Decision Support System Using Decision Tree and Neural Networks

Decision making in a complex and dynamically changing environment of the present day demands a new techniques of computational intelligence for building equally an adaptive, hybrid intelligent decision support system. In this paper, a Decision Tree-Neuro Based model was developed to handle loan granting decision support system and clinical decision support system(Eye Disease Diagnosis) which are two important decision problems that requires delicate care. The system uses an integration of Decision Tree and Artificial Neural Networks with a hybrid of Decision Tree algorithm and Multilayer Feed-forward Neural Network with backpropagation learning algorithm to build up the proposed model. Different representative cases of loan applications and eye disease diagnosis were considered based on the guidelines of different banks in Nigeria and according to patient complaint, symptoms and physical eye examinations to validate the model. Object-Oriented Analysis and Design (OO-AD) methodology was used in the development of the system, and an object-oriented programming language was used with a MATLAB engine to implement the models and classes designed in the system. The system developed, gives 88% success rate and eliminate the opacity of an ordinary neural networks system. Keywords: Decision Tree-Neuro Based Model, Backpropagation Learning Algorithm, Object-Oriented Analysis and Design, MATLAB Embedded Engine, Loan Granting, Eye Diseases Diagnosis

Knowledge Discovery using Artificial Neural Networks for a Conservation Biology Domain

Abstract- We present an artificial intelligence method for the development of decision support systems for environmental management and demonstrate its strengths using an example from the domain of biodiversity and conservation biology. Renosterveld vegetation is unique to South Africa; it is under threat of extinction as a result of rapidly growing agricultural activities. We use artificial neural networks and decision trees for knowledge discovery on the Renosterveld domain. We train artificial neural networks on a dataset of the existing plant species and show their generalization performance with gradient descent learning. We then extract knowledge from trained neural networks in the form of decision trees and obtain rules which describe the existence of the remaining Renosterveld vegetation. These rules will be used as a contribution for the conservation of Renosterveld. The rules demonstrate a prediction of 78 % that a Renosterveld plant will grow in a particular environment given its environmental conditions. The general paradigm can hence be applied to other plant species for knowledge discovery and the development of decision support systems

Multi-temporal analysis of changes in vegetation distribution in the Great Fish River Game Reserve, Eastern Cape Province, South Africa :1982-2012

Great Fish River Game Reserve plays an important role in curtailing the ever-increasing biodiversity declines in Eastern Cape Province and South Africa at large. Though this area plays an important role in the conservation of natural biodiversity, it has been observed that it is undergoing considerable changes with regards to conditions and composition of vegetation cover. These changes signal a decline in the capacity of the Great Fish River Game Reserve to support wildlife population. In this study, remote sensing was used to investigate multi-temporal changes in vegetation distribution in this particular reserve over a period of 30 years (1982-2012). A supervised classification was carried out to classify four Landsat images including; Landsat TM, Landsat ETM and Landsat 8 imagery of 1984, 1992, 2002 and 2013 respectively to map historical and present vegetation conditions and distribution in the Great Fish River Game Reserve. A comparative examination of the classified images showed that there were significant changes in the composition and structure of vegetation with much of the palatable plant species being driven to extinction. The results showed that herbivory pressure inconjuction with climate variability has subsequently resulted in a decrease of the supporting potential of the reserve to sustain wildlife due to mortality of the most preferred plant species and abundant increase of non-palatable plant species. As grazing and browsing intensity increased, there was a decline in regenerative potential of the selected plant species by wild herbivores and successful increase in abundance of the non-palatable plant species. The results of this investigation suggest that the current increase in wildlife population will facilitate the deterioration of habitat condition to support wildlife up to the point of no recovery