Vehicular Ad-hoc networks (VANETs) are a subset of Mobile Ad-hoc Networks made by vehicles communicating among themselves on roadways. The Routing protocols implemented for MANETs such as Ad-hoc on Demand Distance Vector Routing Protocol (AODV), Dynamic Source Routing (DSR), and Destination Sequence Distance Vector Routing Protocol (DSDV) are not suitable for VANET due to high Mobility. Trusted routing in VANET is a challenging task due to highly dynamic network topology and openness of wireless architecture. To avoid a frequent communication link failure, to reduce the communication overhead and to provide a trusted routing among the vehicular nodes for achieving high packet transmission, we implemented an Optimized Node Selection Routing protocol (ONSRP) of VANET based on Trust. In our proposed work, we implemented an enhanced routing protocol which prevents the network from communication link failure frequently. The testing results stated that the ONSRP routing have a high performance measures than the above mentioned existing routing protocols.

Literacy rate of deaf students is very less in India. So there is a need to build an effective academic prediction model for identifying weak deaf students. Many machine learning techniques like Decision tree, Support Vector Machine, Neural Network are used to build prediction models. But the most preferred technique is neural network. It is found out that regression model build with neural networks takes more time to converge and the error rate is quite high. To solve the problems of neural network, we use Particle Swarm Optimization (PSO) for weight adjustment in the neural network. But, one of the main drawback of PSO lies in setting the initial parameters. So, a new PSO algorithm which determines the initial weight of the neural network using regression equation is proposed. The results show that neural network build with the proposed PSO algorithm performs well than neural network build with basic PSO algorithm. The Mean Square Error (MSE) achieved in this work is 0.0998, which is comparatively less than many existing models.

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