University of Patras

About: University of Patras is a education organization based out in Pátrai, Greece. It is known for research contribution in the topics: Population & Catalysis. The organization has 13372 authors who have published 31263 publications receiving 677159 citations. The organization is also known as: Panepistímio Patrón.

Preventing Student Dropout in Distance Learning Using Machine Learning Techniques

Abstract: Student dropout occurs quite often in universities providing distance education. The scope of this research is to study whether the usage of machine learning techniques can be useful in dealing with this problem. Subsequently, an attempt was made to identifying the most appropriate learning algorithm for the prediction of students’ dropout. A number of experiments have taken place with data provided by the ‘informatics’ course of the Hellenic Open University and a quite interesting conclusion is that the Naive Bayes algorithm can be successfully used. A prototype web based support tool, which can automatically recognize students with high probability of dropout, has been constructed by implementing this algorithm.

Low Frequency Scattering

Abstract: This book for the first time unifies the theories of acoustic, electromagnetic, and elastic waves and discusses the many physical and geometric aspects of their interactions with obstacles It offers a complete introduction to scattering theory, and goes on to discuss low frequency scattering in particular A significant number of results are previously unpublished, and the book fills many gaps in the existing literature Included is an extended bibliography covering the whole existing literature on low frequency scattering, making this an invaluable reference for researchers

On the multivariate Nakagami-m distribution with exponential correlation

Abstract: Capitalizing on the proof of a theorem presented by L.E. Blumenson and K.S. Miller (see Ann. Math. Statist., vol.34, p.903-10, 1963), we propose a useful closed formula for the exponentially correlated n-variate Nakagami-m probability density function. Moreover, an infinite series approach for the corresponding cumulative distribution function is presented. Bounds on the error resulting from the truncation of the infinite series are also derived. Finally, in order to check the accuracy of the proposed formulation, numerical results are presented.