Work place: Faculty Member of Department of Computer Engineering and Information Technology, Payame Noor University, I.R. of Iran
E-mail: iraji.ms@Gmail.com
Website:
Research Interests: Data Mining, Image Processing, Artificial Intelligence, Software Engineering
Biography
Mohammad Saber Iraji received B.Sc in computer software engineering from shomal university, Iran, amol;M.Sc1 in industrial engineering (system management and productivity) from khatam university Iran ,Tehran and M.Sc2 in computer science from Islamic azad university , sari branch . Currently, he is engaged in research and teaching on computer graphics, image processing , fuzzy and artificial intelligent , data mining, software engineering.
By Mohammad Saber Iraji Majid Aboutalebi Naghi. R. Seyedaghaee Azam Tosinia
DOI: https://doi.org/10.5815/ijmecs.2012.07.06, Pub. Date: 8 Jul. 2012
Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and can be used as an effective alternative system for classifying students.
[...] Read more.By Mohammad Saber Iraji Homayun Motameni
DOI: https://doi.org/10.5815/ijisa.2012.06.02, Pub. Date: 8 Jun. 2012
Use case size point (USP) method has been proposed to estimate object oriented software development effort in early phase of software project and used in a lot of software organizations. Intuitively, USP is measured by counting the number of actors, preconditions, post conditions, scenarios included in use case models. In this paper have presented a Adaptive fuzzy Neural Network model to estimate the effort of object oriented software using Use Case size Point approach. In our proposed system adaptive neural network fuzzy use case size point has less error and system worked more accurate and appropriative than prior methods.
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