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The emerging new data types bring tremendous challenges to data mining. There is an enormous amount of high-dimensional class-imbalanced data in different fields. In this case, traditional classification methods are not appropriate because they are prone to ensure the accuracy of the majority class. Meanwhile, the curse of dimensionality makes situations more complicated. Finding a complicated classifier...
Software effort estimation is crucial task in software development life cycle. There are many model exists for estimating the software project effort like regression model, expert judgment, algorithmic and non-algorithmic. But, still software industries are lacking to have more accurate effort estimation method due to which they will lead to failure. In this work, an enhanced Use Case Point model...
Search engines are usually used for exploring the net and finding required information. When search results are shown usually 10 links are included in the first page. It must be notices how many percent of achieved results are related to our request. Unfortunately some of advertisement websites utilize phony techniques to attract users so that they could obtain their personal goals (such as increase...
Number of defects remaining in a system provides an insight into the quality of the system. Defect detection systems predict defects by using software metrics and data mining techniques. Clustering analysis is adopted to build the software defect prediction models. Cluster ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. The clustering...
Analogy-based estimation has recently emerged as a promising technique and a viable alternative to other conventional estimation methods. One of the most important research areas for analogy-based cost estimation is how to predict the effort of software projects when they are described by mixed numerical and categorical data. To address this issue, we have proposed, in an earlier work, a new approach...
The paper presents a measure to improve the Tarantula spectral fault localization algorithm and solves the problems that the weight of the statements with a high-frequency coverage is significantly lower than the statements with a low-frequency coverage when Tarantula algorithm calculate the statement suspicious degrees. The improved algorithm has better discrimination when calculating the statement...
In this paper, we consider second order elliptic ODE eigen problems on general grids. We construct an efficient algorithm for computing the eigen value by using weighted mean combination of the linear finite element method and corresponding 2nd-order finite difference method. We first take the arithmetic mean of the two methods. Then we compute the quasi-optimal combined parameters for different eigen...
Development of software change prediction models, based on the change histories of a software, are valuable for early identification of change prone classes. Classification of these change prone classes is vital to yield competent use of limited resources in an organization. This paper validates Artificial Immune System (AIS) algorithms for development of change prediction models using six open source...
Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, we proposed a technique using a text filtering technique. We assume that bugs relate to words and context that are contained in a software module. Our technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing...
Software build system (e.g., Make) plays an important role in compiling human-readable source code into an executable program. One feature of build system such as make-based system is that it would use a build configuration file (e.g., Make file) to record the dependencies among different target and source code files. However, sometimes important dependencies would be missed in a build configuration...
In software quality research, software defect prediction is a key topic. The characteristics of software attributes influences the performance and effectiveness of the defect prediction model. However this issue is not well explored to the best of our knowledge. Thus we focus on the problem of attribute selection in the context of software defect prediction here and hence in this research, we propose...
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data...
Radar scattering characteristics is an important researching content of military target. It is always very difficult to deal with simulation of electromagnetism field , especially to analyze the monostatic RCS characteristics. Aiming at a kind of complex & electrically large size target , this paper reasonably estimated its multi-point monostatic RCS using smaller resources by adopting the electromagnetic...
With improving of the complexity level of the large software system, the number of code line shows geometrical multiples growth, so software reliability become more and more difficult to be guaranteed. The traditional test security measure must spend a lot of manpower and time to complete. And the system itself needs more reliability to run. In some key fields, such as aviation, spaceflight, its reliability...
This system is needed to make the designing and research of algorithm more efficient. Two aspects are adopted in the designing of the system, the first is software designing and the second is the construction of image database with complex background. The software adopts several modules in the designing which is convenient for updating. Batch processing function is put into this software to simplify...
With the introduction of intensive new treatments such as hypo-fractionation and proton beam therapy, localization of the tumor target volume and tracking of points across the skin entrance surface have become critically important. Optical metrology has been used to monitor the patient's bulk position and motion throughout treatment. However systems have not been capable of high temporal and spatial...
The emergence of multi-electrode array enables the study of real-time neurophysiological activities across multiple regions of the brain. However, the real-time extracellular action potentials recorded on any electrode represent the simultaneous electrical activity of an unknown number of neurons which present a critical challenge to the accuracy of interpretation and identification of the neural...
Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multimodal functions. To improve PSO performance on global optimization problems, this paper...
Learning algorithm evaluation is usually focused on classification performance. However, the characteristics and requirements of real-world applications vary greatly. Thus, for a particular application, some evaluation criteria are more important than others. In fact, multiple criteria need to be considered to capture application-specific trade-offs. Many multi-criteria methods can be used for the...
Accurate and fast nodes locating is required in large-scale emergency logistics with WSN (wireless sensor network) deployed. Under this background, the sensor nodes often move rapidly and frequently, and the mobility of nodes leads to poor reachability to the reference nodes. In this paper, we propose a fast and accurate positioning scheme based on WSN in emergency logistics. Two different wireless...
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