Supervised Classification Using Copula and Mixture Copula

Statistical classification is a field of study that has developed significantly after 1960\u27s. This research has a vast area of applications. For example, pattern recognition has been proposed for automatic character recognition, medical diagnostic and most recently in data mining. Classical discrimination rule assumes normality. However in many situations, this assumption is often questionable. In fact for some data, the pattern vector is a mixture of discrete and continuous random variables. In this dissertation, we use copula densities to model class conditional distributions. Such types of densities are useful when the marginal densities of a pattern vector are not normally distributed. This type of models are also useful for a mixed discrete and continuous feature types. Finite mixture density models are very flexible in building classifier and clustering, and for uncovering hidden structures in the data. We use finite mixture Gaussian copula and copula of the Archimedean family based mixture densities to build classifier. The complexities of the estimation are presented. Under such mixture models, maximum likelihood estimation methods are not suitable and regular expectation maximization algorithm may not converge, and if it does, not efficiently. We propose a new estimation method to evaluate such densities and build the classifier based on finite mixture of copula densities. We develop simulations scenarios to compare the performance of the copula based classifier with classical normal distribution based models, the logistic regression based model and the Independent model. We also apply the techniques to real data, and present the misclassification errors

PRIMARY SCHOOL STUDENTS' ABSTRACTION LEVELS OF WHOLE-HALF-QUARTER CONCEPTS ACCORDING TO RBC THEORY

Whole-half-quarter are important mathematical concepts that form the basis of fractions and should be well understood for advancing mathematical topics. The aim of this study is to determine the primary school students' abstraction levels of whole-half-quarter concepts according to RBC theory. The participants of the study are six students (8 age group) from the second grade of primary school. The data of the research which is a case study were collected through worksheets and semi-structured interviews. The data obtained from interviews were analyzed by qualitative data analysis steps. The abstraction levels of students were evaluated according to RBC theory. As a result of the study, it was seen that many of the students could not abstract the whole, half and quarter concepts. It was determined that difficulties of students to abstract the whole-half-quarter concepts resulted from reasons such as not understanding the half and quarter concepts, not being able to divide the whole into two equal parts, not being able to divide one dimensional shapes into half and quarter, generalizing dividing into quarter as putting a "+", not being able to divide into four equal parts for quarter

'Useful' civic hacking for environmental sustainability:knowledge transfer and the International Space Apps Challenge

Civic hackathons have become a popular, experimental process through which to promote public access to open government data and enable innovative civic uses for the information. The International Space Apps Challenge, led by NASA, is a high-profile event, promoting the use of space-derived data with the aim of contributing solutions to 'grand challenges' such as environmental sustainability. Central to the civic hackathons are the concepts of 'stewardship,' and 'usefulness'. The study explores the promises and realities of civic hacking through analysis of the aims of the organisers, perspectives of participants and the event's outcomes, concluding that hackathon peer processes promote networks for knowledge transfer

Supervised Classification Using Finite Mixture Copula

Use of copula for statistical classification is recent and gaining popularity. For example, statistical classification using copula has been proposed for automatic character recognition, medical diagnostic and most recently in data mining. Classical discrimination rules assume normality. But in this data age time, this assumption is often questionable. In fact features of data could be a mixture of discrete and continues random variables. In this paper, mixture copula densities are used to model class conditional distributions. Such types of densities are useful when the marginal densities of the vector of features are not normally distributed and are of a mixed kind of variables. Authors have shown that such mixture models are very useful for uncovering hidden structures in the data, and used them for clustering in data mining. Under such mixture models, maximum likelihood estimation methods are not suitable and regular expectation maximization algorithm is inefficient and may not converge. A new estimation method is proposed to estimate such densities and build the classifier based on mixture finite Gaussian densities. Simulations are used to compare the performance of the copula based classifier with classical normal distribution based models, logistic regression based model and independent model cases. The method is also applied to a real data

'Useful' civic hacking for environmental sustainability:knowledge transfer and the International Space Apps Challenge

Civic hackathons have become a popular, experimental process through which to promote public access to open government data and enable innovative civic uses for the information. The International Space Apps Challenge, led by NASA, is a high-profile event, promoting the use of space-derived data with the aim of contributing solutions to 'grand challenges' such as environmental sustainability. Central to the civic hackathons are the concepts of 'stewardship,' and 'usefulness'. The study explores the promises and realities of civic hacking through analysis of the aims of the organisers, perspectives of participants and the event's outcomes, concluding that hackathon peer processes promote networks for knowledge transfer

