In hydrology and water resource engineering, water flow forecasting is of great importance for getting the information about the river engineering, dam structure design and waterrelated inflow demand management. In order to prevent flooding on the downstream side of the river during the rainy season, sufficient outflow from a barrage should be maintained. It is very difficult to predict the desired water flow using physically-based models and conventional regression-based methods due to the nonlinear and fuzzy nature of hydrological activity and scarcity of relevant data. These traditional methods are incapable to handle the complex non-linearity and non-stationarity process of water flow. Thus, the aim of this study is to develop intelligent hybrid artificial intelligence model, namely genetic algorithm based Artificial Neural Network (GA-ANN) for monthly Water Flow prediction in the Mahanadi river system. All parameters associated with the artificial neural network (ANN) model are optimized simultaneous automatically using Genetic Algorithm (GA) for prediction of the Water flow. Twenty years monthly data from Mahanadi river in India has collected for the development of this GA-ANN model. The hydro-climatical parameters like Rainfall, Water Level, Sediment yield and Temperature are used for the development of the ANN prediction model of Water Flow at Tikarapara gauging station which is extreme last downstream station in Mahanadi River basin, India. The performances of the GA-ANN model were compared with Artificial Neural Network (ANN) model for checking the estimation capability of the model. The obtained results revealed that the proposed novel GA-ANN model is capable to predict river flow with satisfactory performances and provided better results than the ANN model. This modelling approach can be potentially used for prediction of water flow discharge in the river system where measurement of water flow is unavailable.

Cognitive Radio Networks is the focused research area where optimal usage of resources takes place. The spectrum scarcity is cause for searching new techniques to serve resources to the users other than privileged (primary) which are called as Secondary users. By using interleaving paradigm, the secondary users also served the license bands without interrupting Primary users. While serving the Secondary users, the preemption of secondary users takes place because of high priority of Primary users and they have to leave the system if no buffer is provisioned for the interrupted secondary users. The focus of this research paper is to compare the impact of keeping a buffer to hold the interrupted Secondary users with the case of providing no buffer in the System. A comparative study reveals the necessity and role of the buffer in the System for the provision of better service to Secondary users. The main focus of this paper is the consideration of the system performance with and without the buffer mechanism for interrupted secondary users and this scenario is modeled as M/M/C/K analytical queuing model. The differential difference equations are derived for both the cases and based on these equations, performance metric average queue length and waiting time are calculated. Finally numerical illustrations are carried out for different values of primary, secondary user arrival rates, service rate, buffer size and number of channels. The analytical results are presented through tables and graphs were shown to draw the conclusions.

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Today the MapReduce frameworks become the standard distributed computing mechanisms to store, process, analyze, query and transform the Bigdata. While processing the Bigdata, evaluating the performance of the MapReduce framework is essential, to understand the process dependencies and to tune the hyper-parameters. Unfortunately, the scope of the MapReduce framework in-built functions is limited to evaluate the performance till some extent. A reliable analytical performance model is required in this area to evaluate the performance of the MapReduce frameworks. The main objective of this paper is to investigate the performance effect of the MapReduce computing models under various configurations. To accomplish this job, we proposed an analytical transient queuing model, which evaluates the MapReduce model performance for different job arrival rates at mappers and various job completion times of mappers as well as the reducers too. In our transient queuing model, we appointed an efficient multi-server queuing model M/M/C for optimal waiting queue management. To conduct the experiments on proposed analytics model, we selected the Bigdata applications with three mappers and two reducers, under various configurations. As part of the experiments, the transient differential equations, average queue lengths, mappers blocking probability, shuffle waiting probabilities and transient states are evaluated. MATLAB based numerical simulations presented the analytical results for various combinations of the input parameters like λ, µ1 and µ2 and their effect on queue length.