List of Articles Neural Network Model

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  1 - Prediction of the results of implementation of air pollution control strategies using the Geo-Artificial Neural Network for Tehran metropolis
  Mehran Ghoddousi Farideh Atabi Jafar Nouri Alireza Gharagozlu
  10.22034/jest.2019.23618.3275
  Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the resul More

  Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the result of urban air pollution controlling strategies in Tehran metropolis using a multi-dimensional decision support system. Method: First, the most appropriate air pollution control strategies were selected based on existing conditions and structures in each zone of the city and then weighed according to selected criteria. Based on the spatial monitoring of air pollution formation patterns in the past and present time, as well as the analysis of their effects, the results of implementing air pollution control strategies were simulated using Geo-Artificial Neural Network models. In the next step, variables of time series and uncertainty variables were simulated for predicting the potential future air pollution patterns and finally, the results of the defined control strategies were evaluated based on spatial thematic layers. Findings: Definition of final clusters of air quality control strategies, weighting and ranking of the selected policies based on defined criteria have been the first findings of this research. Also, extraction of time series zoning based on the data collected during a four-year period, as well as simulation of the baseline scenario models and spatial data layers of their output were among the achievements of this study. Finally, the modeling of the predictive variables, design of the air quality control software and the prediction of the results of the the implementation of air pollution control strategies were presented. The results showed that by applying the Geo-Artificial Neural Network models (GANN), the urban managers could effectively predict the results of implementing the air pollution control strategies. Discussion and Conclusion: The results of this study showed that the spatio-temporal analysis supports the process of evaluation and prediction of the effects of pollution and can be used to determine the best pollution control strategies for the zones affected by air pollution. The final results of GANN models indicate that if the selected strategies are implemented based on the scenarios defined, in the "optimistic scenario", air quality in all areas of Tehran is completely stable and remains healthy, while in the "ordinary scenario" will reduce the level of air pollution up to 70 percent in the autumn and winter season if the selected strategies are implemented compared to the lack of implementation of control plans. The final model of the verification process model also confirmed that the pattern of pollution predicted by the model in each of the urban areas had a proper trend and adaptation compared to the pattern of contamination obtained from the actual results of the field data.   Manuscript profile
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  2 - Developing a model for predicting the Tehran Stock Exchange index using a combination of artificial neural network and Markov hidden model
  Leila Talaie Kakolaki Mehdi Madanchi Taghi Torabi Farhad Ghaffari
  The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its More

  The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%. Manuscript profile
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  3 - Applying Kinetic Spectrophotometric Method and Artificial Neural Network Model for Determination of Metronidazole with Albizia Lebbeck Leaves-capped AgNPs Sensor in Blood and Urine Samples
  Elham Pournamdari Leila Niknam Farzaneh Marahel
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  4 - The Evaluation of the Capability of the Regression & Neural Network Models in Predicting Future Cash Flows
  Bahman Talebi Rasol Abdi Zohreh Hajiha Nader Rezaei
  10.22034/amfa.2020.1876840.1277
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  5 - Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)
  Bahman Talebi Rasoul Abdi Zohreh Hajiha Nader Rezaei
  10.22034/amfa.2021.1910900.1493
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  6 - Environmental sustainability assessment with emphasis on drought and water resources using multi-criteria artificial neural network technology (case study of Babak city)
  محمد ابراهیم عفیفی ahmad mangeli meydook ali vakhsoori
  In this study, first, by studying the research, criteria and sub-criteria were identified that are effective in terms of environmental sustainability. After the Delphi stages, the criteria of environmental resources and services, environmental health and energy were sel More

  In this study, first, by studying the research, criteria and sub-criteria were identified that are effective in terms of environmental sustainability. After the Delphi stages, the criteria of environmental resources and services, environmental health and energy were selected as the most important criteria for assessing environmental sustainability in Babak, then using the neural network model to analyze and evaluate the environmental sustainability of Babak. In this study, drought in Babak city was analyzed with a SPI index of drought during a statistical period of 32 years 1361-1392. This index is specifically for time series six; Twelve and forty-eight months were calculated. The city of Babak has been facing drought during the statistical period of thirty-two years, especially the last seven years, and on an annual scale of six months, most of its droughts are mild to moderate droughts. But in the long-term Myas 48 months, 75% of the droughts were severe and very severe, which shows a high relationship with the quantitative and qualitative decline of groundwater in this area. Manuscript profile
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  7 - Evaluating the effects of climate change on Lar Basin Water Resources Using SWAT Model and comparing its results with Bayesian Networks and Hybrid Intelligent Models
  Mahsa Solimani Puor Amirpouya Sarraf
  Iran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rai More

