

Massimo Buscema, M. Buscema, 1995 .

An Artificial Intelligent Systems Approach to Unscrambling Power Networks in Italy’s Business Environment

Massimo Buscema, Pier L. Sacco, M. Buscema, 2010 .

Artificial neural networks can classify uninvestigated patients with dyspepsia

Massimo Buscema, Enzo Grossi, Francesco Perri, 2007, European journal of gastroenterology & hepatology.

K-CM: a new artificial neural network. Application to supervised pattern recognition

Massimo Buscema, Marco Breda, Roberto Todeschini, 2014 .

The H0 function, a new index for detecting structural/topological complexity information in undirected graphs

Massimo Buscema, Marco Breda, Masoud Asadi-Zeydabadi, 2016 .

Prediction of optimal warfarin maintenance dose using advanced artificial neural networks.

Massimo Buscema, Enzo Grossi, M. Buscema, 2014, Pharmacogenomics.

Artificial Adaptive Systems in Data Visualization: Proactive Data

Massimo Buscema, M. Buscema, 2013 .

The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Massimo Buscema, Marco Intraligi, Paola Bolchi, 2002 .

Artificial intelligence to design collaborative strategy: An application to urban destinations

Massimo Buscema, Guido Ferilli, Manuela De Carlo, 2020 .

Assessment of the Role of Genetic Polymorphism in Venous Thrombosis Through Artificial Neural Networks

M Buscema, M. Buscema, M. Intraligi, 2005, Annals of human genetics.

The Topological Weighted Centroid (TWC): A topological approach to the time-space structure of epidemic and pseudo-epidemic processes

Massimo Buscema, Giulia Massini, Pier Luigi Sacco, 2017, 1703.07175.

Semantics of Point Spaces Through the Topological Weighted Centroid and Other Mathematical Quantities: Theory and Applications

Massimo Buscema, Marco Breda, Enzo Grossi, 2013 .

TOWARD AN INTEGRATIVE APPROACH IN THE ANALYSIS OF DEPENDENCY PROBLEMS

M. Buscema, David Hamilton, J. Lilja, 2001, Substance use & misuse.

Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis.

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2005, World journal of gastroenterology.

Training with Input Selection and Testing (TWIST) Algorithm: A Significant Advance in Pattern Recognition Performance of Machine Learning

Massimo Buscema, Marco Breda, Weldon A. Lodwick, 2013 .

A nonlinear, data-driven, ANNs-based approach to culture-led development policies in rural areas: The case of Gjakove and Peć districts, Western Kosovo

M. Buscema, P. Sacco, G. Ferilli, 2022, Chaos, Solitons & Fractals.

Improved Understanding of Urban Sprawl Using Neural Networks

Massimo Buscema, Lidia Diappi, Paola Bolchim, 2004 .

MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms

Massimo Buscema, Pier Luigi Sacco, M. Buscema, 2016 .

Why Mathematical Computer Simulations Are the New Laboratory for Scientists

Massimo Buscema, M. Buscema, 2015, Substance use & misuse.

The Topological Weighted Centroid and the Semantic of the Physical Space - Application

M. Buscema, E. Grossi, T. Jefferson, 2009 .

The Topological Weighted Centroid, and the Semantic of the Physical Space - Theory

M. Buscema, Marco Breda, Luigi Catzola, 2009 .

Application of artificial neural networks to eating disorders.

M Buscema, M. Buscema, M. Intraligi, 1998, Substance use & misuse.

Upper GI endoscopy in elderly patients: predictive factors of relevant endoscopic findings

M. Buscema, C. Hassan, A. Zullo, 2013, Internal and Emergency Medicine.

Digging deeper on “deep” learning: A computational ecology approach

Massimo Buscema, Pier Luigi Sacco, M. Buscema, 2017, Behavioral and Brain Sciences.

Auto-Contractive Maps, H Function and Maximally Regular Graph - Theory

M. Buscema, 2009 .

Prediction of high on-treatment platelet reactivity in clopidogrel-treated patients with acute coronary syndromes.

M. Buscema, S. de Servi, E. Grossi, 2017, International journal of cardiology.

The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm

Massimo Buscema, Enzo Grossi, Angelo Compare, 2014 .

Images as Active Connection Matrixes: the J-Net System

Massimo Buscema, Enzo Grossi, Luigi Catzola, 2008 .

The Smart Library Architecture of an Orientation Portal

Massimo Buscema, Guido Maurelli, Stefano Terzi, 2006 .

Preprocessing Tools for Nonlinear Datasets

Massimo Buscema, Marco Breda, Alessandra Mancini, 2013 .

Spatiotemporal Mining: A Systematic Approach to Discrete Diffusion Models for Time and Space Extrapolation

M. Buscema, E. Grossi, W. Lodwick, 2013 .

GenD An Evolutionary System for Resampling in Survey Research

Massimo Buscema, G. Massini, Cinzia Meraviglia, 2006 .

A general presentation of artificial neural networks. I.

M Buscema, M. Buscema, Massimo Buscema, 1997, Substance use & misuse.

Appropriateness Guidelines and Predictive Rules to Select Patients for Upper Endoscopy: A Nationwide Multicenter Study

Massimo Buscema, Enzo Grossi, Marco Intraligi, 2009, The American Journal of Gastroenterology.

The added value of Artificial Organisms in the analysis of medical data: six years of experience

M. Buscema, E. Grossi, A. Mancini, 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

International experience on the use of artificial neural networks in gastroenterology.

M Buscema, M. Buscema, E. Grossi, 2007, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease

M. Buscema, M. Intraligi, E. Grossi, 2005, Journal of Translational Medicine.

Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia.

M. Buscema, P. Dominici, N. M. Intraligi, 2003, Digestive and Liver Disease.

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

M. Buscema, D. Snowdon, E. Grossi, 2007, BMC Neurology.

Application of adaptive systems methodology to radiotherapy

Massimo Buscema, Francis Newman, Enzo Grossi, 2010, 2010 Annual Meeting of the North American Fuzzy Information Processing Society.

Supervised Artificial Neural Networks: Backpropagation Neural Networks

Massimo Buscema, M. Buscema, 2013 .

How to Perform Data Mining: The “Persons Arrested” Dataset

Massimo Buscema, M. Buscema, 2010 .