7 research outputs found

    Advances in Data Mining Knowledge Discovery and Applications

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    Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

    Investigating the effect of data representation on neural network and regression

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    In this research the impact of different data representation on the performance of neural network and regression was investigated on different datasets that has binary or Boolean class target.In addition, the performance of particular predictive data mining model could be affected with the change of data representation.The seven data representations that have been used in this research are As_Is, Min Max normalization, standard deviation normalization, sigmoidal normalization, thermometer representation, flag representation and simple binary representation.Moreover, all data representations have been applied on two datasets which are Wisconsin breast cancer and German credit dataset. As a result, the neural network performance is better than logistic regression on both datasets if we exclude the thermometer and flag representations.For datasets having a binary or Boolean target class, flag or thermometer binary representation is recommended to be used if logistic regression analysis is performed. Meanwhile, As_is representation, min max normalization,standard deviation normalization or sigmoidal normalization is recommended for neural network analysis on datasets having binary or Boolean target class

    Modeling the Measurements of the Determinants of ICT Fluency and Evolution of Digital Divide Among Students in Developing Countries—East Africa Case Study

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    [[abstract]]During the last decade, information and communication technology has brought remarkable changes to the education style of developed countries, especially in the context of online learning materials accessibility. However, in developing nations such as the East African (EA) countries, university students may lack the necessary ICT training to take advantage of e-learning resources productively. Therefore, the comprehension of the key factors behind ICT fluency is a significant concern for this region and all the developing countries in general. This paper applies the Concentration Index and proposes a Logistic Regression based model to discover the key determinants of ICT fluency and to explore the evolution of the digital divide among EA students within the four years of undergraduate studies. To identify the principal determinants, data composing of 1237 participants is collected from three different universities in EA within a one year period. The experimental results indicate that the digital divide among students decreases quite fast from the first year to the fourth year. Regression computational findings show that the key determinants of ICT fluency are the student urban/rural origin, computer ownership, computer experience, class year, and major. The findings provide heuristic implications for developers, practitioners, and policy makers for an improved ICT environment implementation in EA and the developing nations in general.[[notice]]補正完
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