POPULATION FORECAST WITH THE DATA PROCESSING GROUP METHOD (GMDH) type NEURAL NETWORK FOR EUROPEAN UNION COUNTRIES

2021 ◽  
Vol 6 (15) ◽  
pp. 563-598
Author(s):  
Eda FENDOĞLU
Keyword(s):  
Neural Network ◽  
European Union ◽  
Population Estimation ◽  
Percentage Error ◽  
Group Method ◽  
Data Set ◽  
Eu Countries ◽  
Comparison Results ◽  
The World ◽  
Annual Population

Population is a critically important factor in a country's planning, policy making, and setting its social and economic goals. Population estimation and planning in advance are of great importance for policy makers, since the natural resources, which are the production areas where people can meet their basic needs, are limited and they need to protect the areas they live in in order to continue their lives. In this study, the Group Method of Data Handling (GMDH) type Neural Network (NN) approach was used for the annual population estimation of 27 European Union (EU) countries (Germany, Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Cyprus, Croatia, Netherlands, Ireland, Spain, Sweden, Italy, Latvia, Lithuania, Luxembourg, Hungary, Malta, Poland, Portugal, Romania, Slovenia, Slovak Republic, Greece). The data set was obtained from the World Data Bank and analyzed using data from the years 1960 - 2020. The test performances obtained are generally below 10% of the Root Mean Square Percentage Error (RMSPE). The coefficient of determination (R^2) is above 0.90 and generally around 0.99. In addition, the mean absolute percentage error (MAPE) value is below 10%. According to these values, it is concluded that the model predicts extremely accurately. In addition, the analysis was compared with the 2021 - 2032 forecast values in the World Bank Database. According to the findings and comparison results, it has been concluded that the GMDH type Neural Network is a very good approach for the annual population estimation of 27 EU countries, it has almost exactly the same results with the real values in the past years, therefore it is consistent and successful in its predictions for the future years.

scholarly journals A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2058 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Ahmed Al-AbdulJabbar ◽  
Khaled Abdelgawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mutual Effect ◽  
Percentage Error ◽  
Drilling Process ◽  
Ann Model ◽  
Data Set ◽  
Individual Parameter ◽  
Rate Of Penetration ◽  
Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

scholarly journals Graph-neural-network-based delay estimation for communication networks with heterogeneous scheduling policies

2021 ◽  
Vol 2 (4) ◽  
pp. 1-8
Author(s):  
Martin Happ ◽  
Matthias Herlich ◽  
Christian Maier ◽  
Jia Lei Du ◽  
Peter Dorfinger
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Communication Networks ◽  
Delay Estimation ◽  
Percentage Error ◽  
Extended Model ◽  
Data Set ◽  
Scheduling Policies ◽  
One Step ◽  
Graph Neural Networks

Modeling communication networks to predict performance such as delay and jitter is important for evaluating and optimizing them. In recent years, neural networks have been used to do this, which may have advantages over existing models, for example from queueing theory. One of these neural networks is RouteNet, which is based on graph neural networks. However, it is based on simplified assumptions. One key simplification is the restriction to a single scheduling policy, which describes how packets of different flows are prioritized for transmission. In this paper we propose a solution that supports multiple scheduling policies (Strict Priority, Deficit Round Robin, Weighted Fair Queueing) and can handle mixed scheduling policies in a single communication network. Our solution is based on the RouteNet architecture as part of the "Graph Neural Network Challenge". We achieved a mean absolute percentage error under 1% with our extended model on the evaluation data set from the challenge. This takes neural-network-based delay estimation one step closer to practical use.

scholarly journals A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Ali M. Al-Salihi ◽  
Zahraa A. AL-Ramahy
Keyword(s):  
Neural Network ◽  
Soil Temperature ◽  
Hydrological Cycle ◽  
Absolute Error ◽  
Training Data ◽  
Percentage Error ◽  
Ann Model ◽  
Temperature Estimation ◽  
Data Set ◽  
Daily Data

Soil temperature is an important meteorological variable which plays a significant role in hydrological cycle. In present study, artificial intelligence technique employed for estimating for 3 daysa head soil temperature estimation at 10 and 20 cm depth. Soil temperature daily data for the period 1 January 2012 to 31 December 2013 measured in three stations namely (Mosul, Baghdad and Muthanna) in Iraq. The training data set includes 616 days and the testing data includes 109 days. The Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian regularization algorithms. To evaluate the ANN models, Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE) and Correlation Coefficient (r) were determined. According to the four statistical indices were calculated of the optimum ANN model, it was ANN model (3) in Muthanaa station for the depth 10 cm and ANN model (3) in Baghdad station for the depth 20 were (RMSE=0.959oC, MAE=0.725, MAPE=4.293, R=0.988) and (RMSE=0.887OC, MAE=0.704, MAPE=4.239, R=0.993) respectively, theses statistical criteria shown the efficiency of artificial neural network for soil temperature estimation.

