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This book presents a design framework based on a centralized
scalable architecture for effective simulated aerial threat
perception. In this framework data mining and pattern
classification techniques are incorporated. This paper focuses on
effective prediction by relying on the knowledge base and finding
patterns for building the decision trees. This framework is
flexibly designed to seamlessly integrate with other applications.
The results show the effectiveness of selected algorithms and
suggest that more the parameters are incorporated for the decision
making for aerial threats; the better is our confidence level on
the results. To delve into accurate target prediction we have to
make decisions on multiple factors. Multiple techniques used
together helps in finding the accurate threat classification and
result in better confidence on our results.
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