Analysis of the Index of Gender Inequality in the World by a Neural Approach

Khayria Karoui; Manel Zribi; Rochdi Feki

doi:doi:10.11648/j.ijdst.20210704.11

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Analysis of the Index of Gender Inequality in the World by a Neural Approach

Khayria Karoui, Manel Zribi, Rochdi Feki

Published in International Journal on Data Science and Technology (Volume 7, Issue 4)

Received: 14 April 2021 Accepted: 8 May 2021 Published: 23 November 2021

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Abstract

The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.

Published in	International Journal on Data Science and Technology (Volume 7, Issue 4)
DOI	10.11648/j.ijdst.20210704.11
Page(s)	69-73
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Gender Inequality Index, Self-Organizing Maps, Classification of Countries

References

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Khayria Karoui, Manel Zribi, Rochdi Feki. (2021). Analysis of the Index of Gender Inequality in the World by a Neural Approach. International Journal on Data Science and Technology, 7(4), 69-73. https://doi.org/10.11648/j.ijdst.20210704.11

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ACS Style

Khayria Karoui; Manel Zribi; Rochdi Feki. Analysis of the Index of Gender Inequality in the World by a Neural Approach. Int. J. Data Sci. Technol. 2021, 7(4), 69-73. doi: 10.11648/j.ijdst.20210704.11

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AMA Style

Khayria Karoui, Manel Zribi, Rochdi Feki. Analysis of the Index of Gender Inequality in the World by a Neural Approach. Int J Data Sci Technol. 2021;7(4):69-73. doi: 10.11648/j.ijdst.20210704.11

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@article{10.11648/j.ijdst.20210704.11,
  author = {Khayria Karoui and Manel Zribi and Rochdi Feki},
  title = {Analysis of the Index of Gender Inequality in the World by a Neural Approach},
  journal = {International Journal on Data Science and Technology},
  volume = {7},
  number = {4},
  pages = {69-73},
  doi = {10.11648/j.ijdst.20210704.11},
  url = {https://doi.org/10.11648/j.ijdst.20210704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20210704.11},
  abstract = {The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.},
 year = {2021}
}

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TY  - JOUR
T1  - Analysis of the Index of Gender Inequality in the World by a Neural Approach
AU  - Khayria Karoui
AU  - Manel Zribi
AU  - Rochdi Feki
Y1  - 2021/11/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdst.20210704.11
DO  - 10.11648/j.ijdst.20210704.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 69
EP  - 73
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20210704.11
AB  - The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.
VL  - 7
IS  - 4
ER  -

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Author Information

Khayria Karoui

Research Unit Development Economics (URED) FSEG, University of Sfax, Tunisia
Manel Zribi

Laboratory of Applied Economics (UREA) FSEG, University of Sfax, Tunisia
Rochdi Feki

Research Unit Development Economics (URED), Graduate School of Business, and University of Sfax, Tunisia

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APA Style

Khayria Karoui, Manel Zribi, Rochdi Feki. (2021). Analysis of the Index of Gender Inequality in the World by a Neural Approach. International Journal on Data Science and Technology, 7(4), 69-73. https://doi.org/10.11648/j.ijdst.20210704.11

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ACS Style

Khayria Karoui; Manel Zribi; Rochdi Feki. Analysis of the Index of Gender Inequality in the World by a Neural Approach. Int. J. Data Sci. Technol. 2021, 7(4), 69-73. doi: 10.11648/j.ijdst.20210704.11

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AMA Style

Khayria Karoui, Manel Zribi, Rochdi Feki. Analysis of the Index of Gender Inequality in the World by a Neural Approach. Int J Data Sci Technol. 2021;7(4):69-73. doi: 10.11648/j.ijdst.20210704.11

Copy | Download

@article{10.11648/j.ijdst.20210704.11,
  author = {Khayria Karoui and Manel Zribi and Rochdi Feki},
  title = {Analysis of the Index of Gender Inequality in the World by a Neural Approach},
  journal = {International Journal on Data Science and Technology},
  volume = {7},
  number = {4},
  pages = {69-73},
  doi = {10.11648/j.ijdst.20210704.11},
  url = {https://doi.org/10.11648/j.ijdst.20210704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20210704.11},
  abstract = {The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Analysis of the Index of Gender Inequality in the World by a Neural Approach
AU  - Khayria Karoui
AU  - Manel Zribi
AU  - Rochdi Feki
Y1  - 2021/11/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdst.20210704.11
DO  - 10.11648/j.ijdst.20210704.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 69
EP  - 73
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20210704.11
AB  - The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.
VL  - 7
IS  - 4
ER  -

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