scholarly journals SYNTHESIS OF SELF-ORGANIZING MAP AND FEEDFORWARD NEURAL NETWORK FOR BETTER FORECASTING

2014 ◽  
pp. 68-75
Author(s):  
Oles Hodych ◽  
Yuriy Shcherbyna ◽  
Michael Zylan
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Share Price ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Price Fluctuation ◽  
Price Data ◽  
Comparison Results ◽  
Self Organizing

In this article the authors propose an approach to forecasting the direction of the share price fluctuation, which is based on utilization of the Feedforward Neural Network in conjunction with Self-Organizing Map. It is proposed to use the Self-Organizing Map for filtration of the share price data set, whereas the Feedforward Neural Network is used to forecast the direction of the share price fluctuation based on the filtered data set. The comparison results are presented for filtered and non-filtered share price data sets.

scholarly journals Evaluating the liberalization process on Telecommunications services for EU countries

2018 ◽  
Vol 7 (3) ◽  
pp. 98
Author(s):  
Christos Agiakloglou ◽  
Michael Polemis
Keyword(s):  
European Union ◽  
International Market ◽  
Regulatory Process ◽  
Market Segments ◽  
Data Set ◽  
Eu Countries ◽  
On Demand ◽  
Before And After ◽  
Telecommunications Services ◽  
Main Determinants

This paper investigates the main determinants of Telecommunications demand for European Union (EU) countries using a panel data set for 19 EU countries over the period 1991-2010, capturing the years before and after the liberalization process.  The goal is to clarify whether any changes in the demand of Telecommunications, as expressed by volume of traffic in local, mobile and international market segments, are attributed to regulatory process or to some other major drivers, taking also into account the relevant price elasticities.  It turns out that the regulatory process does not seem to have significant impact on demand for Telecommunications services for the first period of liberalization.

scholarly journals Non-Tariff Measures in Indian Context and the European Union

2018 ◽  
Vol 10 (9) ◽  
pp. 54
Author(s):  
Rakhi Singh ◽  
Seema Sharma ◽  
Deepak Tandon
Keyword(s):  
European Union ◽  
Structural Changes ◽  
The European Union ◽  
Strategic Partner ◽  
Data Set ◽  
Per Capita Gdp ◽  
Indian Economy ◽  
The World ◽  
Per Capita ◽  
The Eu

Indian economy is one of the fastest growing economies in the world today. In line with global trade trends, Indian export sector has been growing and contributing significantly to the economy. Given its exports structure, India is well positioned to benefit from the structural changes in technology and emerging forces of globalization. Indian economy has shown remarkable progress in terms of foreign trade after the introduction of economic reforms in 1991. The European Union (EU) is a very important trading partner of India. The trade volumes between India and EU have shown remarkable improvement in last one and a half decade. After starting out at a relatively low level in the 1990’s, the trade volumes, both with respect to Indian exports to the EU as well as with respect to Indian imports from the EU, started to increase most noticeably after the year 2001.Use of non-tariff measures (NTMs) as means of protection has captured a lot of focus after reduction of tariffs in the world trade. India even after being a strategic partner for European Union (EU) has to face lot of NTMs on its exports. Based on studies in the past, link between the incidence of NTMs imposed by the home country and the income level of the foreign country has been established. The interplay of incidence of NTMs and the GDP remains largely unexplored in the context of India-EU trade relationship. This paper tries to fill this gap and show the importance of the study in policy decisions. Authors have used UNCTAD’s NTM data and Spearman’s correlation coefficient to measure the strength and direction of the relationship between incidence of NTM with per capita GDP of the exporting country (India). The authors have used different permutations of data from the main data set (1994-95 to 2016-17) for analysis and have concluded that incidence of NTMs on Indian exports to EU is positively co-related to the per capita GDP of India.

scholarly journals CHALLENGES AND DEVELOPMENT OF THE ASYLUM SYSTEM IN THE EUROPEAN UNION

2020 ◽  
Vol 3 (8) ◽  
pp. 54-63
Author(s):  
Iveta Adijāne
Keyword(s):  
European Union ◽  
Comparative Method ◽  
Time Frame ◽  
Legal Framework ◽  
The European Union ◽  
Eu Countries ◽  
Eu Member States ◽  
The World ◽  
Asylum Procedure ◽  
The Eu

There still is a lack of unity among EU Member States on asylum issues, both, in the practical application of the existing legal framework and in the direction of the common asylum system. Latvia is subject of both international and European Union common asylum conditions. Any changes in the scale of the European Union affect Latvia, and the world situation in the field of refugees also affects our country. The aim of this article is to analyse the current situation of asylum in the EU, touching upon main trends in the world of refugees, and to identify the main problems in the existing asylum procedure in the EU. In order to achieve objectives, following research methods were used: monographic research of theoretical and empirical sources in order to analyse and evaluate various asylum domain information, analytical method in order to acquire legislative content and verities, comparative method in order to discover differences in legislation of asylum procedure in EU countries, systemic method in order to disclose interconnections in legislation, descriptive statistics method and correlation analysis in order to analyse process of asylum procedure and determine interconnections in asylum procedure time frame between legislation and practical instances in EU countries.