A Probabilistic Approach to Identifying Run Scoring Advantage in the Order of Playing Cricket

In the game of cricket, the result of coin toss is assumed to be one of the determinants of match outcome. The decision to bat first after winning the toss is often taken to make the best use of superior pitch conditions and set a big target for the opponent. However, the opponent may fail to show their natural batting performance in the second innings due to a number of factors, including deteriorated pitch conditions and excessive pressure of chasing a high target score. The advantage of batting first has been highlighted in the literature and expert opinions, however, the effect of batting and bowling order on match outcome has not been investigated well enough to recommend a solution to any potential bias. This study proposes a probability theory-based model to study venue-specific scoring and chasing characteristics of teams under different match outcomes. A total of 1117 one-day international matches held in ten popular venues are analyzed to show substantially high scoring advantage and likelihood when the winning team bat in the first innings. Results suggest that the same 'bat-first' winning team is very unlikely to score or chase such a high score if they were to bat in the second innings. Therefore, the coin toss decision may favor one team over the other. A Bayesian model is proposed to revise the target score for each venue such that the winning and scoring likelihood is equal regardless of the toss decision. The data and source codes have been shared publicly for future research in creating competitive match outcomes by eliminating the advantage of batting order in run scoring

The Associating Abilities of Pre-Service Teachers Science Education Program Acquisitions with Engineering According to STEM Education

The aim of this study is to determine the associating abilities of elementary education pre-service teachers science education program acquisitions with engineering using STEM education. In the study which is a case study, firstly pre-service teachers were trained about the STEM education approach. Then “Elementary School Science Education Program Acquisitions-STEM Activities Form” was applied asking the subjects to prepare activities associating elementary education science lessons acquisitions with engineering. After the application of the form, semi-structured interviews were conducted to ask pre-service teachers’ opinions about STEM education, using STEM in elementary education science lessons and the activities they had written in the form. An analysis of the data showed that pre-service teachers could easily associate elementary school science program acquisitions and the field of engineering. A variety of activities were given that could be conducted in the elementary education science lessons. Interviewed teachers gave positive feedback to the approach and stated that it is an educational approach that must be applied to lessons. Keywords: elementary education, pre-service teachers, STEM educatio

The Mediator Effect of Logistics Performance Index on the Relation Between Corruption Perception Index and Foreign Trade Volume

Logistics performance of a country plays an important role within both economic and social developments. Therefore examining the relationship among the logistic performance, corruption and foreign trade volume of a country would contribute to the literature. Logistics Performance Index (LPI) has firstly been issued byWorld Bank in 2007 and continued in the years 2010, 2012, 2014. In this paper the mediator effect of Logistics Performance Index (LPI) on the relation between Corruption Perception Index (CPI) and Foreign Trade Volume (FTV) analyzed for the years 2007, 2010, 2012, 2014. The hierarchical regression analysis method was used in order to determine the mediator effect. As per the analysis results, the mediator effect of LPI on the relation between CPI and FTV is statistically significant. Consequently it could be suggested that the logistics ability of a country trigger the relation between corruption and foreign trade volume

A Bivariate Distribution with Conditional Gamma and its Multivariate Form

A bivariate distribution whose marginal are gamma and beta prime distribution is introduced. The distribution is derived and the generation of such bivariate sample is shown. Extension of the results are given in the multivariate case under a joint independent component analysis method. Simulated applications are given and they show consistency of our approach. Estimation procedures for the bivariate case are provided

Cybersecurity for Nuclear Power Plants Working with Simulator's Data and Machine Learning Algorithms to Find Abnormalities at Nuclear Power Plants

Cybersecurity has the utmost importance for nuclear power plants (NPPs). Demand for clean and constant energy has increased the need and use of NPPs. Countries want to have and maintain secure NPPs both physically (well-studied area) and digitally. We live in a digital world, and cyber-attacks have skyrocketed in recent years. This study explores the cyber risk for NPPs, digital attacks, potential future attacks, international aspects, and law and policy requirements of cyber protection for nuclear power plants. With the help of data analysis and machine learning algorithms, extra monitoring can be conducted on plants' data. Data monitoring applications require comprehensive data to build models and develop solutions. However, nuclear facilities do not share their data because of security concerns. Plant simulators are heavily used for training people and conducting experiments. In this thesis, we inspect plant simulators to assess their usability by people with a technical background such as cyber experts, information technology technicians, and software developers. People responsible for protecting digital systems can benefit from the help of data analytic tools and machine learning models to detect abnormalities. We study machine learning models on simulator data to examine their potential in identifying anomalies