CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning

The industrialization has been the primary cause of the economic boom in almost all countries. However, this happened at the cost of the environment, as industrialization also caused carbon emissions to increase exponentially. According to the established literature, Gross Domestic Product (GDP) is related to carbon emissions (CO2) which could be optimally employed to precisely estimate a country's GDP. However, the scarcity of data is a significant bottleneck that could be handled using transfer learning (TL) which uses previously learned information to resolve new tasks, more specifically, related tasks. Notably, TL is highly vulnerable to performance degradation due to the deficiency of suitable information and hesitancy in decision-making. Therefore, this paper proposes ‘Intuitionistic Fuzzy Transfer Learning (IFTL)’, which is trained to use CO2 emission data of developed nations and is tested for its prediction of GDP in a developing nation. IFTL exploits the concepts of intuitionistic fuzzy sets (IFSs) and a newly introduced function called the modified Hausdorff distance function. The proposed IFTL is investigated to demonstrate its actual capabilities for TL in modeling hesitancy. To further emphasize the role of hesitancy modelled with IFSs, we propose an ordinary fuzzy set (FS) based transfer learning. The prediction accuracy of the IFTL is further compared with widely used machine learning approaches, extreme learning machines, support vector regression, and generalized regression neural networks. It is observed that IFTL capably ensured significant improvements in the prediction accuracy over other existing approaches whenever training and testing data have huge data distribution differences. Moreover, the proposed IFTL is deterministic in nature and presents a novel way for mathematically computing the intuitionistic hesitation degree.© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Analyzing economic structure and comparing the results of the predicted economic growth based on solow, fuzzy-logic and neural-fuzzy models

Investigating the factors effective on economic growth is of great importance for most economists. Although lots of studies have been done on economic growth in the world, it has less been regarded in Iran. In this article, by estimating growth regression, we attempt to investigate the supply side of economic growth in Iran. Then we compare the predictive results of Fuzzy-logic, Neural-Fuzzy and Solow models. The results show that there was negative significant relationship (i.e.–0.035) between unstable policy and economic growth rate in Iran during investigation period (1959–2001). In this model, the effect of expenses used by government is positive (i.e. 0.01). Furthermore, the estimated results of long term relationship show that the variable coefficients of capital, labor power, exportation, and inflation are 0.319, 0.016, 0.001, and–0.001, respectively. And also by comparing the predictive results of models for the average percent of annual growth, it is predicted that the average percent of Solow, Neural-Fuzzy, and Fuzzy-logic models are 7.17%, 5.92%, and 6.46% for 2002–2006, respectively. Evaluation of results from the models on the basis of criteria shows that model Neural-Fuzzy predicts better than Fuzzy-logic and Solow models. In other words, forecasting by the model Neural-Fuzzy is recommended

Energy consumption, economic growth, and CO2 emissions in G20 countries: Application of adaptive neuro-fuzzy inference system

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy

Building an ANFIS-based Decision Support System for Regional Growth: The Case of European Regions

This paper proposes a Decision Support System that can provide European policy makers with systematic guidance in allocating and prioritizing scant public resources. We do so by taking the stance of the Smart Specialisation Strategies which aim at consolidating the regional strengths and make effective and efficient use of public investment in R&D. By applying the ANFIS method we were able to understand how – and to what extent – the competitiveness drivers promoted technological development and how the latter contributes to the economic growth of European regions. We used socio-economic, spatial, and patent-based data to train, test and validate the models. What emerges is that an increase of R&D investments enhances the regional employment rate and the number of patents per capita; in turn, by taking into account the several combinations of specialization and diversification indicators, this leads to an increase of the regional GDP

Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.This research was funded by Universiti Teknologi Malaysia (UTM), Flagship UTMSHINE grant PY/2017/02187

The Design of Stacking Yards Management of The Early Warning System Model: A case study in Jakarta International Container Terminal, Indonesia

As a part of the Indonesia-National Logistics System, Jakarta International Container Terminal (JITC), facing a long dwell time which impacts to high yard occupancy ratio (YOR). This is happened because of the long necessary documents of clearance processing time, the limited yard provided, and the owners preferring for storing their goods in the terminal for cost reasons, etc. The objectives of this research are to design an early warning system model to avoid YOR above the normal by using adaptive neuro fuzzy inference system (ANFIS); designing the inter-agency institutional collaboration to apply the model by using interpretive Structural Modeling (ISM); and formulating YOR above normal mitigation strategy. The primary data used are collected through the in-depth interviews and the focus group discussions with the multi-discipline experts. The secondary data are collected from JITC daily operations and from other supporting agencies. The proposed model is validated, verified and tested. It shows the promising results. Keywords: dwell time, yard occupancy ratio, institution model, ANFIS, ISM

Measuring country sustainability performance using ensembles of neuro-fuzzy technique

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries' sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries' sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries' sustainability performance

An adaptive hierarchical fuzzy logic system for modelling and prediction of financial systems

In this thesis, an intelligent fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates is developed. The proposed system uses a Hierarchical Fuzzy Logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases. A fuzzy logic system is developed to model and predict three month quarterly interest rate fluctuations. The system is further trained to model and predict interest rates for six month and one year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to model and predict interest rates. A Feed Forward Fuzzy Logic system using fuzzy logic and genetic algorithms is developed to predict interest rates for three months periods. A back-propagation Hierarchical Neural Network system is further developed to predict interest rates for three months, six months and one year periods. These two systems are then compared with the Hierarchical Fuzzy Logic system results and conclusions on their accuracy of prediction are compared

Forecasting Automobile Demand Via Artificial Neural Networks & Neuro-Fuzzy Systems

The objective of this research is to obtain an accurate forecasting model for the demand for automobiles in Iran\u27s domestic market. The model is constructed using production data for vehicles manufactured from 2006 to 2016, by Iranian car makers. The increasing demand for transportation and automobiles in Iran necessitated an accurate forecasting model for car manufacturing companies in Iran so that future demand is met. Demand is deduced as a function of the historical data. The monthly gold, rubber, and iron ore prices along with the monthly commodity metals price index and the Stock index of Iran are Artificial neural network (ANN) and artificial neuro-fuzzy system (ANFIS) have been utilized in many fields such as energy consumption and load forecasting fields. The performances of the methodologies are investigated towards obtaining the most accurate forecasting model in terms of the forecast Mean Absolute Percentage Error (MAPE). It was concluded that the feedforward multi-layer perceptron network with back-propagation and the Levenberg-Marquardt learning algorithm provides forecasts with the lowest MAPE (5.85%) among the other models. Further development of the ANN network based on more data is recommended to enhance the model and obtain more accurate networks and subsequently improved forecasts