  Iran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rainfall and runoff in future periods with the help of LARS-WG statistical model and SWAT hydrological conceptual model for Lar Basin. To estimate the flow rate of the river, the performance of Bayesian network and the combined wavelet-neural network model are also examined. After entering the rainfall and temperature information of the region, runoff was simulated for two hydrometric stations of Gozeldareh and Plour and the outflow runoff of Plour station between 1979 to 2018 was calibrated and validated as a control point. In order to evaluate the efficiency of the models, the criteria of Nash-Sutcliffe and explanation coefficient are used. According to climate models, the highest temperature increase in the final period and under the RCP8.5 climate scenario shows about 10% increase in temperature in spring and winter. Finally, among these models, the physical model with an average annual prediction of 6.04 cubic meters per second according to the observation period, showed a decrease in runoff. Manuscript profile
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  8 - Utilization of Artificial Neural Networks for Determining the Overflow Discharge of Marun Dam
  Ebrahim Nohani valiolah partovi zia
  For more accurate measurement of the water flow, it has been always attempted to design structures with least errors and highest accuracy. Nowadays, the use of artificial neural networks (ANN) models has been rapidly grew mainly due to the fact that these models are not More

  For more accurate measurement of the water flow, it has been always attempted to design structures with least errors and highest accuracy. Nowadays, the use of artificial neural networks (ANN) models has been rapidly grew mainly due to the fact that these models are not confined to the physical parameters. Artificial neural networks are based on use of embedded knowledge between input and output variables of a problem, regardless of physical aspects and these networks are able to extract inherent relation of the input and output and they can generalize the obtained relation to other situations and cases. In the present research, the information related to the overflow of Marun Storage Dam was adopted. The input parameters of ANN model are as follows: day, month, water surface elevation, water sharing percent and output parameters overflow discharge of storage dam. The models employed in artificial neural networks include FF, JEN, MLP and RBF. Moreover, the genetic algorithm (GA Manuscript profile
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  9 - Designing Credit Risk Early-warning System for Individual and Corporate Customers of the Banks using Neural Network Models, Survival Probability Function and Support Vector Machine
  Roya Derakhshani Mirfeiz Fallah hosein jahangirnia Reza Gholami jamkarani Hamidreza kordlouie
  Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in b More

  Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article is to estimate the credit risk of individual and corporate customers. In this study, the statistical information of 400 individual customers and7500 corporate customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on their personal, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. The comparison of the results of the prediction accuracy shows the higher explanatory power of the support vector machine model and the use of the survival probability function than the simple neural network model for both groups of customers. Manuscript profile
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  10 - Modeling of aging process for pre-rolled Ni-Span C 902 superalloy
  Mohammad Rasool Moazami Godarzi Maryam Morakabati Seyed Mahdi Abbasi Hassan Badri
  The age-hardening curves of hardness measurements obtained for Ni-Span C 902 superalloy under different amounts of cold work, aging temperatures and times showed leveling and pronounced oscillations, indicating instability and reflecting a competition between the effect More

  The age-hardening curves of hardness measurements obtained for Ni-Span C 902 superalloy under different amounts of cold work, aging temperatures and times showed leveling and pronounced oscillations, indicating instability and reflecting a competition between the effect of sub-structure coarsening and the effect of solute drag and precipitation hardening. An artificial neural network (ANN) was used to model the nonlinear relationship between the parameters of the aging process and the corresponding hardness measurements. The predicted values of the ANN are in accordance with the experimental data. Results showed that the non-deformed and 50 pct cold rolled alloy exhibited a maximum hardness at a tempering parameter of 22 and 21, respectively. Manuscript profile