Farhad rad

Work place: Faculty of engineering, department of computer science, Islamic Azad University, Kohgilouye-vaBoyerahmad, Yasouj, Iran

E-mail: f_rad@hotmail.com

Website:

Research Interests: Data Mining

Biography

Farhad Rad received the B.S. degree in science computer from shiraz University Iran, in 2007. He received his M.Sc. degree in Computer Engineering from the Department of Computer Engineering, iran university of science technology, 2011. member of the Computer Society of Iran. received PHD Degrees in Artificial intelligence computer engineering from the department of computer engineering, Iran science of technology university, Tehran, Iran, in 2016.

Author Articles
The Impact of Feature Selection on Meta-Heuristic Algorithms to Data Mining Methods

By Maysam Toghraee Hamid parvin Farhad rad

DOI: https://doi.org/10.5815/ijmecs.2016.10.05, Pub. Date: 8 Oct. 2016

Feature selection is one of the issues that have been raised in the discussion of machine learning and statistical identification model. We have provided definitions for feature selection and definitions needed to understand this issue, we check. Then, different methods for this problem were based on the type of product, as well as how to evaluate candidate subsets of features, we classify the following categories. As in previous studies may not have understood that different methods of assessment data into consideration, We propose a new approach for assessing similarity of data to understand the relationship between diversity and stability of the data is selected. After review and meta-heuristic algorithms to implement the algorithm found that the cluster has better performance compared with other algorithms for feature selection sustained.

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Effect Neural Networks on Selected Feature by Meta Heuristic Algorithms

By Maysam Toghraee Farhad rad Hamid parvin

DOI: https://doi.org/10.5815/ijmsc.2016.03.04, Pub. Date: 8 Jul. 2016

Feature selection is one of the issues that have been raised in the discussion of machine learning and statistical identification model. We have provided definitions for feature selection and definitions needed to understand this issue, we check. Then, different methods for this problem were based on the type of product, as well as how to evaluate candidate subsets of features, we classify the following categories. As in previous studies may not have understood that different methods of assessment data into consideration, We propose a new approach for assessing similarity of data to understand the relationship between diversity and stability of the data is selected. After review and meta-heuristic algorithms to implement the algorithm found that the cluster algorithm has better performance compared with other algorithms for feature selection sustained.

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Evaluation of Meta-Heuristic Algorithms for Stable Feature Selection

By Maysam Toghraee Hamid parvin Farhad rad

DOI: https://doi.org/10.5815/ijitcs.2016.07.04, Pub. Date: 8 Jul. 2016

Now a days, developing the science and technology and technology tools, the ability of reviewing and saving the important data has been provided. It is needed to have knowledge for searching the data to reach the necessary useful results. Data mining is searching for big data sources automatically to find patterns and dependencies which are not done by simple statistical analysis. The scope is to study the predictive role and usage domain of data mining in medical science and suggesting a frame for creating, assessing and exploiting the data mining patterns in this field. As it has been found out from previous researches that assessing methods can not be used to specify the data discrepancies, our suggestion is a new approach for assessing the data similarities to find out the relations between the variation in data and stability in selection. Therefore we have chosen meta heuristic methods to be able to choose the best and the stable algorithms among a set of algorithms.

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Other Articles