268,801 research outputs found
NEURAL NETWORKS FOR DECISION SUPPORT: PROBLEMS AND OPPORTUNITIES
Neural networks offer an approach to computing which - unlike conventional
programming - does not necessitate a complete algorithmic specification. Furthermore,
neural networks provide inductive means for gathering, storing, and
using, experiential knowledge. Incidentally, these have also been some of the
fundamental motivations for the development of decision support systems in
general. Thus, the interest in neural networks for decision support is immediate
and obvious. In this paper, we analyze the potential contribution of neural
networks for decision support, on one hand, and point out at some inherent constraints
that might inhibit their use, on the other. For the sake of completeness
and organization, the analysis is carried out in the context of a general-purpose
DSS framework that examines all the key factors that come into play in the
design of any decision support system.Information Systems Working Papers Serie
A CASE STUDY ON SUPPORT VECTOR MACHINES VERSUS ARTIFICIAL NEURAL NETWORKS
The capability of artificial neural networks for pattern recognition of real world problems is well known. In recent years, the support vector machine has been advocated for its structure risk minimization leading to tolerance margins of decision boundaries. Structures and performances of these pattern classifiers depend on the feature dimension and training data size. The objective of this research is to compare these pattern recognition systems based on a case study. The particular case considered is on classification of hypertensive and normotensive right ventricle (RV) shapes obtained from Magnetic Resonance Image (MRI) sequences. In this case, the feature dimension is reasonable, but the available training data set is small, however, the decision surface is highly nonlinear.For diagnosis of congenital heart defects, especially those associated with pressure and volume overload problems, a reliable pattern classifier for determining right ventricle function is needed. RVÂĄÂŚs global and regional surface to volume ratios are assessed from an individualÂĄÂŚs MRI heart images. These are used as features for pattern classifiers. We considered first two linear classification methods: the Fisher linear discriminant and the linear classifier trained by the Ho-Kayshap algorithm. When the data are not linearly separable, artificial neural networks with back-propagation training and radial basis function networks were then considered, providing nonlinear decision surfaces. Thirdly, a support vector machine was trained which gives tolerance margins on both sides of the decision surface. We have found in this case study that the back-propagation training of an artificial neural network depends heavily on the selection of initial weights, even though randomized. The support vector machine where radial basis function kernels are used is easily trained and provides decision tolerance margins, in spite of only small margins
Integration of Artificial Neural Networks and Simulation Modeling in a Decision Support System
A simulation based decision support system is developed for AT&T Microelectronics in Orlando. This system uses simulation modeling to capture the complex nature of semiconductor test operations. Simulation, however, is not a tool for optimization by itself. Numerous executions of the simulation model must generally be performed to narrow in on a set of proper decision parameters. As a means of alleviating this shortcoming, artificial neural networks are used in conjunction with simulation modeling to aid management in the decision making process. The integration of simulation and neural networks in a comprehensive decision support system, in effect, learns the reverse of the simulation process. That is, given a set of goals defined for performance measures, the decision support system suggests proper values for decision parameters to achieve those goals
A Neural-CBR System for Real Property Valuation
In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the
increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based
reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed
the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However,
individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of
decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system
for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case-
Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the
system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case-
Based Reasoning systems
A NEURAL NETWORK APPROACH TO FORECASTING EARNINGS PER SHARE
This paper explores the potential of neural networks to forecast earnings per share. The neural network would serve as a decision support system for finance managers, stock brokers and investment analysts and investors. Results of experiments with training/testing indicate that neural networks appear to be promising in forecasting EPS. Further investigations are necessary
DECISION SUPPORT FOR FREEZE PROTECTION USING ARTIFICIAL NEURAL NETWORKS
Crop Production/Industries,
Combined Machine Learning Techniques for Decision Making Support in Medicine
Computational intelligent support for decision making is becoming increasingly popular and essential among medical professionals. Also, with the modern medical devices being capable to communicate with ICT, created models can easily find practical translation into software. Machine learning solutions for medicine range from the robust but opaque paradigms of support vector machines and neural networks to the also performant, yet more comprehensible, decision trees and rule-based models. So how can such different techniques be combined such that the professional obtains the whole spectrum of their particular advantages? The presented approaches have been conceived for various medical problems, while permanently bearing in mind the balance between good accuracy and understandable interpretation of the decision in order to truly establish a trustworthy âartificialâ second opinion for the medical expert.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndlaucĂa Tech
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