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The main goal of the work presented in this paper was to develop a set of algorithms which allows to predict what will be the probability ratio of acquisition of the items form the given database. To fulfill this goal, the appropriate statistical methods were developed, mainly using R programming language. In order to apply the specific statistical methods, the appropriate database preprocessing was...
This paper focuses on the attempt to formulate the prescription prediction logic based on the medical data analysis towards the future computer-assisted-diagnosis for Kampo medicine. We constructed and evaluated prediction models for some frequently-used prescriptions using six kinds of machine learning algorithms including artificial neural network, multinomial logit, random forest, support vector...
A lattice time series model may be used to estimate the inter-area electromechanical modes of a power system from measured synchrophasor data. The accuracy of these estimates is sensitive to the order of the model. This paper describes a methodology for real-time, order-recursive whiteness testing of the prediction errors. This hypothesis testing methodology may be used in conjunction with the lattice...
The prediction of software reliability can determine the current reliability of a product, using statistical techniques based on the failures data, obtained during testing or system usability. Software reliability growth models attempt to predict the number of defect using a correlation between exponential function and defect data. The purpose of this paper is to study the evolution of a real-life...
The price of crude oil is tied to major economic activities in all nations of the world, as a change in the price of crude oil invariably affects the cost of other goods and services. This has made the prediction of crude oil price a top priority for researchers and scientists alike. In this paper we present an intelligent system that predicts the price of crude oil. This system is based on Support...
Rainfall forecasting is vital for making important decisions and performing strategic planning in agriculture-dependent countries. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Artificial Neural Networks (ANNs) have recently become...
Compared to traditional service, the characteristics of the customer behavior in electronic service are personalized demand, convenient consumed circumstance and perceptual consumer behavior. Therefore, customer behavior is an important factor to facilitate online electronic service. The purpose of this study is to explore the key success factors affecting customer purchase intention of electronic...
The conventional approach to Risk Assessment in Software Testing is based on analytic models and statistical analysis. The analytic models are static, so they don't account for the inherent variability and uncertainty of the testing process, which is an apparent deficiency. This paper presents an application of Six Sigma and Simulation in Software Testing. DMAIC and simulation are applied to a testing...
Process variations are a major hurdle for continued technology scaling. Both systematic and random variations will affect the critical delay of fabricated chips, causing a wide frequency and power distribution. Tuning techniques adapt the microarchitecture to mitigate the impact of variations at post-fabrication testing time. This paper proposes a new post-fabrication testing framework that accounts...
Statistical yield modeling is used to calculate the probability of a die containing a latent defect based on its spatial relationship with other dies in its surrounding neighborhood. Previous research implements a blanket application of predictive yield mining on devices and assumes that a spatial relationship exists between killer defects screened at probe test, and latent defects screened at packaged...
Incorrect changes made to the stable parts of a software system can cause failures - software regressions. Early detection of faulty code changes can be beneficial for the quality of a software system when these errors can be fixed before the system is released. In this paper, a statistical model for predicting software regressions is proposed. The model predicts risk of regression for a code change...
Through statistic analysis on the donor site sequences in the dataset of HS3D, the rules that the bases appear in the adjacent sites around the splice sites are used for constructing motifs, which are then utilized as the attributes of the DNA sequences. And by setting the value of each attribute the literal sequences are transformed into quasi numeric vectors, based on which a decision tree (C4.5...
We present a generic method for analyzing the effect of process variability in nanoscale circuits. The proposed framework uses kernel and a generic tail probability estimator to eliminate the need for a-priori density choice for the nature of circuit variation. This allows capturing the true nature of the circuit variation from a few random samples of its observed responses. The data-driven, non-parametric,...
Studies by previous researchers using production test data reported that not all the production test patterns applied detected defective chips. Researchers found that 70% to 90% of their production test patterns seemed useless because these patterns detected no defective chips and they could therefore be removed without impacting test quality. Previous researchers qualitatively explained this finding...
Mutants are automatically-generated, possibly faulty variants of programs. The mutation adequacy ratio of a test suite is the ratio of non-equivalent mutants it is able to identify to the total number of non-equivalent mutants. This ratio can be used as a measure of test effectiveness. However, it can be expensive to calculate, due to the large number of different mutation operators that have been...
This study mainly remarks the efficiency of black-box modeling capacity of neural networks in the case of forecasting soccer match results, and opens up several debates on the nature of prediction and selection of input parameters. The selection of input parameters is a serious problem in soccer match prediction systems based on neural networks or statistical methods. Several input vector suggestions...
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