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Procedia Computer Science
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Abstract The impressive growth of research productivity of Russian organizations in the recent years is not accompanied by the corresponding increase in funding and occurred on the background of drop of number of R & D personnel according to state statistics. There is no definitive explanation of the nature of this phenomenon at the moment. This study is based on the detailed analysis of bibliometric data of Scopus database. Attribution of Russian authors’ affiliations to the main sectors of Rus...
Abstract In this work we combine classical distance metrics (Hamming and Euclidean), interval valued intuitionistic aggregation operators and different starting points to achieve consensus in a group decision making situation. Optimality is achieved by minimizing weighed incoherencies between each opinion, described by interval valued intuitionistic fuzzy sets (IVIFS), and the consensus in an iterative scheme. An example illustrating the different combinations is proposed.
#1Sucianna Ghadati Rabiha (Binus University)
Last.SasmokoH-Index: 2
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Abstract Research related to the Indonesian Teacher Engagement Index (ITEI) is still being developed to get better predictive accuracy. In this study we conducted an analysis using the Neural Network approach to determine the performance of each dimension obtained from the ITEI database. Root Mean Square Errors values ​​generated when using variables in dimensions related to Nationality Character are quite low, which is 0.151, with a correlation of 0.906 and execution time of 13s. In addition, t...
Abstract Data breaches represent a permanent threat to all types of organizations. Although the types of breaches are different, the impacts are always the same. This paper focuses on analyzing over 9000 data breaches made public since 2005 that led to the loss of 11,5 billion individual records which have a significant financial and technical impact. Also, since the most devastating breaches are hacking breaches, we shed the light on type to unveil the most targeted organizations, and examine h...
Abstract Over the last two decades, there has been an important increase in studies dealing with the software development effort estimation (SDEE) using machine learning (ML) techniques that aimed to improve the accuracy of the estimates and to understand the process used to generate these estimates. Among these ML techniques, decision tree-based models have received a considerable scholarly attention thanks to their generalization ability and understandability. However, very few studies have in...
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