List of Articles artificial neural network

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  1 - Identify and assess the relative importance of knowledge management strategies by using ANN (Case study Knowledge base Software Companies)
  Saeedeh khabbazkar Mohsen Shafiei Nikabadi مائده  دهقان
  Abstract: Knowledge management is an important resource for any organization. Organizations to implement knowledge management strategies, improve innovation in processes, activities, products and services. The aim of this study is to identify the key strategies of knowl More

  Abstract: Knowledge management is an important resource for any organization. Organizations to implement knowledge management strategies, improve innovation in processes, activities, products and services. The aim of this study is to identify the key strategies of knowledge management by ANN .The innovative aspect of the research is, the use of artificial neural networks (ANN) to rank the strategies of knowledge management. The population consists of the all employees of the   knowledge based software companies in Tehran, that the total questionnaires were distributed, only 123 were usable. This study is practical from the objective aspect, and descriptive-survey from data collection method aspect. Data from the surveys and questionnaires obtained and then by using the ANN techniques h as been investigated the research objectives.  Results and ANN outputs indicated that sequencely, explicit knowledge startegy is the most important criteria of Knowledge management strategy and tacit khowledge, internal and external strategy are the next priorities  knowledge based  software companies  are located in Tehran.  Manuscript profile
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  2 - Comparison of support vector machine and artificial neural network classification methods to produce landuse maps (Case study: Bojagh National Park)
  Mahsa Abdoli Laktasaraei Maryam  Haghighi khomami
  10.29252/.5.8.47
  National parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classi More

  National parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classify satellite imagery in remote sensing, selecting an appropriate classification algorithm is very important in achieving the accurate results. In this research, a more accurate algorithm was determined by comparing the classification accuracy of two artificial neural network and support vector machine algorithms, and it was used to examine the process of the land use changes. The present study was performed in Boujagh National Park, in the Guilan Province, during the years 2000 to 2017, using satellite imagery ETM and OLI of Landsat 7 and 8. The results of the research revealed that the support vector machine algorithm with overall accuracy and Kappa coefficient of 86.42 and 0.83 respectively for the year 2000 and, 90.65 and 0.88 for the year 2017, classified the satellite images more precisely, in comparison with the artificial neural network algorithm with overall accuracy and Kappa coefficient of 83.71 and 0.80 respectively for the year 2000 and overall accuracy and Kappa coefficient of 89.25 and 0.87 for the year 2017. Therefore, the land use maps of the support vector machine algorithm were used to determine the land use changes. The study of land use change by this method concluded that the areas of the waterbody, sea, grassland and agriculture have decreased and marshland, woody and bare lands classes showed an increase during the study period. Manuscript profile
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  3 - Comparison of the MLP and RBF Neural Networks for the Determination of Confined Aquifer Parameters
  Tahereh Azari Nozar Samani
  In this paper, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) are designed for the determination of confined aquifer parameters: transmissibility and storage coefficient. The networks are trained for the well function of c More

  In this paper, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) are designed for the determination of confined aquifer parameters: transmissibility and storage coefficient. The networks are trained for the well function of confined aquifers. By applying the principal component analysis (PCA) on the training data sets the topology of the MLP and RBF networks is reduced and fixed to [1×12×1] and [1×14×1], respectively regardless of number of records in the pumping test data. The networks generate the optimal match point coordinates for any individual real pumping test data set. The match point coordinates are then incorporated with Theis analytical solution (1935) and the aquifer parameter values are determined. The generalization ability and performance of the developed networks is evaluated with 100000 sets of synthetic data and their accuracy is compared with that of type curve matching technique by two sets of real pumping test data. The results showed that though both MLP and RBF networks are able to determine the confined aquifers parameters and eliminate graphical error inherent in the type curve matching technique but the MLP network is more accurate than the RBF network. Therefore, the proposed MLP network is recommended as an accurate automatic and fast procedure for the confined aquifer parameters estimation. Manuscript profile
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  4 - Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm
  حامد ابراهیم خانی مصطفی کاظمی Alireza Pooya Amir Mohammad  Fakoor Saghih
  The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting More

