A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions

Renewable energy consumption and economic growth in OECD countries: A nonlinear panel data analysis

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance In this paper, we hope to present a comprehensive review on ELM Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram The controversies of ELM were also discussed in this paper We aim to report these advances and find some future perspectives

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A review on extreme learning machine

Impact of natural resource rents on human development: What is the role of globalization in Asia Pacific countries?

Forest and mineral volatility and economic performance: Evidence from frequency domain causality approach for global data

Inflation is one of the most important indicator for economy and markets. Inflation represents a rate of rising of general level of prices for goods and services in regard to the currency falls of purchasing power. Although there many investigation of inflation phenomenon there is still missing gap about the factors analyzing which have influence on inflation. In this paper is applied multiple regression analysis to determine how consumer price index, monetary aggregates, discount rate, exchange rate affect inflation.

Analyzing of consumer price index influence on inflation by multiple linear regression

The linkage between energy resources and economic development is a topic of great interest. Research in this area is also motivated by contemporary concerns about global climate change, carbon emissions fluctuating crude oil prices, and the security of energy supply. The purpose of this research is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources. Our results indicate that GDP predictive accuracy can be improved slightly by applying a machine learning approach.

A machine learning approach for predicting the relationship between energy resources and economic development

Economic profit could be influenced by economic rents. However natural resource rents provided different impact on the economic growth or economic profit. The main focus of the study was to evaluate the economic growth as function of natural resource rents. For such a purpose machine learning approach, artificial neural network, was used. The used natural resource rents were coal rents, forest rents, mineral rents, natural gas rents and oil rents. Based on the results it is concluded that the machine learning approach could be used as the tool for the economic growth evaluation as function of natural resource rents. Moreover the more advanced approaches should be incorporated to improve more the forecasting accuracy.

Management of business economic growth as function of resource rents

/pdf/insurance-as-a-factor-for-the-development-of-the-1z6pe3aut4.pdf

Insurance as a factor for the development of the agricultural sector in the Republic of Serbia

Climate changes have a strong negative impact on the insurance sector, which is reflected in the slow development of the insurance sector and in the transfer of the greater part of risk on the state and individuals. The difference between collected and paid premiums on the basis of incurred losses is rapidly decreasing, which leads to the fact that insurance market is less and less capable of absorbing the losses associated with climate changes, which then has negative repercussions on the availability of insurance services at an affordable premium. The question of establishing potential long and short-term effects of climate changes on business activities of insurance and reinsurance companies represents a priority and its ultimate objective is to find ways to minimize risks and losses. 
The problem of climate changes represents an important social problem in today’s civilization. At the same time, it is also an ecological problem, but also economic, political, social, cultural, health, etc. It is a global ecological problem, hence we can speak about global climate changes which affect states, nations, continents regardless of where they are and how responsible they are for creating and sustaining these changes.

Dušan Cogoljević

Papers

Analyzing of consumer price index influence on inflation by multiple linear regression

A machine learning approach for predicting the relationship between energy resources and economic development

Management of business economic growth as function of resource rents

Insurance as a factor for the development of the agricultural sector in the Republic of Serbia

Insurance Sector and Climate Changes in Serbia