Hamid parvin

Work place: Faculty of engineering, department of computer science, Islamic Azad University, Kohgilouye-vaBoyerahmad, Isfahan and 8661854381, Iran

E-mail: parvinhamid@gmail.com

Website:

Research Interests: Computer Science & Information Technology

Biography

Dr. Hamid Parvin received PHD Degrees in Artificial intelligen-ce computer engineering from the department of computer engineering, Iran science of technology university, Tehran, Iran, in 2013. He received his M.sc degrees in Artificial intelligence computer engineering from Iran science of technology university, Tehran, Iran, in 2007, his research interests include Neural Networks and data mining.

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.

[...] Read more.
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.

[...] Read more.
Other Articles