MohammadReza Keyvanpour

Mohammadreza Keyvanpour, Fereshteh Azizani

Understanding the structure and dynamics of biological networks is one of the important challenges in system biology. In addition, increasing amount of experimental data in biological networks necessitate the use of efficient methods to analyze these huge amounts of data. Such methods require to recognize common patterns to analyze data. As biological networks can be modeled by graphs, the problem of common patterns recognition is equivalent with frequent sub graph mining in a set of graphs. In this paper, at first the challenges of frequent subgrpahs mining in biological networks are introduced and the existing approaches are classified for each challenge. then the algorithms are analyzed on the basis of the type of the approach they apply for each of the challenges.

Mohammadreza Keyvanpour, Reza Tavoli

Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the feature using coefficient of multiple correlations. Coefficient of multiple correlations can be used to describe the synthesized effects and correlation of each feature. The aim of this paper is to show that feature weighting increases the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 93.23% and average recall become 98.66% respectively

Siavash Emtiyaz, MohammadReza Keyvanpour

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.

Ashraf Sadat Jabari, Mohammadreza Keyvanpour

Scene mining is a subset of image mining in which scenes are classified to a distinct set of classes based on analysis of their content. In other word in scene mining, a label is given to visual content of scene, for example, mountain, beach. Scene mining is used in applications such as medicine, movie, information retrieval, computer vision, recognition of traffic scene. Reviewing of represented methods shows there are various methods in scene mining. Scene mining applications extension and existence of various scenes, make comparison of methods hard. Scene mining can be followed by identifying scene mining components and representing a framework to analyzing and evaluating methods. In this paper, at first, components of scene mining are introduced, then a framework based on extracted features of scene is represented to classify scene mining methods. Finally, these methods are analyzed and evaluated via a proposal framework.

Fatemeh Alamdar, MohammadReza Keyvanpour

One of the important requirements in image retrieval, indexing, classification, clustering and etc. is extracting efficient features from images. The color feature is one of the most widely used visual features. Use of color histogram is the most common way for representing color feature. One of disadvantage of the color histogram is that it does not take the color spatial distribution into consideration. In this paper dynamic color distribution entropy of neighborhoods method based on color distribution entropy is presented, which effectively describes the spatial information of colors. The image retrieval results in compare to improved color distribution entropy show the acceptable efficiency of this approach.