Data analysis, machine learning and adaptive algorithms for research quality evaluation methods
Evangelia A E C Lipitakis
University of Kent, UK
: J Comput Eng Inf Technol
Abstract
Research quality evaluation methodologies, based on advanced bibliometric indicators, are presented in combination with a mathematical model for the e-business performance evaluation and certain machine learning algorithmic methodologies in the case of very large data sets. The mathematical model of e-business performance evaluation includes a number of related computational modules, critically affecting the e-business performance. In this research study, we consider that the research quality and e-business performance evaluation methodologies can be efficiently applied and intergraded to a new unified solution approach by using appropriate machine learning adaptive algorithms. Adaptive algorithmic modelling methodologies can be efficiently applied for solving important scientific and technological problems, such as research quality evaluation, e- business performance assessment, machine learning and artificial (computational) intelligence applications, complex e-business and e-services problems with big data.
Biography