  The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting businesses, entrepreneurs and companies to pay. Accordingly, the purpose of this study was to predict the growth of small and medium enterprises with the combination of neural network and meta-heuristic algorithms. The purpose of this research was applied and based on the method of doing descriptive-modeling work. Statistical population of this research was all small and medium enterprises of Zanjan province. Statistical sample size According to the growth of companies, 158 companies has been designated. In order to collect data in this study, interviews, questionnaires and documents of companies have been used. Validity and reliability of the questionnaire were verified and and using Cronbach's alpha coefficient. In order to analyze the research data using confirmatory factor analysis methods, the neural network of multilayer perceptron, neural network combined with genetic algorithm and neural network combined with particle swarm algorithm have been used. The results show that all three methods are able to predict the growth of the company. Among these three methods, the best predictive method for growth of the company is the neural network combined with the particle swarm algorithm with the least error rate compared to the other two methods. Manuscript profile
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  5 - Evaluation efficiency of the internal structure of decision making units in the past, present and future using dynamic network data envelopment analysis and artificial neural network
  javad niknafs mphammadali keramati jalal haghighatmonfared
  Network data envelopment analysis models and dynamic network data envelopment analysis models cannot evaluated the future performance of the internal structure of decision-making units .In other words, all NDEA and DNDEA models evaluate the past performance of their DMU More

  Network data envelopment analysis models and dynamic network data envelopment analysis models cannot evaluated the future performance of the internal structure of decision-making units .In other words, all NDEA and DNDEA models evaluate the past performance of their DMUs and their internal structure, and measure their efficiency and inefficiency, and ultimately rank them based on that assessment .In this paper, we are going to evaluation the future efficiency of deposit and lending sections in bank branches. In order to notified inefficiencies in the internal structure of a unit before the occurrence, we will prevent it.This approach can change the role of managers from the evaluator to the planner .First, using the literature of the subject and opinion of the experts, the structure of the bank branches and the network variables were determined .Then, the values of variables are forecasted using the artificial neural network for the next two periods.Finally, a DNDEA model is formulated using the values of past periods and predicted values.Using its efficiency, its branches and its internal structure have been evaluated in the past, present and future. Manuscript profile
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  6 - Designing a model based on blockchain technology to strengthen cyber security in the banking industry
  hossein amoozadkhalili neda haghi Reza Tavakkoli-Moghaddam
  10.61186/jstpi.33168.19.76.60
  Designing a model based on blockchain technology to strengthen cyber security in the banking industry is one of the new methods studied in the banking industry to strengthen cyber security. Accordingly, this study seeks to achieve the goal of evaluating a model based on More

  Designing a model based on blockchain technology to strengthen cyber security in the banking industry is one of the new methods studied in the banking industry to strengthen cyber security. Accordingly, this study seeks to achieve the goal of evaluating a model based on blockchain technology to strengthen cyber security in the banking industry based on artificial neural networks. This model is based on a conceptual model used in an MLP neural network simulation that simulates a blockchain-like process. Also, the neural networks created in the block chain have a strong connection and the possibility of breaking them is low. The data became closer to normal distribution after learning, indicating that blockchain technology will be able to provide cyber security. The level of correlation and efficiency presented was also reported and the findings of the study showed that the efficiency related to blockchain technologies after learning reached the level of 770.57 units, which shows that using the MLP method to learn the process of blockchain technology can be Lead to greater efficiency for cyber security. Also, the value of variance is equal to 27.77 and the mean value of computational values is equal to 0.35 and the value of correlation is equal to 0.99. Manuscript profile
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  7 - Application of Artificial Intelligence during History matching in One of fractured oil Reservoirs
  ناصر اخلاقی ریاض خراط صدیقه مهدوی
  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with More

