Showing 6 results for Artificial Neural Network
Mehdi Khashei , Farimah Mokhatab Rafiei, Mehdi Bijari ,
Volume 23, Issue 4 (11-2012)
Abstract
In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficient once in financial markets. In this paper, the performance of four interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressive integrated moving average (FARIMA), hybrid ANNs and fuzzy (FANN) and Improved FARIMA models are compared together. Empirical results of exchange rate forecasting indicate that the FANN model is more satisfactory than other those models. Therefore, it can be a suitable alternative model for interval forecasting of financial time series.
Yahia Zare Mehrjerdi, Tahereh Aliheidary,
Volume 25, Issue 1 (2-2014)
Abstract
Job Satisfaction (JS) plays important role as a competitive advantage in organizations especially in helth industry. Recruitment and retention of human resources are persistent problems associated with this field. Most of the researchs have focused on the job satisfaction factors and few of researches have noticed about its effects on productivity. However, little researchs have focused on the factors and effects of job satisfaction simultanosly by system dynamics approaches.In this paper, firstly, analyses the literature relating to system dynamics and job satisfaction in services specially at a hospital clinic and reports the related factors of employee job satisfaction and its effects on productivity. The conflicts and similarities of the researches are discussed and argued. Then a novel procedure for job satisfaction evaluation using (Artificial Neural Networks)ANNs and system dynamics is presented. The proposed procedure is implemented for a large hospital in Iran. The most influencial factors on job satisfaction are chosen by using ANN and three differents dynamics scenarios are built based on ANN's result. . The modelling effort has focused on evaluating the job satisfaction level in terms of key factors which obtain from ANN result such as Pay, Work and Co-Workers at all three scenarios. The study concludes with the analysis of the obtained results. The results show that this model is significantly usfule for job satisfaction evaluation
Keywords: Job Satisfaction, system dynamics, Artificial Neural Network (ANN), healthcar field.
Hiwa Farughi, Ahmad Hakimi, Reza Kamranrad,
Volume 29, Issue 1 (3-2018)
Abstract
In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA). Results based on comparative studies between four methods based on ANN and by considering the several conditions for the effective parameters in ANN show that, the generalized regression method is the best method for predicting the availability. Furthermore, results of the EWMA and three mentioned TSM are also show the better performance of MA model for predicting the availability values in future periods.
Mahdieh Akhbari,
Volume 29, Issue 2 (6-2018)
Abstract
The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of Earned Value Management (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using Monte Carlo simulation, and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an artificial neural network is used as efficient data mining methods to estimate the project time and cost at completion.
Saadat Ali Rizvi, Wajahat Ali,
Volume 32, Issue 3 (9-2021)
Abstract
The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (vc), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (Ra and Rq) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method. ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (Ra and Rq) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.
Hojjat Pourfereidouni, Hasan Hosseini-Nasab,
Volume 34, Issue 2 (6-2023)
Abstract
This paper proposes a data-driven method, using Artificial Neural Networks, to price financial options and compute volatilities, which speeds up the corresponding numerical methods. Prospects of the Stock Market are priced by the Black Scholes model, with the difference that the volatility is considered stochastic. So, we propose an innovative hybrid method to forecast the volatility and returns in Stock Market indices, which declare a model with a generalized autoregressive conditional heteroscedasticity framework. In addition, this research analyzes the impact of COVID-19 on the option, return, and volatility of the stock market indices. It also incorporates the long short-term memory network with a traditional artificial neural network and COVID-19 to generate better volatility and option pricing forecasts. We appraise the models' performance using the root second-order quadratic function means of the out-of-sample returns powers. The results illustrate that the autoregressive conditional heteroscedasticity forecasts can serve as informative features to significantly increase the predictive power of the neural network model. Integrating the long short-term memory and COVID-19 is an effective approach to construct proper neural network structures to boost prediction performance. Finally, we interpret the sensitivity of option prices concerning the market or model parameters, which are essential in practice.
