List of Articles شبکه های عصبی

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  1 - A systematic review of artificial neural network applications in supply chain management
    Aref  Toghroljerdi pooria malekinejad
  Nowadays, the success rate of companies/organizations in the competitive market is the performance of their supply chain managment. Various techniques have been utilized to improve it, which one of the most widely used methods to solve these problems is artificial neura More

  Nowadays, the success rate of companies/organizations in the competitive market is the performance of their supply chain managment. Various techniques have been utilized to improve it, which one of the most widely used methods to solve these problems is artificial neural network. The purpose of this study is to systematically review the various applications of artificial neural networks in solving the problems of different parts of the supply chain. Hence, by using the literature review, the key vocabulary of the link between the two domains was identified. Using the keywords extracted from the research literature, a search was made between the Scopus databases and Web-based Science. By searching in these databases, articles related to the application of artificial neural network in different areas of supply chain have been extracted. Finally, the articles were filtered using a variety of tools and then high-ranking papers were identified. Using important articles identified, various categories of artificial neural network applications were implemented in supply chain management. The results of this study indicate that artificial neural networks have been most used in solving engineering, computer science and business issues Manuscript profile
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  2 - Secondary porosity index effect on improving approaches permeability estimation from petrophysical logs utilizing artificial intelligent
  سجاد کاظم شیرودی مرتضی خانیان
  Abstract Permeability estimation using core data and petrophysical logs is a conventional approach which bears high uncertainty especially in carbonate reservoir characterization. In essence, the problem consists not only due to coring expenses rate, but also ambig More

  Abstract Permeability estimation using core data and petrophysical logs is a conventional approach which bears high uncertainty especially in carbonate reservoir characterization. In essence, the problem consists not only due to coring expenses rate, but also ambiguity in finding proper explicit log correlation to core data. Moreover, utilizing the correlated formula in wells without core data can pose errors. In this research the permeability was estimated from conventional petrophysical logs and it was calibrated with permeability obtained from core lab experiments. Applied intelligent systems are the matter of this research for permeability values estimation. To construct permeability estimation model, three techniques have been applied including conventional ANN, the Gonzalez, and Hambalek fuzzy logic techniques. These methods were applied in two wells drilled in Surmeh reservoir in Balal field to establish ANN and to derive a relation between core and well. The models were applied in control well in order to check the reliability and capability of models to estimate representative permeability value. The result showed however three foresaid techniques for permeability estimation were successful the secondary porosity distributed the correlation due to its reduction effect on permeability so that they were not interconnected. Therefore this effect was omitted using secondary porosity index in which the permeability estimation were improved and were estimated close to core value. Manuscript profile
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  3 - Compilation of artificial neural networks and the thinned Fault likelihood auto-tracking algorithm, for identification, interpretation and extraction of faults
  Alireza Ghazanfari Hoseyn Mohammadrezaei Hamidreza Ansari
  Fault identification and investigating their evolution is of special importance in the exploration and development of hydrocarbon resources. Success in exploration and development of hydrocarbon fields, need to recognition of petroleum systems and in this regard one of More

  Fault identification and investigating their evolution is of special importance in the exploration and development of hydrocarbon resources. Success in exploration and development of hydrocarbon fields, need to recognition of petroleum systems and in this regard one of the most important topics is identifying faults and their extension condition as a main fluid migration path, specially in deeper zones. Faults and fractures have crucial role in making high permeable and porous segments and cut reservoir and cap rock in the fluid migration path. In addition, for maximizing the production of hydrocarbon from reservoirs and also for reducing the risk of drilling, it is necessary to gain information about geometry and nature of faults of reservoirs. In this paper, the purpose is investigating the performance of combination of neural networks and Fault Likelihood auto-tracking algorithm for identification and interpretation of faults in seismic data. At first using the Dip-steering feature of software, the early filter for accurate identification of dip of structures in the data, have been designed and applied. Then with designing and applying the appropriate filters, the seismic data have been improved. After that proper seismic attributes for fault identification have been calculated from seismic data. With picking fault and non-fault points from data, a supervised neural network using the selected attributes was formed and after training the network, the appropriate output achieved. Then the output of neural network has been used as a input for Thinned Fault Likelihood auto-tracking algorithm. The output of this part contains a volume of tracked faults. Finally using sub-tools of TFL and optimal setting of parameters, 3D fault planes has been interpreted and extracted. Manuscript profile
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  4 - Using Sentiment Analysis and Combining Classifiers for Spam Detection in Twitter
  mehdi salkhordeh haghighi Aminolah Kermani
  The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one More

  The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one of the latest spam detection techniques and its combination with sentiment analysis. Using the word embedding technique, we give the tweet text as input to a convolutional neural network (CNN) architecture, and the output will detect spam text or normal text. Simultaneously, by extracting the suitable features in the Twitter network and applying machine learning methods to them, we separately calculate the Tweeter spam detection. Eventually, we enter the output of both approaches into a Meta Classifier so that its output specifies the final spam detection or the normality of the tweet text. In this study, we employ both balanced and unbalanced datasets to examine the impact of the proposed model on two types of data. The results indicate an increase in the accuracy of the proposed method in both datasets. Manuscript profile