Intelligent Data Analysis, Decision Making and Modelling Adaptive Financial Systems Using Hierarchical Neural Networks

Author(s):  
Masoud Mohammadian ◽  
Mark Kingham
Author(s):  
Masoud Mohammadian ◽  
Mark Kingham

In this chapter, an intelligent hierarchical neural network system for prediction and modelling of interest rates in Australia is developed. A hierarchical neural network system is developed to model and predict 3 months’ (quarterly) interest-rate fluctuations. The system is further trained to model and predict interest rates for 6-month and 1-year periods. The proposed system is developed with first four and then five hierarchical neural networks to model and predict interest rates. Conclusions on the accuracy of prediction using hierarchical neural networks are also reported.


Human Ecology ◽  
2021 ◽  
pp. 55-64
Author(s):  
A. N. Narkevich ◽  
K. A. Vinogradov ◽  
K. M. Paraskevopulo ◽  
A. M. Grjibovski

2021 ◽  
pp. 35-47
Author(s):  
Yevgeniya A. Savchenko-Synyakova ◽  
◽  
Olena V. Tutova ◽  

The tools of intellectual modeling in MS Excel are developed on the basis of a technique of the analysis, modeling and forecasting of difficult processes on a DB of social and economic processes which contains the information on the indicators characterizing the development of the digital economy of Ukraine. This will automate and improve the decision-making process in the field of socio-economic development of Ukraine both at the regional level and in comparison with other countries.


Author(s):  
K. B. Shabanov ◽  
◽  
V. V. Alekseev ◽  

In connection with the relevance of data analysis automation, basic data mining methods are considered, such as neural networks, genetic algorithms, and fuzzy logic methods. The essence of these methods and their practical applicability are shown, in particular, methods of fuzzy logic for the problem of improving the quality of decision-making when managing the resources of the information media system.


1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.


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