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

Causality between Gross Domestic Product and Health Care Expenditure in the Augmented Solow’s Growth Model

This paper examines conditional convergence of OECD countries in gross domestic product (GDP) and health care expenditure (HCE) per capita. It extends the augmented Solow model by incorporating health capital to explain variations in output and expenditure per capita across countries. The issue of causality between GDP and HCE is investigated. The results show that HCE has positive effect on the economic growth and the speed of convergence. In the HCE model a regression of the speed of convergence on variables determining the rate of convergence show close link to the variables characterising the health care system of sample countries

Un enfoque de sustentabilidad utilizando lógica difusa y minería de datos

[ES] Sustainable development goals are now the agreed criteria to monitor states, and this work will demonstrate that numerical and graphical methods are valuable tools in assessing progress. Fuzzy Logic is a reliable procedure for transforming human qualitative knowledge into quantitative variables that can be used in the reasoning of the type “if, then” to obtain answers pertaining to sustainability assessment. Applications of machine learning techniques and artificial intelligence procedures span almost all fields of science. Here, for the first-time, unsupervised machine learning is applied to sustainability assessment, combining numerical approaches with graphical procedures to analyze global sustainability. CD HJ-Biplots to portray graphically the sustainability position of a large number of countries are a useful complement to mathematical models of sustainability. Graphical information could be useful to planners it shows directly how countries are grouped according to the most related sustainability indicators. Thus, planners can prioritize social, environmental, and economic policies and make the most effective decisions. One could graphically observe the dynamic evolution of sustainability worldwide over time with a graphical approach used to draw relevant conclusions. In an era of climate change, species extinction, poverty, and environmental migration, such observations could aid political decision-making regarding the future of our planet. A large number of countries remain in the areas of moderate or low sustainability. Fuzzy logic has proven to be an uncontested numerical method as it occurs with SAFE. An unsupervised learning method called Variational Autoencoder interplay Graphical Analysis (VEA&GA) has been proposed, to support sustainability performance with appropriate training data. The promising results show that this can be a sound alternative to assess sustainability, extrapolating its applications to other kinds of problems at different levels of analysis (continents, regions, cities, etc.) further corroborating the effectiveness of the unsupervised training methods

Efficiency in South African agriculture : a two-stage fuzzy approach

PURPOSE : The purpose of this paper is to assess the efficiency of agricultural production in South Africa from 1970 to 2014, using an integrated two-stage fuzzy approach. DESIGN/METHODOLOGY/APPROACH : Fuzzy technique for order preference by similarity to ideal solution is used to assess the relative efficiency of agriculture in South Africa over the course of the years in the first stage. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of socio-economic and demographic variables on agricultural efficiency. They are compared with the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2016a). FINDINGS : The results show that the fuzzy efficiency estimates ranged from 0.40 to 0.68 implying inefficiency in South African agriculture. The results further reveal that research and development, land quality, health expenditure–population growth ratio have a significant, positive impact on efficiency levels, besides the GINI index. In terms of accuracy, fuzzy regressions outperformed the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2015). PRACTICAL IMPLICATIONS : Policies to increase social expenditure especially in terms of health and hence productivity should be prioritized. Also policies aimed at conserving the environment and hence the quality of land is needed. ORIGINALITY/VALUE : The paper is original and has not been previously published elsewhere.https://www.emerald.com/insight/publication/issn/1463-5771hj2019Economic

Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

One of the most pervasive trends in the global airline industry over the past few three decades has been the rapid development of low cost carriers (LCCs). Australia has not been immune to this trend. Following deregulation of Australia’s domestic air travel market in the 1990s, a number of LCCs have entered the market, and these carriers have now captured around 31 per cent of the market. Australia’s LCCs require reliable and accurate passenger demand forecasts as part of their fleet, network, and commercial planning and for scaling investments in fleet and their associated infrastructure. Historically, the multiple linear regression (MLR) approach has been the most popular and recommended method for forecasting airline passenger demand. In more recent times, however, new advanced artificial intelligence-based forecasting approaches – artificial neural networks (ANNs), genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) - have been applied in a broad range of disciplines. In light of the critical importance of passenger demand forecasts for airline management, as well as the recent developments in artificial intelligence-based forecasting methods, the key aim of this thesis was to specify and empirically examine three artificial intelligence-based approaches (ANNs, GA and ANFIS) as well as the MLR approach, in order to identify the optimum model for forecasting Australia’s domestic LCCs demand. This is the first time that such models – enplaned passengers (PAX) and revenue passenger kilometres performed (RPKs) – have been proposed and tested for forecasting Australia’s domestic LCCs demand. The results show that of the four modeling approaches used in this study that the new, and novel, ANFIS approach provides the most accurate, reliable, and highest predictive capability for forecasting Australia’s LCCs demand. A second aim of the thesis was to explore the principal determinants of Australia’s domestic LCCs demand in order to achieve a greater understanding of the factors which influence air travel demand. The results show that the primary determinants of Australia’s domestic LCCs demand are real best discount airfare, population, real GDP, real GDP per capita, unemployment, world jet fuel prices, real interest rates, and tourism attractiveness. Interestingly three determinants, unemployment, tourism attractiveness, and real interest rates, which have not been empirically examined in any previously reported study of Australia’s domestic LCCs demand, proved to be important predictor variables of Australia’s domestic LCCs demand. The thesis also found that Australia’s LCCs have increasingly embraced a hybrid business model over the past decade. This strategy is similar to LCCs based in other parts of the world. The core outcome of this research, the fact that modelling based on artificial intelligence approaches is far more effective than the traditional models prescribed by the International Civil Aviation Organization (ICAO), means that future work is essential to validate this. From an academic perspective, the modelling presented in this study offers considerable promise for future air travel demand forecasting. The results of this thesis provide new insights into LCCs passenger demand forecasting methods and can assist LCCs executives, airports, aviation consultants, and government agencies with a variety of future planning considerations

