List of Articles Partial Least Square Structural Model

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - The Causal Structure Model of Human Resources Productivity, Job Satisfaction, Organizational Citizenship Behavior and Organizational Commitment in Water and Wastewater Industry (Case Study: Alborz Province
  Ali Badizadeh Gholam Reza Rezaifar
  Empowered human resource in any organization is an effective factor for success of organization. Human resource productivity as a key factor is proposed. The Aim of this study is to make causal model for human resource productivity, job satisfaction, organizational citi More

  Empowered human resource in any organization is an effective factor for success of organization. Human resource productivity as a key factor is proposed. The Aim of this study is to make causal model for human resource productivity, job satisfaction, organizational citizenship behavior and organizational commitment. Standard questionnaire as research tool and statistical population of study was official and contract employees of Alborz Province water and Wastewater Company. Partial Least square method is used for modeling. Findings show that there is significant and positive relation between human resource productivity with job satisfaction, organizational citizenship behavior and organizational commitment .Also, there are significant and positive relation between organizational commitment with job satisfaction and organizational citizenship behavior .Finally, there is significant and positive relation between job satisfaction and organizational citizenship behavior. Further research advices have been proposed at the end. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Using different learning algorithms in the stock price prediction by using neural networks
  Reza Kiyani Mavi Kamran Sayadi Nik
  Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerableattention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity,dynamics and turb More

  Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerableattention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity,dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it isdifficult to tackle the stock price prediction problems effectively by using only single soft computing technique.In this research, in the first step, the possibility of predicting stock price of National Iranian Copper Industries Company wasstudied. Then, for predicting of stock price after one day neural ¬network of MLP by learning algorithm of Levenberg-Marquardt were used. Then optimize structure of neural network was trained with the standard BP algorithm, the learningrate is 3/0 has the best performance. And for this learning rate, sensitive of standard BP algorithm was calculated to minimizelocal. At the end, standard BP algorithm with momentum is used. The results showed that predicting by standards BPalgorithm with momentum is better than the standard BP algorithm. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Using different learning algorithms in the stock price prediction by using neural networks
  Ahmad Esa Khani Akbar Hassan pour Sima Naghde froshha
  Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerableattention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity,dynamics and turb More

  Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerableattention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity,dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it isdifficult to tackle the stock price prediction problems effectively by using only single soft computing technique.In this research, in the first step, the possibility of predicting stock price of National Iranian Copper Industries Company wasstudied. Then, for predicting of stock price after one day neural ¬network of MLP by learning algorithm of Levenberg-Marquardt were used. Then optimize structure of neural network was trained with the standard BP algorithm, the learningrate is 3/0 has the best performance. And for this learning rate, sensitive of standard BP algorithm was calculated to minimizelocal. At the end, standard BP algorithm with momentum is used. The results showed that predicting by standards BPalgorithm with momentum is better than the standard BP algorithm. Manuscript profile