Tamer Medhat

Work place: Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, Egypt

E-mail: tmedhatm@eng.kfs.edu.eg

Website: https://scholar.google.com.eg/citations?user=l7Dv-q0AAAAJ&hl=en

Research Interests: Multicriteria Decision Making, Information Systems, Computer Science & Information Technology, Immersive learning with Augmented Reality, Rough Set


Tamer Medhat

Professor at Faculty of Engineering, Kafrelsheikh University, Egypt


Research Interests: Information Systems, Augmented Reality, Decision Making, Computer Science, and Rough Set Theory Applications.

Author Articles
Handling Numerical Missing Values Via Rough Sets

By Elsayed Sallam T. Medhat A.Ghanem M. E. Ali

DOI: https://doi.org/10.5815/ijmsc.2017.02.03, Pub. Date: 8 Apr. 2017

Many existing industrial and research data sets contain missing values. Data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors, and incorrect measurements. It is usual to find missing data in most of the information sources used. Missing values usually appear as "NULL" values in the database or as empty cells in the spreadsheet table. Multiple ways have been used to deal with the problem of missing data. The proposed model presents rough set theory as a technique to deal with missing data. This model can handle the missing values for condition and decision attributes, the web application was developed to predict these values.

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Prediction of Missing Values for Decision Attribute

By T. Medhat

DOI: https://doi.org/10.5815/ijitcs.2012.11.08, Pub. Date: 8 Oct. 2012

The process of determining missing values in information system is an important issue for decision making especially when the missing values are in the decision attribute. The main goal for this paper is to introduce algorithm for finding missing values of decision attribute. Our approach is depending on distance function between existing values. These values can be calculated by distance function between the conditions attributes values for the complete information system and incomplete information system. This method can deal with the repeated small distance by eliminating a condition attribute which has the smallest effect on the complete information system. This algorithm will be discussed in detail with an example of a case study.

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Other Articles