Literature DB >> 12180558

A brief overview and introduction to artificial neural networks.

Massimo Buscema1.   

Abstract

This article is designed to acquaint professionals working in the field of substance use intervention with a range of artificial intelligence nonlinear, powerful tools, artificial neural networks, concepts, and paradigms. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data as well as our studying and understanding the many people, processes, and phenomena which comprise substance use and its intervention. The latter represent complex, dynamic, multidimensional phenomena which are unpredictable and uncontrollable in the traditional "cause and effect" sense. As such they are likely to be nonlinear in their very essence. Using linear-based paradigms for planned intervention with nonlinear phenomena brooks the all-too-common possibility of using inappropriate intervention paradigms and/or drawing misleading conclusions about what is and/or has happened.

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Year:  2002        PMID: 12180558     DOI: 10.1081/ja-120004171

Source DB:  PubMed          Journal:  Subst Use Misuse        ISSN: 1082-6084            Impact factor:   2.164


  5 in total

1.  Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status.

Authors:  Massimo Buscema; Enzo Grossi; David Snowdon; Piero Antuono; Marco Intraligi; Guido Maurelli; Rita Savarè
Journal:  Neuroinformatics       Date:  2004

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

Authors:  Edith Lahner; Marco Intraligi; Massimo Buscema; Marco Centanni; Lucy Vannella; Enzo Grossi; Bruno Annibale
Journal:  World J Gastroenterol       Date:  2008-01-28       Impact factor: 5.742

Review 3.  Subjective responses to alcohol consumption as endophenotypes: advancing behavioral genetics in etiological and treatment models of alcoholism.

Authors:  Lara A Ray; James Mackillop; Peter M Monti
Journal:  Subst Use Misuse       Date:  2010-09       Impact factor: 2.164

4.  Prediction of semen quality using artificial neural network.

Authors:  Anna Badura; Urszula Marzec-Wroblewska; Piotr Kaminski; Pawel Lakota; Grzegorz Ludwikowski; Marek Szymanski; Karolina Wasilow; Andzelika Lorenc; Adam Bucinski
Journal:  J Appl Biomed       Date:  2019-09-17       Impact factor: 1.797

5.  Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke.

Authors:  Hassan Bagher-Ebadian; Kourosh Jafari-Khouzani; Panayiotis D Mitsias; Mei Lu; Hamid Soltanian-Zadeh; Michael Chopp; James R Ewing
Journal:  PLoS One       Date:  2011-08-10       Impact factor: 3.240

  5 in total

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