We analyze a Mx/G(a,b)/1 queueing system with fast and slow service rates and multiple vacations. The server does the service with a faster rate or a slower rate based on the queue length. At a service completion epoch (or) at a vacation completion epoch if the number of customers waiting in the queue is greater than or equal to N (N > b), then the service is rendered at a faster rate, otherwise with a slower service rate. After finishing a service, if the queue length is less than 'a' the server leaves for a vacation of random length. When he returns from the vacation, if the queue length is still less than 'a' he leaves for another vacation and so on until he finally finds atleast 'a' customers waiting for service. After a service (or) a vacation, if the server finds atleast 'a' customers waiting for service say ξ, then he serves a batch of min (ξ, b) customers, where b ≥ a. We derive the probability generating function of the queue size at an arbitrary time. Various performance measures are obtained. A cost model is discussed with a numerical solution.

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A novel hybrid approach, integrating stepwise regression analysis (SRA), adaptive neuro-fuzzy inference system (ANFIS) and capital asset pricing model (CAPM), is addressed in this paper for stock portfolio optimization. The SRA is applied to select some of the features from technical indicators that these selected important features improve the performance of the prediction model. In order to create a more accurate forecasting model, ANFIS is applied to forecast future trend values of the Bombay stock exchange (BSE) indices like BSE SENSEX and BSE BANKEX using technical indicators. Stock portfolio optimization aims to determine which of the stocks are to be added to a portfolio based on the investor's needs and changing economic and market conditions. The proposed hybrid optimization technique offers significant improvements in managing investments in a stock portfolio under volatile and uncertainty stock market without the need for human intervention, with diversification procedure, and thus provides acceptable returns with minimal risks. Furthermore, the proposed hybrid SRA-ANFIS-CAPM portfolio model achieves satisfactory performance among the various portfolio models in the presence of fluctuation in a stock market environment.

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Problem statement: As the Variable Bit Rate (VBR) is high in MPEG, the video experienced long delay and unexpected data loss. In wireless channel, due to noise and interference of other signals transmission rate cannot be predicted. Approach: In this paper we proposed an intelligent fuzzy logic controller for transmission of Moving Picture Expert Group (MPEG-4) video signals over wireless channel. A Nero-Fuzzy (NF) controller was used to control the output rate of the buffer so that the signals were transmitted smoothly to wireless channel. Results: Simulation results showed that the use of intelligent fuzzy logic and nero-fuzzy controller improved the data transmission rate and decreased long delay when compared with other conventional methods. Conclusion: We proposed an intelligent fuzzy logic controller which adjusts the transmission rate dynamically so that the transmission loss and delay could be minimized. The traffic shaping buffer is used to prevent excess back-to-back transmission of video signals.