In training and examining professional drivers who serve as commercial drivers for trucks and coaches, studying the effects of training courses on their success rate for professional qualifications is a crucial concern for transport authorities in developing training programs. To this purpose, the statistical methods of Kolmogorov-Smirnov and paired two-sample mean analysis have been utilized to investigate the statistical similarity of success rates for two groups of drivers who receive permission by taking training courses and those who receive it without taking training courses. Data for commercial drivers across twenty-one provinces of the West-Asian country of Iran has been collected for a year and categorized into two groups and twenty-one observations. The results revealed that their distribution functions and the mean success rates are not different for the two groups of drivers. Since the results of success rates are the same, 1) training courses do not have enough efficiency to affect success rates, or 2) exams could not adequately evaluate the skill and knowledge of drivers. Therefore, transport authorities are recommended to redesign training courses and exams for drivers interested in serving as commercial drivers.

Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PDF

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