Results 271 to 280 of about 274,716 (313)

Artificial neural networks in urolithiasis

Current Opinion in Urology, 2005
The management of urolithiasis is a clinical challenge worldwide which may result in difficulty in diagnosis, treatment and prevention of recurrence. Artificial neural networks (ANNs) are well described adjuncts to many aspects of clinical urological practice. We review literature published in on-line Medline-citable English language journals to assess
Prabhakar, Rajan, David A, Tolley
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Artificial Neural Networks [PDF]

, 2008
Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.
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Artificial neural networks in neurosurgery

Journal of Neurology, Neurosurgery & Psychiatry, 2014
Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken.
Parisa, Azimi, Hasan Reza, Mohammadi, Edward C, Benzel, Sohrab, Shahzadi, Shirzad, Azhari, Ali, Montazeri   +5 more
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Introduction to artificial neural networks

European Journal of Gastroenterology & Hepatology, 2007
The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity.
Enzo, Grossi, Massimo, Buscema
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EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS

International Journal of Neural Systems, 1993
Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, architectures and learning rules. It first reviews each
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On training of artificial neural networks

International Joint Conference on Neural Networks, 1989
A theory and methodology are presented for training artificial neural networks in a general setting. Starting with defining general concepts, and analyzing associated properties of artificial neural networks, the authors formalize, categorize, and characterize artificial neural networks from a system point of view.
Behnam Malakooti, Jun Wang
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Overview of Artificial Neural Networks

2008
The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties.
Jinming, Zou, Yi, Han, Sung-Sau, So
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Neural networks and artificial intelligence

Information Sciences, 1993
L'article presente les sept papiers de ce numero. Il met, en particulier, l'emphase sur l'avance considerable du connexionnisme et des reseaux neuronaux. Il montre comment la methode connexionniste de resolution de probleme de diagnostic peut etre mieux appropriee que les methodes de recherche sequentielle.
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ASIPs for artificial neural networks

2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2011
Customized application-specific processors called ASIPs are becoming commonplace in contemporary embedded system designs. Neural networks are an interesting application for which an ASIP can be tailored to increase performance, lower power consumption and/or increase throughput.
Daniel Shapiro, Jonathan Parri, John-Marc Desmarais, Voicu Groza, Miodrag Bolic   +4 more
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Explanation and artificial neural networks

International Journal of Man-Machine Studies, 1992
Abstract Explanation is an important function in symbolic artificial intelligence (AI). For instance, explanation is used in machine learning, in case-based reasoning and, most important, the explanation of the results of a reasoning process to a user must be a component of any inference system.
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