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Palmprint is a reliable and unique biometric trait with high acceptability. In this paper, we propose a new Local Composition Derivative Pattern (LCDP) for palmprint recognition. LCDP extracts first order derivative information of images along radial and directional directions which can capture more detailed information than the non-directional local binary pattern (LBP). Different from LBP encoding...
Medical databases contain massive volume of clinical data which could provide valuable information regarding diagnosis, prognosis and treatment plan when mining algorithms are used in appropriate manner. The irrelevant, redundant and incomplete data in medical databases makes the extraction of useful pattern a difficult process. Feature selection, a robust data preprocessing method selects attributes...
As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of medical domain application is detection system for heart disease based on computer-aided diagnosis methods, were the data are obtained from some other sources and are evaluated based on computer based applications. At the earlier time, the use...
This paper presents a method for extracting automatically classification rules via multi-objective genetic algorithms. The paper also proposes a novel objective measure to quantify the similarity of the rules. The other objectives of the rules are average support value and accuracy. We experimentally evaluate our approach on socio-demographics and biochemistry datasets of schizophrenia patients and...
There have been various bug tracking systems for software development. However, it's difficult to obtain useful information from those systems since they are composed of web pages. In this paper, we propose two ways of extracting raw data by parsing the bug report pages and refining them. Our future work is to categorize bugs and interesting patterns automatically by analyzing the extracted data from...
This letter presents a new model for the prediction of the 1-min integrated complementary cumulative distribution function (CCDF) of the rain rate, , valid for tropical and equatorial regions (specifically, latitudes ranging from 35 to 35). The proposed model inherits its analytical formulation from the method currently recommended by the International Telecommunication...
Top-K dominating query selects k data objects and influences the highest number of objects in a dataset. This is a decision supportable query since it provides data analysts a best way for finding significant objects. This search is not only for the earlier examination of large upper bounds that leads to earlier identification of results, but also eliminates partial dominance relationship between...
Rough Set Theory was used for data mining based on the characteristic database and can form a knowledge database for hailstone recognition to establish a single model for hailstone forecast. Thus the comprehensive hailstone forecasting model was formed. Firstly the rules discovered from Apriori algorithm were used to eliminate the interference, Secondly the integrated knowledge database was formed...
The representation of input data set is important for learning task. In data summarization, the representation of the multi-instances stored in non-target tables that have many-to-one relationship with record stored in target table influences the descriptive accuracy of the summarized data. If the summarized data is fed into a classifier as one of the input features, the predictive accuracy of the...
P2P lending is a new form of lending where in the lenders and borrowers can meet at a common platform like Prosper and ZOPA and strike a best deal. While the borrower looks for a lender who offers the fund at a cheaper interest rate, the lender looks for a borrower whose probability of default is nil or minimal. The peer to peer lending sites can help the lenders judge the borrower by allowing the...
With the development of computer network and widely used of Internet, online information increases in broadband level exponentially, the difficulty and complexity of information retrieval also increase gradually, so the Crawler is developing rapidly. Crawler is a program that can auto collect information from internet. In this paper, we design and implement a multi-thread Crawler for specific resources...
Investigating potential dependencies in data and their effect on future business developments can help experts to prevent misestimations of risks and chances. This makes correlation a highly important factor in risk analysis tasks. Previous research on correlation in uncertain data management addressed foremost the handling of dependencies between discrete rather than continuous distributions. Also,...
In this paper, a palmprint identification and verification approach based on Pyramidal Histograms of Oriented Gradients (PHOG) and fast tree based matching is presented. In the feature extraction stage, proposed local histograms of oriented gradient are extracted in each level or scale of the Gaussian pyramid of the palmprint. This matter helps to extract high contrast and reliable lines. In the identification...
Currently there are a number of ontological tools available in handling heterogeneous data presented in the computing domain. However, there are no surveys available in evaluating the performance of these tools. In this paper, we provide an in-depth discussion on the state-of-the-art ontological tools and their application to the real world domain. First, we describe the operation of these tools,...
Semi-supervised classification from pairwise constraints is a challenge in pattern recognition, since the constraints just represent the relationships between data pairs rather than the definite labels. In the last few years, several methods have been proposed, however, they still utilize either the discriminability within the constraints or the abundant unlabeled data insufficiently. In this paper,...
We investigate how to use the scripts with automatically generated fast-performing analytic SQL statements to speed up the KDD-related tasks of attribute selection and decision tree induction. We base our framework on the entity-attribute-value data model in order to seamlessly scale the required queries with respect to the amounts of attributes involved in the given task's specification. We note...
Large databases of digital information are ubiquitous. Data from the neighborhood store's checkout register, your bank's credit card authorization device, records in your doctor's office, patterns in your telephone calls and many more applications generate streams of digital records archived in huge databases, sometimes in so-called data warehouses A new generation of computational techniques and...
Traditional PCA-based face recognition algorithms usually have low performance in the complicated illumination database. There are two reasons. One is that the number of classes is large compared with other classification problems. The other is that the data in the PCA domain distributes in a narrow space and overlaps frequently. This paper presents a novel supervised learning framework for PCA-based...
Data Mining is concerned with extraction of interesting patterns or knowledge from huge amounts of Data. Generally data mining tasks are either predictive or descriptive. Classification falls under predictive induction while clustering and association rule mining fall under descriptive induction. Subgroup discovery is a task at the intersection of supervised learning and descriptive induction. In...
In this paper we present a student information sheet reading system. Relevant algorithm is proposed to locate and label handwritten answer field. As information sheets can be filled in Arabic and/or in French, automating the script language differentiation is a pre-recognition required in the proposed system. We have developed a robust and fast field classification and script language identification...
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