List of Articles SVM

• Open Access Article
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  1 - New Method to Improve Illumination Variations in Adult Images Based on Fuzzy Deep Neural Network
  Sasan Karamizadeh abouzar arabsorkhi
  20.1001.1.27170411.1398.11.41.3.8
  In the era of the Internet, recognition of adult images is important to children's physical and mental protection. It is a challenge to recognize adult images with changes in the illumination and skin color. In this paper, we proposed a new method for solving illumi More

  In the era of the Internet, recognition of adult images is important to children's physical and mental protection. It is a challenge to recognize adult images with changes in the illumination and skin color. In this paper, we proposed a new method for solving illumination normalization with skin color classification in the diagnosis of the adult image. In this paper, the deep fuzzy neural network method is utilized to improve the illumination normalization of adult images, which has improved the recognization of adult images is utilized. Using Xception to dividing the images and reduce the illumination variations in each part separately, which makes it possible to reduce the illumination variation in the whole image without losing details. In addition, the advanced color combination algorithm based on Gaussian-KNN algorithm is used for skin color classification, a non-parametric method is used for classifications and regressions. Finally, the SVM algorithm is utilized for image classification. In this paper, 33,000 different types of images are collected from the Internet. The results show that the proposed method of 1/3 has improved the accuracy of the recognization. Manuscript profile
• Open Access Article
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  2 - An Intelligent Pricing System for Cloud Services aims at Increasing Implementation Simplicity and Flexibility
  Mahboubeh Zandieh Sepideh Adabi Samaneh Yazdani
  20.1001.1.27170411.1401.14.53.6.6
  Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goa More

  Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goals: 1) low computation complexity, 2) high accuracy, and 3) high implementation simplicity in real cloud environments. CMM (Cloud Market Maker) is one of the most popular dynamic pricing models that has two advantages of computation accuracy and the possibility to implement in the real cloud environments. This model calculates the bid price based on a linear function. In designing this linear function, the parameters: buyer’s urgency, number of competitors and number of opponents are considered. Despite the advantages of this pricing function, the importance ratio of the constructor parameters of it is considered the same in various market conditions. Ignoring this issue reduces both system flexibility and computation accuracy in tangible changes in the cloud market. Therefore, the authors of this paper focus on designing a new cloud market-aware intelligent pricing system (which developed in customer side of the market) to tackle the mentioned problem. At the same time, high implementation simplicity of the proposed system should be guaranteed. For this purpose, an agent-based intelligent pricing system by combining support vector machine (SVM) and hierarchical analysis process (AHP) techniques is proposed. Simulation results show the better performance of the proposed solution which is named as DPMA in comparison to CMM. Manuscript profile
• Open Access Article
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  3 - Presenting a web recommender system for user nose pages using DBSCAN clustering algorithm and machine learning SVM method.
  reza molaee fard Mohammad mosleh
  20.1001.1.27170411.1402.15.57.6.1
  Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can s More

  Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent. Manuscript profile