Hossein Moeinzadeh

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Recently, there has been great interest in Bioinformatics among researches from various disciplines such as computer science, mathematics, statistics and artificial intelligence. Bioinformatics mainly deals with solving biological problems at molecular levels. One of the classic problems of bioinformatics which has gain a lot attention lately is(More)
Selection of a classifier is only one aspect of the problem of data classification. Equally important (if not, more so) is the pre-processing strategy to be employed. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix to discriminate between classes by(More)
An appropriate pre-processing algorithm in classification is not only of great importance with respect to classifier choice, but also would be more crucial. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix causes classes to be more discriminable by(More)
An appropriate pre-processing algorithm in classification is important and crucial with respect to classifier type. In this paper, two pre-processing methods are suggested to be applied before classification in order to increase classification accuracy. The aim of this approach is finding a transformation matrix to discriminate between classes by(More)
Reduced ordered binary decision diagram (ROBDD) is a memory-efficient data structure which is used in many applications such as synthesis, digital system, verification, testing and VLSI-CAD. The size of an ROBDD for a function can be increased exponentially by the number of independent variables of the function that is called “memory explosion(More)
Gaetano D. Gargiulo 1,2,*, Paolo Bifulco 2, Mario Cesarelli 2, Alistair L. McEwan 3, Hossein Moeinzadeh 1, Aiden O’Loughlin 4, Ibrahim M. Shugman 5, Jonathan C. Tapson 1 and Aravinda Thiagalingam 6 1 The MARCS Institute, Western Sydney University, Milperra NSW 2214, Australia; h.moeinzadeh@westernsydney.edu.au (H.M.); j.tapson@westernsydney.edu.au (J.C.T.)(More)
In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peerto-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is(More)
Linear Discriminant Analysis (LDA) is a feature selection method in speech recognition. LDA finds transformations that maximizes the between-class scatter and minimizes within-class scatter. This transformation can be obtained in a class-dependent or class independent manner. In this paper, we propose a method to improve LDA and also we use it instead of(More)