  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with usual methods is too difficult and has either no single response or their response finding need more time and cost due to their nonlinear of the related problems. Because of much uncertainty on information which used in simulators, the results of these simulation models may have lot errors so production data (Pressure, Production Rate, Water Oil Ratio (WOR), Gas Oil Ratio (GOR) and etc.) during reservoir life is used to historical accommodation between simulator results and actual data. The main purpose of this study is investigation and feasibility study of a usual method of artificial intelligence in oil industry, which is based on the soft computing. In this study, Artificial Neural Network (ANN) is used to make a predicting model for bottom hole pressure and for one of the fractured oil reservoirs with the seven years history of production. Some unconditional parameters such as fracture porosity, horizontal and vertical fracture permeability, height of matrix and matrix-fracture dual porosity were applied as input data of the networks, and pressure was applied as an output in network making. Applied data in network making is achieved from the 50 runs with simulator. The conclusion of this study showed that predicting model of ANN with error less than 4% and reduces the time of process, has a good ability to history matching. Manuscript profile
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  8 - Application of Artificial Intelligence during History matching in One of fractured oil Reservoirs
  ناصر اخلاقی Reyaz kharata Sedigheh Mahdavi
  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with u More

  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with usual methods is too difficult and has either no single response or their response finding need more time and cost due to their nonlinear of the related problems. Because of much uncertainty on information which used in simulators, the results of these simulation models may have lot errors so production data (Pressure, Production Rate, Water Oil Ratio (WOR), Gas Oil Ratio (GOR) and etc.) during reservoir life is used to historical accommodation between simulator results and actual data. The main purpose of this study is investigation and feasibility study of a usual method of artificial intelligence in oil industry, which is based on the soft computing. In this study, Artificial Neural Network (ANN) is used to make a predicting model for bottom hole pressure and for one of the fractured oil reservoirs with the seven years history of production. Some unconditional parameters such as fracture porosity, horizontal and vertical fracture permeability, height of matrix and matrix-fracture dual porosity were applied as input data of the networks, and pressure was applied as an output in network making. Applied data in network making is achieved from the 50 runs with simulator. The conclusion of this study showed that predicting model of ANN with error less than 4% and reduces the time of process, has a good ability to history matching. Manuscript profile
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  9 - Permeability estimation using petrophysical logs and artificial intelligence methods: A case study in the Asmari reservoir of Ahvaz oil field
  Abouzar Mohsenipour Bahman Soleimani iman Zahmatkesh Iman  Veisi
  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calcula More

  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters. Manuscript profile
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  10 - Performance evaluation model of educational centers using artificial neural network: One of the government organizations in the country
  zaman azhdari Hosein Abdollahi samad Borzoian morteza Taheri mostafa Ebrahimpour Azbari
  The purpose of this study is “to design a model for evaluating the performance of educational centers of one of the government organizations in the country using artificial neural network”. This is an evaluation study to evaluate the performance of public organizational More

  The purpose of this study is “to design a model for evaluating the performance of educational centers of one of the government organizations in the country using artificial neural network”. This is an evaluation study to evaluate the performance of public organizational educational centers. The information required for this research was collected through parallel information channels such as using the documents of educational centers under the organization and referring to their documents while maintaining the classification level. The statistical population of this study was five educational centers, one of the government organizations that hold educational courses for about 10 thousand personnel between 2013 and 2020; Based on the opinion of experts and the results of related studies, the inputs and outputs of the research were selected and determined. In order to reduce the input and output variables, the structural equation modeling method - partial least squares were used. In order to train the MLP bilayer neural network, the training method was used. After the teaching of neural network. The performance of neural network was examined through test patterns. The value (mean square error) of the MSE corresponds to 13 equal test patterns and 74/7413, which indicated the high accuracy of the trained network. Finally, the performance of the educational centers was ranked based on the analyzed data. Manuscript profile