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Background: Many relevancy filters have been proposed to select training data for building cross-project defect prediction (CPDP) models. However, up to now, there is no consensus about which relevancy filter is better for CPDP. Goal: In this paper, we conduct a thorough experiment to compare nine relevancy filters proposed in the recent literature. Method: Based on 33 publicly available data sets,...
Big data has been revolutionizing individualized medicine, improving the results of diagnostic imaging, genetic testing, and by providing frameworks for electronic health record sharing and analysis. In this paper we present a nextstep in personalized data-driven health by demonstrating the capability of predictive individualized models to model peak postprandial plasma glucose concentrations. Past...
Predicting the number of defects in software modules can be more helpful in the case of limited testing resources. The highly imbalanced distribution of the target variable values (i.e., the number of defects) degrades the performance of models for predicting the number of defects. As the first effort of an in-depth study, this paper explores the potential of using resampling techniques and ensemble...
Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security using traceable patterns and software metrics during development. The concept of traceable patterns is similar to design patterns but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared...
Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations,...
The sheer usage of social media presents an opportunity for an automated analysis of a social media user based on his/her information, activities, or status updates. This opportunity is due to the abundant amount of information shared by the user. This fact is especially true for countries with high number of active social media users such as Indonesia. Extraction of information from social media...
Code review is a key tool for quality assurance in software development. It is intended to find coding mistakes overlooked during development phase and lower risk of bugs in final product. In large and complex projects accurate code review is a challenging task. As code review depends on individual reviewer predisposition there is certain margin of source code changes that is not checked as it should...
Accurate forecasting of solar time series is challenging due to irregularities and uncertainties of such datasets. This paper develops an advanced hybrid forecasting method for solar radiation. The proposed framework combines a novel data mining technique for clustering the time-series data with an innovative cluster selection method and a multilayer recurrent neural network (RNN) to enhance the forecast...
Least angle regression (LARS) by Efron et al. (2004) is a novel method for constructing the piece-wise linear path of Lasso solutions. For several years, it remained also as the de facto method for computing the Lasso solution before more sophisticated optimization algorithms preceded it. LARS method has recently again increased its popularity due to its ability to find the values of the penalty parameters,...
Software fault prediction models are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. We apply three different ensemble methods to develop a model for predicting fault proneness. We propose a framework to validate the source code metrics and select the right set of metrics with the objective to improve the performance of the fault prediction...
Software reliability is a major attribute for software product and can be considered as one of the major performance parameters. Software Reliability unlike hardware reliability cannot be considered merely as function of time, although researchers have come up with models relating the two. In literature, numerous models on software reliability have been proposed but they seem to have limitations in...
Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work,...
Among software reliability growth models (SRGMs), the NHPP models perform well in practice. However, the traditional NHPP models still have many problems which are mainly due to the following unreasonable assumptions: 1) fault detection rate subjects to constant or regular change, 2) the testing environment and the final field environment are the same, 3) the testing environment is a completely closed...
Covering arrays have been extensively used for software testing. Therefore, many covering array constructors have been developed. However, each constructor comes with its own pros and cons. That is, the best constructor to use typically depends on the specific application scenario at hand. To improve both the efficiency and effectiveness of covering arrays, we, in this work, present a classification-based...
The ability to closely track the traffic load of base stations is very important for resource management and energy saving in green communications. Thus how to predict the future traffic accurately is critical and some recent studies show that correlation of traffic load exists among neighboring base stations. Inspired by these conclusions, this paper proposes a novel base station traffic prediction...
Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
Electric vehicle (EV) driving range directly reflects EVs' performance, safety, reliability and economy. EV has gained wide attention in recent years. However, most of researches are carried out under ideal conditions and the existing methods have numerous drawbacks. This paper presents a novel prediction method based on a least squares support vector machine (LSSVM) model with parameters γ and σ2...
This study aims to present time series-based forecasting for Malaysian crude palm oil prices using neural network algorithms. Daily prices of soy bean oil and currency exchange rates are tested as input features, in addition to crude palm oil prices. Efforts are focused on finding the optimal network structures for the modelling of crude palm oil price forecasting. Neural network structures with an...
The advancement of information technology and research in finance have recently led to flash decision making and actions by computer algorithms in order to respond to fast events occurring in the stock markets. This new area of technology involves the implementation of high-speed trading strategies which have generated significant amount of activity and information for financial research. In this...
Researchers often focus on the development process and the final product (source code) to investigate and predict software defects. Unfortunately, these models may not be applicable to software projects in which there is no access to the data sources regarding development process. For example, in cases when a company conducts tests on behalf of its business contractors, it is only possible to evaluate...
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