scholarly journals Społeczeństwo XXI wieku wobec zagrożenia islamskim terroryzmem

2018 ◽  
Vol 40 (1) ◽  
pp. 73-84
Author(s):  
Kamila Kasperska-Kurzawa
Keyword(s):  
European Union ◽  
Mass Scale ◽  
Islamic State ◽  
The European Union ◽  
Military Operations ◽  
Eu Countries ◽  
Sense Of Security ◽  
The World ◽  
European Union Countries ◽  
The Eu

SOCIETY OF THE 21ST CENTURY AGAINST THE THREAT OF ISLAMIC TERRORISMThe subject matter includes the issue of transformation in the consciousness of the societies of European Union countries, but also communities in other areas of the world, perception of the phenomenon of migration to the territories of native countries, mainly in the European Union. The period of rapid socio-political changes in Islamic states, as well as the outbreak of civil war in 2011 in Syria, was the largest stimulator of migration movements from the Middle East, especially those covered by military operations in Europe. Hundreds of thousands of migrants continued to reach EU countries. Germany widely opened the door of its state and accepted the largest number of migrants. Some countries, such as Poland or Hungary, refused to accept migrants from countries with an Islamic origin. Migration on such a mass scale caused many social problems. The perceived sense of security of the community has deteriorated considerably in the EU countries where the most migrants came. The decline in the sense of security included areas not only of safety for life and health, but also concerns about reducing the level of social status or increasing unemployment. However, the biggest threat to the community of the EU countries, and many other countries in the world was ahuge increase in terrorist attacks, where the attackers came from orthodox Islamist groups. It should be added that the majority of migrants were Muslims. Another phenomenon also affecting the reduction of the level of perceptible security of European societies was the reactivation of political groups that in their ideologies presented the slogans of populism, nationalism, racism, or even fascism. There has been a clear polarization of Western societies, where until now they were arefuge of democracy, tolerance and values for which they fought for years. Undoubtedly, the politics of Erdogan, the president of Turkey, and the president of Russia, Putin, also influencedthestate of security of societies, and tried to influence EU decisions with their actions. Russia, let the annexation of Crimea and activities in Ukraine be left in peace, and Turkey, to force the EU to acceleratethe admission of this country to the EU. Also calling up the so-called Islamic state posed a huge threat to the security of the communities of European Union countries with attacks inspired by this terrorist group.

scholarly journals Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

2019 ◽  
Vol 8 (2) ◽  
pp. 2550-2563
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Early Detection ◽  
Clinical Data ◽  
Process Model ◽  
Research Work ◽  
Process Time ◽  
Data Set ◽  
The World

Chronic kidney disease (CKD) is one of the most widely spread diseases across the world. Mysteriously some of the areas in the world like Srilanka, Nicrgua and Uddanam (India), this disease affect more and it is cause of thousands of deaths particular areas. Now days, the prevention with utilizing statistical analysis and early detection of CKD with utilizing Machine Learning (ML) and Neural Networks (NNs) are the most important topics. In this research work, we collected the data form Uddanam (costal area of srikakulam district, A.P, India) about patient’s clinical data, living styles (Habits and culture) and environmental conditions (water, land and etc.) data from 2016 to 2019. In this paper, we conduct the statistical analysis, Machine Learning (ML) and Neural Network application on clinical data set of Uddanam CKD for prevention and early detection of CKD. As per statistical analysis we can prevent the CKD in the Uddanam area. As per ML analysis Naive Bayes model is the best where the process model is constructed within 0.06 seconds and prediction accuracy is 99.9%. In the analysis of NNs, the 9 neurons hidden layer (HL) Artificial Neural Network (ANN) is very accurate than other all models where it performs 100% of accuracy for predicting CKD and it takes the 0.02 seconds process time.

scholarly journals The impact of globalization on the business position of European Union

2020 ◽  
Vol 74 ◽  
pp. 05022
Author(s):  
Zuzana Rosnerova ◽  
Dagmar Hraskova
Keyword(s):  
European Union ◽  
World Trade Organization ◽  
World Trade ◽  
Financial Flows ◽  
Eu Countries ◽  
Power Drive ◽  
The World ◽  
Export Shares ◽  
The Impact ◽  
The Eu

We are meeting with the term globalization for many years. The globalization process sets in motion goods, services, financial flows, information, through globalization come to the mobility of people, the workforce and globalization is also a power drive to move the whole world. This paper deals with the contribution of globalization to the EU market. The aim is to find out to what extent EU countries are involved in world trade. It also points to the position of the World Trade Organization, which is the only organization connecting the countries of the world, with the aim strengthening of world trade and ensuring its liberalization. We assume that the EU as the largest integration group in the world will play an important role in world trade and that EU countries will be among the top 10 world players. The document contains an analysis of the EU’s position in world trade. The methodology used is based on comparing the export shares of the top 10 world trade countries and assessing how the countries of the EU positioned on the scale in 2018. The discussion deals with assessing the findings and estimating the situation in the future.

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