Fuzzy system

Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange markets. This research first deals with the evaluation of 8 various models to forecasting volatility of stock index using daily data of Tehran stock exchange. The used models include simple ones such as random walk as well as more complex models like Arch and Garch group. Forecasting volatility index method is developed in this paper. This method is based a random walk using a fuzzy logic approach. This method is used to fore-casting volatility of Iran stock exchange index. The proposed method is assessed by comparing other methods such as Moving Average, Random walk… Results show that our proposed method is compatible with existent methods.

A greater community of researchers widely studies fog computing as it reduces the massive data flow to the existing cloud-connected network and performs better for real-time systems that expect a quick response. As the fog layer plays a significant role in a fog-cloud system, all of the devices participating in fog computing must be balanced with appropriate load to upstretched the system performance. The proposed method is founded on a tree-based dynamic resources arrangement mechanism that refreshes the fog clusters created using Fuzzy C Mean (FCM) to increase the speed of resource allocation. With the help of Fuzzy rule-based load calculation and intra-cluster job allocation, the load inside the group is maintained. The system also has the facility of inter-cluster job forwarding, which works on demand. A novel load balancing strategy, Real-Time Flexi Forwarded Cluster Refreshing System (RTFRS) is proposed by which all the tasks can be handled efficiently within the fog cloud system. The proposed system is designed so that overall complexity is not upraised and becomes suitable for fog computing architecture with low processing capacity by maintaining the quality of service. Experimental results show that the proposed model outperforms standard methods and algorithms used in fog computing concerning average turnaround time, average waiting time, resource utilization, average failure rate, and the load on the gateway.