massimo buscema
massimo buscema
Director of Semeion, Research & Professor Adjoint at Dept of Mathematical and Statistical Sciences
Verified email at - Homepage
Cited by
Cited by
Introduction to artificial neural networks
E Grossi, M Buscema
European journal of gastroenterology & hepatology 19 (12), 1046-1054, 2007
Innovative diagnostic tools for early detection of Alzheimer's disease
C Laske, HR Sohrabi, SM Frost, K López-de-Ipiña, P Garrard, M Buscema, ...
Alzheimer's & Dementia 11 (5), 561-578, 2015
Back propagation neural networks
M Buscema
Substance use & misuse 33 (2), 233-270, 1998
Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets
R Todeschini, V Consonni, H Xiang, J Holliday, M Buscema, P Willett
Journal of chemical information and modeling 52 (11), 2884-2901, 2012
The interaction between culture, health and psychological well-being: Data mining from the Italian culture and well-being project
E Grossi, G Tavano Blessi, PL Sacco, M Buscema
Journal of Happiness Studies 13, 129-148, 2012
Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study
E Grossi, C Olivieri, M Buscema
Computer methods and programs in biomedicine 142, 73-79, 2017
Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?
PM Rossini, M Buscema, M Capriotti, E Grossi, G Rodriguez, C Del Percio, ...
Clinical Neurophysiology 119 (7), 1534-1545, 2008
The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease
M Buscema, E Grossi
International journal of data mining and bioinformatics 2 (4), 362-404, 2008
Genetic doping algorithm (GenD): theory and applications
M Buscema
Expert Systems 21 (2), 63-79, 2004
Metanet*: The theory of independent judges
M Buscema
Substance use & misuse 33 (2), 439-461, 1998
Auto-contractive maps: an artificial adaptive system for data mining. An application to Alzheimer disease
M Buscema, E Grossi, D Snowdon, P Antuono
Current Alzheimer Research 5 (5), 481-498, 2008
The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy
M Buscema, P Rossini, C Babiloni, E Grossi
Artificial Intelligence in Medicine 40 (2), 127-141, 2007
A new meta-classifier
M Buscema, S Terzi, W Tastle
2010 Annual Meeting of the North American Fuzzy Information Processing …, 2010
Improved artificial neural networks in prediction of malignancy of lesions in contrast‐enhanced MR‐mammography
TW Vomweg, M Buscema, HU Kauczor, A Teifke, M Intraligi, S Terzi, ...
Medical physics 30 (9), 2350-2359, 2003
A brief overview and introduction to artificial neural networks
M Buscema
Substance use & misuse 37 (8-10), 1093-1148, 2002
Artificial neural networks accurately predict mortality in patients with nonvariceal upper GI bleeding
G Rotondano, L Cipolletta, E Grossi, M Koch, M Intraligi, M Buscema, ...
Gastrointestinal Endoscopy 73 (2), 218-226. e2, 2011
International experience on the use of artificial neural networks in gastroenterology
E Grossi, A Mancini, M Buscema
Digestive and liver disease 39 (3), 278-285, 2007
Artificial neural networks allow the use of simultaneous measurements of Alzheimer disease markers for early detection of the disease
M Di Luca, E Grossi, B Borroni, M Zimmermann, E Marcello, F Colciaghi, ...
Journal of translational medicine 3, 1-7, 2005
An optimized experimental protocol based on neuro-evolutionary algorithms: application to the classification of dyspeptic patients and to the prediction of the effectiveness of …
M Buscema, E Grossi, M Intraligi, N Garbagna, A Andriulli, M Breda
Artificial Intelligence in Medicine 34 (3), 279-305, 2005
Self-reflexive networks: Theory· topology· applications
M Buscema
Quality and Quantity 29, 339-403, 1995
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