List of Articles آنتولوژی

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Architectural tactics identification in source code based on a semantic approach
  Ehsan Sharifi Ahmad عبداله زاده بارفروش
  Software systems are alive as long as they can be changed and used. Changing the source code without considering the consequences can lead to the architectural erosion of software systems. Architectural erosion gradually makes the system unchangeable and moves it toward More

  Software systems are alive as long as they can be changed and used. Changing the source code without considering the consequences can lead to the architectural erosion of software systems. Architectural erosion gradually makes the system unchangeable and moves it towards deterioration. Architectural decisions in the source code are usually made using architectural tactics. These tactics are fine-grained decisions that are made to achieve a certain quality attribute. Recognizing these tactics in the source code, allows developers to change the code while knowing the implementation location of these decisions. This slows the architectural erosion process and delays the system's movement towards deterioration. Thus, this paper introduces a method based on semantic web for recognizing the architectural tactics presented in the source code. Based on this approach, the new concept of microtactic is introduced that increases the possibility of recognizing architectural tactics using a semantic web and ontological approach. The evaluation results show that this method in comparison with other similar methods recognizes the tactics with higher precision and quality. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - A semantic sentiment recognition model based on ontology and cellular deep learning automata
  Hoshang Salehi Reza Ghaemi maryam khairabadi
  Today, social networks and communication media play a significant role in the daily life of users. Users talk and exchange information in different fields in social networks. In the sentences and comments of users, there are negative and positive feelings in relation to More

  Today, social networks and communication media play a significant role in the daily life of users. Users talk and exchange information in different fields in social networks. In the sentences and comments of users, there are negative and positive feelings in relation to the news of the day, current events, etc., and recognizing these feelings faces many challenges. So far, various methods such as machine learning, statistical approaches, artificial intelligence, etc., have been proposed for the purpose of detecting emotions, which despite their many applications; But they have not yet been able to have acceptable accuracy, transparency and accuracy. Therefore, in this article, an ontology-based semantic analysis model using cellular deep learning automata based on GMDH deep neural network is presented. Ontology approach is used to select salient features based on production rules and cellular deep learning automata is used to classify user sentiments. The main innovation of this article is the proposed algorithm that a deep learning method is developed to process only one expression and then by transferring it to the field of cellular automata, parallel or distributed processing is provided. In this article, the data sets of Amazon customers, Twitter, Facebook, fake news of COVID-19, Amazon and fake news network are used. By simulating the proposed method, it was observed that the proposed method has an average improvement of 3% compared to other methods Manuscript profile