List of Articles Artificial Neural Networks

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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 - DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System
  Shima Mehrabi Seyed Abolghassem Mirroshandel Hamidreza  Ahmadifar
  10.7508/jist.2019.03.005
  With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language pro More

  With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language processing researchers. Today, with improvement in processing power and the development of computational tools, efforts to improve the performance of the summarization system is continued, especially with utilizing more powerful learning algorithms such as deep learning method. In this paper, a novel multi-lingual multi-document summarization system is proposed that works based on deep learning techniques, and it is amongst the first Persian summarization system by use of deep learning. The proposed system ranks the sentences based on some predefined features and by using a deep artificial neural network. A comprehensive study about the effect of different features was also done to achieve the best possible features combination. The performance of the proposed system is evaluated on the standard baseline datasets in Persian and English. The result of evaluations demonstrates the effectiveness and success of the proposed summarization system in both languages. It can be said that the proposed method has achieve the state of the art performance in Persian and English. 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