Abstract: Volatility Forecasting is an interesting challenging topicin current financial instruments as it is directly associated with profits. There are many risks and rewards directly associated with volatility. Hence forecasting volatility becomes most dispensable topic in finance. The GARCH distributionsplay an important role in the risk measurement and option pricing. In this paper the motive is to measure the performance of GARCH techniques for forecasting volatility by using different distribution model. We have used 9 variations in distribution models that are used to forecast the volatility of a stock entity. The different GARCH distribution models observed in this paper are Std, Norm, SNorm, GED, SSTD, SGED, NIG, GHYP and JSU. Volatility is forecasted for 10 days in advance and values are compared with the actual values to find out the best distribution model for volatility forecast. From the results obtain it has been observed that GARCH with GED distribution models has outperformed all models.

Abstract: Driver's drowsiness is one of the major causes of serious accidents in road traffic. Thus, special effort in searching for better assistant technology has been paid. However, several existing approaches fail to work effectively as the head of a drowsy driver is usually in slanting state. Moreover, the shaking of vehicle or the driver's winking even makes the problem much more complicated. Anyway, head bend posture also signifies a drowsy state. Consequently, this paper proposes a novel approach by considering head nodding behaviour as an input in our detection model. After detecting a human face, some significant facial features are extracted; then, they are used to calculate the predetermined optimal parameters; finally, drowsiness is evaluated based on these thresholds. In our empirical experiments, the proposed algorithm can successfully and accurately detect 96.56% of cases.