The Quality of Service (QoS) routing protocol plays a vital role in enabling a mobile network to interconnect wired networks with the QoS support. It has become quite a challenge in mobile networks, like mobile ad-hoc networks, to identify a path that fulfils the QoS requirements, regarding their topology and applications. The QoS routing feature can also function in a stand-alone multi hop mobile network for real-time applications. The chief aim of the QoS aware protocol is to find a route from the source to the destination that fulfils the QoS requirements. In this paper we present a new energy and delay aware routing method which combines Cellular automata (CA) with the Genetic algorithm (GA). Here, two QoS parameters are used for routing; energy and delay. The routing algorithm based on CA is used to identify a set of routes that can fulfill the delay constraints and then select a reasonably good one using GAs. The results of Simulation show that the method proposed produces a higher degree of performance than the AODV and another QoS method in terms of network lifetime and end-to-end delay.
This study presents a new method for handwritten keyword spotting. The innovation in this paper is to provide a model based on neural network architecture and an output based on the margin. At first, a neural network is designed such that its output determines whether a test word as an input is spotted or rejected. The intended neural network has one input layer, two middle layers, and one output layer. Another innovation in this study is optimising neural network weights based on swarm optimisation method. This optimisation model is used to train the neural network, so that the output has adequate margin for classification. The new components of the proposed classifier include new particle coding and new fitness function. Two layers are considered in coding particle, one for activating and deactivating neural network nodes and the other layer for acquiring proper values for weights. Different experiments with variety of parameters were designed for the multi-layer perceptron neural network. The experiments on three datasets: AMA Arabic dataset, IAM English dataset, and IFN/Farsi dataset yielded 83, 77, and 69% values, respectively, in the best condition. The results demonstrate that the proposed method has been better than the previous ones.
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