Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we...

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do “compilation of inference” because our method transforms a denotational specification of an infere...

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicl...

In this paper, a probabilistic neural network (PNN) based classifier is used to judge the static security of the power system. The proposed classifier classifies the security of the power system based on the voltage profile of each bus in reference to changes in the generation and load profile in the system. The probabilistic neural network is used and compared with the radial basis function ne...

This study investigates the predictive power of three neutral network models: Multi-layer neural network, probabilistic neural network, and logistic regression model in predicting corporate failure. Basing on the database provided by The Corporate Scorecard Group (CSG), we combine financial ratios which deem to be significant predictors of corporate bankruptcy in many previous empirical studies...