Circular Economy and Sustainable Development: A Systematic Literature Review

Circular Economy put forth as an alternative to traditional linear model of extract-use-dispose along with the concept of Sustainable Development encompassing economic, environmental, and social aspects have garnered tremendous impetus among academics, practitioners and policymakers alike. The UN Sustainable Development Goals embraced by the member nations in 2015 based on the preceding Millenium Development Goals have been placed as the targets to be achieved as a part of holistic human development. In this backdrop, this paper examines the intersection of sustainability and circular economy with a focus on the three aspects of sustainable development, first the economic aspect by examining the relationship between GDP and circular economy, second the social economic aspect within the interaction of Circular Economy with Sustainable development and third the environmental-economical aspect by examining circularity and sustainability in waste management and waste valorisation. This paper achieves its objective through a systematic literature review of 1748 journal articles collected from Web of Science and SCOPUS database following PRISMA standards, network analysis of keywords, and manual review of texts. Four Research Questions are formulated: RQ1: What are the major emergent topics in Circular Economy and Sustainable Development and how are they related? RQ2: What is the relationship among CE and GDP in the CE and Sustainability? RQ3: What are the relationships between CE and Sustainability? RQ4: What are different use cases of valorisation of waste as CE tool, and can valorisation be sustainable? RQ1 is answered by presenting hotspot of research on Circular Economy and Sustainable Development through keywords occurrence network analysis using VosViewer. This study identifies three clusters and seven thematic areas of research, along with 25 most used keywords. RQ2 is attended through review of the relationship between economic growth (Gross Domestic Product) and Circular Economy and proposes based on the review that CE is still at its infancy. The paper also discusses the appropriateness of using GDP as a measure of sustainable development. This paper addresses RQ3 by examining the relationship between Circular Economy and Sustainable Development through review of literatures. The indicators used to measure CE and SD are also discussed and summarised. This review finds that achieving SDGs require greater effort, and that the present status of achievement is a bleak picture. Further, the role of waste management and potentiality of waste valorisation to aid in circular economy and sustainable development is analysed to answer RQ4. Though there are ample potential, however the recycle rate is very minimal to quench the required level of circularity. While CE and SD are related, CE cannot be a universal panacea to global challenges like emissions reduction, energy consumption, climate change, gender equality, poverty, well-being, environmental protection etc. even though the impact of CE to achieve SD can be substantial. The paper recommends avenues for future research and presents the conclusion of the study

Food security modelling using two stage hybrid model and fuzzy logic risk assessment

Food security has become a key issue worldwide in recent years. According to the Department for Environment Food and Rural Affair (DEFRA) UK, the key components of food security are food availability, global resource sustainability, access, food chain resilience, household food security, safety and confidence of public towards food system. Each of these components has its own indicators which need to be monitored. Only a few studies had been made towards analysing food security and most of these studies are based on conventional data analysis methods such as the use of statistical techniques. In handling food security datasets such as crops yield, production, economy growth, household behaviour and others, where most of the data is imprecise, non-linear and uncertain in nature, it is better to handle the data using intelligent system (IS) techniques such as fuzzy logic, neural networks, genetic algorithm and hybrid systems, rather than conventional techniques. Therefore this thesis focuses on the modelling of food security using IS techniques, and a newly developed hybrid intelligent technique called a 2-stage hybrid (TSH) model, which is capable of making accurate predictions. This technique is evaluated by considering three applications of food security research areas which relate to each of the indicators in the DEFRA key food security components. In addition, another food security model was developed, called a food security risk assessment model. This can be used in assessing the level of risk for food security. The TSH model is constructed by using two key techniques; the Genetic Algorithm (GA) module and the Artificial Neural Network (ANN) module, where these modules combine the global and local search, by optimizing the inputs of ANN in the first stage process and optimizing of weight and threshold of ANN, which is then used to remodel the ANN resulting in better prediction. In evaluating the performance of the TSH prediction model, a total of three datasets have been used, which relate to the food security area studied. These datasets involve the prediction of farm household output, prediction of cereal growth per capita as the food availability main indicators in food security component, and grain security assessment prediction. The TSH prediction model is benchmarked against five others techniques. Each of these five techniques uses an ANN as the prediction model. The models used are: Principal Component Analysis (PCA), Multi-layered Perceptron-Artificial Neural Network (MLP-ANN), feature selection (FS) of GA-ANN, Optimized Weight and Threshold (OWTNN) and Sensitive Genetic Neural Optimization (SGNO). Each of the application datasets considered is used to show the capability of the TSH model in making effective predictions, and shows that the general performance of the model is better than the other benchmarked techniques. The research in this thesis can be considered as a stepping-stone towards developing other tools in food security modelling, in order to aid the safety of food security

Єдина Європа: Погляд у майбутнє

Викладено результати наукових досліджень щодо сучасних проблем глобалізації та інтеграційних процесів, участі в них українських та польських підприємств, вирішення екологічних та соціально-економічних питань, пов’язаних з інтеграційними процесами, підвищенням конкуренції та інтенсифікацією виробництва. Видання буде корисним для наукових співробітників, фахівців-практиків, які займаються проблемами європейського розвитку, викладачів, аспірантів, студентів вищих навчальних закладів, урядових і неурядових аналітичних організацій, інституцій ЄС