scholarly journals A cognitive model for managing the national innovation system parameters based on international comparisons (the case of the EU countries)

2019 ◽  
Vol 17 (4) ◽  
pp. 153-162
Author(s):  
Igor Khanin ◽  
Gennadiy Shevchenko ◽  
Vladimir Bilozubenko ◽  
Maxim Korneyev
Keyword(s):  
Feature Vector ◽  
Cognitive Model ◽  
International Comparisons ◽  
Innovation System ◽  
Multidimensional Data ◽  
Data Set ◽  
Eu Countries ◽  
Global Innovation ◽  
Multidimensional Data Set ◽  
The Eu

To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.

An analysis of Japan’s connectivity to the global innovation system

2016 ◽  
Vol 24 (4) ◽  
pp. 399-423 ◽  
Author(s):  
Ahreum Lee ◽  
Ram Mudambi ◽  
Marcelo Cano-Kollmann
Keyword(s):  
Tacit Knowledge ◽  
Multinational Enterprises ◽  
Innovation System ◽  
Innovation Systems ◽  
Data Set ◽  
Content Type ◽  
Innovation Activities ◽  
Global Innovation ◽  
Knowledge Intensive ◽  
Short Product

Purpose In the modern knowledge-intensive economy, a nation’s competitiveness depends on the ability of its constituent firms to innovate. Extant research in national systems of innovation highlights institutions and public policies toward innovation as key determinants that affect firms’ innovation activities. This paper aims to widen the investigation by arguing that co-inventor connectivity allows firms to access the most tacit knowledge within global innovation systems. Therefore, it is one of the key factors that underpin a nation’s ability to develop and sustain its competitiveness. Design/methodology/approach Using a data set of 406,168 patents from US Patent and Trademark Office during the period of 1975-2004, this study analyzed the Japanese system of innovation through co-inventor networks. Findings Surprisingly, the authors found that compared to other advanced countries such as Germany and Denmark, the Japanese innovation system is quite closed. Originality/value The dimension of tacit knowledge is crucial in the current environment of rapid cycle time, short product lifespans and increasing emphasis on exploratory innovation. Hence the authors speculate that closedness to global innovation systems could be one of the reasons why many of Japan’s traditionally powerful multinational enterprises exhibit weak performance in recent years.

scholarly journals Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set

2019 ◽  
Vol 7 (2) ◽  
pp. 448 ◽  
Author(s):  
Saadaldeen Rashid Ahmed Ahmed ◽  
Israa Al Barazanchi ◽  
Zahraa A. Jaaz ◽  
Haider Rasheed Abdulshaheed
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithms ◽  
Gaussian Mixture ◽  
Multidimensional Data ◽  
Data Set ◽  
Multidimensional Data Set

scholarly journals Multidimensional comparative analysis of the competitive capacity of the European Union countries and geographical regions

2017 ◽  
Vol 8 (4) ◽  
pp. 487-504 ◽  
Author(s):  
Katarzyna Cheba ◽  
Katarzyna Szopik-Depczyńska
Keyword(s):  
European Union ◽  
Comparative Analysis ◽  
International Comparisons ◽  
Value Added ◽  
Basic Question ◽  
The European Union ◽  
Eu Countries ◽  
Geographical Regions ◽  
European Union Countries ◽  
The Eu

Research background: The basic question we ask is whether is it possible to talk in today’s globalizing world about the uniform of the competitiveness of the economies? Posing such questions is particularly important in the case of political and economic structures such as the European Union. The competitiveness of the economies is now one of the most frequently discussed topics. In this work, due to the context of the conducted research (international comparisons of the EU countries’ economies) the competitiveness of international economies will be considered in terms of international competitive capacity. In addition to the problems associated with defining this concept, there are also important dilemmas concerned with the measurement of the competitiveness. In the performed comparative analyses of European economies the research results presented within reports of „Global Competitiveness Index” will be used. Purpose of the article: The main purpose of the paper is to conduct a multidimensional comparative analysis of the competitive capacity of the European Union countries and geo-graphical regions of Europe. Methods: In the paper, to study the spatial differentiation of the EU countries and geograph-ical regions of Europe in the context of their competitive capacity, the taxonomic measure of development based on median vector Weber was used. Findings & Value added: As a result, the classification and the typological groups of EU countries and geographical regions of Europe calculated on the basis of the features describing their competitive capacity arises. The value added of these research is the analysis of competitive capacity conducted not only for EU countries, but also for geographical regions of Europe. In the paper, the verification of criteria using by World Economic Forum to assess the competitive capacity of EU economies was also conducted. In this area of the research, because of high level of correlation, many features from initial database were deleted.

scholarly journals Principal Graphs and Manifolds

2010 ◽  
pp. 28-59 ◽  
Author(s):  
Alexander N. Gorban ◽  
Andrei Y. Zinovyev
Keyword(s):  
Principal Component ◽  
Approximation Problem ◽  
Graph Grammar ◽  
Multidimensional Data ◽  
Data Set ◽  
Expectation Maximisation ◽  
The Family ◽  
Distance Approximation ◽  
Principal Points ◽  
Multidimensional Data Set

In many physical, statistical, biological and other investigations it is desirable to approximate a system of points by objects of lower dimension and/or complexity. For this purpose, Karl Pearson invented principal component analysis in 1901 and found ‘lines and planes of closest fit to system of points’. The famous k-means algorithm solves the approximation problem too, but by finite sets instead of lines and planes. This chapter gives a brief practical introduction into the methods of construction of general principal objects (i.e., objects embedded in the ‘middle’ of the multidimensional data set). As a basis, the unifying framework of mean squared distance approximation of finite datasets is selected. Principal graphs and manifolds are constructed as generalisations of principal components and k-means principal points. For this purpose, the family of expectation/maximisation algorithms with nearest generalisations is presented. Construction of principal graphs with controlled complexity is based on the graph grammar approach.

scholarly journals 3D Visualization of Sound Fields Perceived by an Acoustic Camera

2015 ◽  
Vol 15 (7) ◽  
pp. 45-57
Author(s):  
Nevena Popova ◽  
Georgi Shishkov ◽  
Petia Koprinkova-Hristova ◽  
Kiril Alexiev
Keyword(s):  
3D Visualization ◽  
Multidimensional Data ◽  
Sound Waves ◽  
Signal Processing Algorithm ◽  
Data Set ◽  
3 Dimensional ◽  
Sound Fields ◽  
Neuro Fuzzy ◽  
Real Time Visualization ◽  
Multidimensional Data Set

Abstract The paper summarizes the application results of a recently proposed neuro-fuzzy algorithm for multi-dimensional data clustering to 3-Dimensional (3D) visualization of dynamically perceived sound waves recorded by an acoustic camera. The main focus in the present work is on the developed signal processing algorithm adapted to the specificity of multidimensional data set recorded by the acoustic camera, as well as on the created software package for real-time visualization of the “observed” sound waves propagation.

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