Today, Sales forecasting plays a key role for each business in this competitive environment. The forecasting of sales data in automobile industry has become a primary concern to predict the accuracy in future sales. This work addresses the problem of monthly sales forecasting in automobile industry (maruti car). The data set is based on monthly sales (past 5 year data from 2008 to 2012). Primarily, we used two forecasting methods namely Moving Average and Exponential smoothing to forecast the past data set and then we use these forecasted values as a input for ANFIS (Adaptive Neuro Fuzzy Inference System). Here, MA and ES forecasted values used as input variable for ANFIS to obtain the final accurate sales forecast.Finally we compare our model with two other forecasting models: ANN (Artificial Neural Network) and Linear Regression. Empirical results demonstrate that the ANFIS model gives better results out than other two models.

During the past few years, there has been increasing interest in the design of energy efficient protocols for wireless ad-hoc networks. Node within an ad hoc network generally relies on batteries (or exhaustive energy sources) for power. Since these energy sources have a limited lifetime, power availability is one of the most important constraints for the operation of the ad hoc network. Therefore energy efficiency is of vital importance in the design of protocols for the application in such networks and efficient operations are critical to enhance the network lifetime. A routing protocol that does not take into account of congestion control will result in usage of paths that are already heavy in traffic load. It will add more burdens on the energy consumption to these paths and indirectly lead to imbalanced energy consumption of the whole network. The nodes in a high traffic load path will 'die' off faster than nodes in paths that have lower traffic load. AOMDV protocol is provided the alternative path from source to destination so that if one path is congested another path can be use to send the packet from source to destination and thus reduce energy consumption. in these research work we are using threshold value to choose some path from the selected path then average energy scheme is used to choose the path for packet delivery.

To ride the tide of change which is inevitable, innovations are necessary. By using the concept of virtualization most of enterprises are trying to reduce their computing cost. This demand of reducing the computing cost has led to the innovation of Cloud Computing. Nowadays organizations recognized cloud for it different attractive property such as economically attractive and use it to host their services. So that their services available easily and economically to their users. But also many organization put security in their top concern before adopting the cloud service. One of the most significant problem that associated with cloud computing is cloud security that drawn a lot of analysis and research within past few years. Inside the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the actual prognosis associated with zombie exploration problems is exceedingly hard. This is because cloud users might deploy somewhat insecure purposes on the exclusive products. NICE is a Network Intrusion detection and Countermeasure selection in virtual network systems (NICE) design to establish an intrusion detection framework which is defense-in-depth in nature. Into the intrusion detection processes an attack graph analytical procedures is incorporated by NICE for better attack detection. In this paper we proposed to implement NICE-A as a host based agent instead network based so the data delivery time between sender and intended destination is saved as NICE-A is implemented in destination (which is cloud server in our case) and for large amount of data this definitely shows improvement in computation time. Moreover as NICE-A is implemented as host based so CPU utilization is also improved.