4653 research outputs found
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Rural tourism in İzmir: Social, cultural and economic aspects on potential and existing destinations
Get PDFA big data analytics based methodology for strategic decision making
No full textPurpose The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this study, two different machine learning algorithms, Random Forest (RF) and ArtifiComputer Science, Interdisciplinary Applications; Information Science & Library Science; ManagementComputer Science; Information Science & Library Science; Business & Economic
Digital transformation of traditional marketing business model in new industry era
No full textPurpose Impact of the digitalization on the production and service sector is a highly popular topic in these days and especially, new business models receive increasingly more attention. Under the light of digitalization, the Fourth Industrial Revolution,Computer Science, Interdisciplinary Applications; Information Science & Library Science; ManagementComputer Science; Information Science & Library Science; Business & Economic
An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling
Get PDFThe world is moving into a situation where resource scarcity leads to an increase in material cost. A possible way to deal with the above challenge is to adopt Circular Economy (CE) concepts to make a close loop of material by eliminating industrial or post-consumer wastes. Integration of emerging technologies such as Artificial Intelligence (AI), machine learning, and big data analytics provides significant support in successfully adopting and implementing CE practices. This study aims to explore the applications of AI techniques in enhancing the adoption and implementation of CE practices. A systematic literature review was performed to analyze the existing scenario and the potential research directions of AI in CE. A collection of 220 articles was shortlisted from the SCOPUS database in the field of AI in CE. A text mining approach, known as Structural Topic Modeling (STM), was used to generate different thematic topics of AI applications in CE. Each generated topic was then discussed with shortlisted articles. Further, a bibliometric study was performed to analyze the research trends in the field of AI applications in CE. A research framework was proposed for AI in CE based on the review conducted, which could help industrial practitioners, and researchers working in this domain. Further, future research propositions on AI in CE were proposed
Prediction and evaluation of greenhouse gas emissions for sustainable road transport within Europe
Get PDFEnvironmental pollution leads a rise in sustainable development problems. Greenhouse gases(GHG) are one of the most important barriers against to sustainable development and greener cities. When the causes of GHG are investigated, human activities appeared as one of the main reasons. As one of the human activities, transportation has the highest impact on the increase in GHG emissions in green cities. Of the different transportation modes such as air, railways, and road, this study focused on road transportation due to its greater impact when compared to others in terms of GHG emissions. GHG emission estimation should be the initial step to evaluate current status of countries. Therefore, this study aims to make detailed analysis of GHG emissions in four European countries, due to different types of road transport vehicles. Grey prediction is used to estimate the amount of GHG emissions for each road transport vehicle for each country. In conclusion, implications are presented in order to reduce the GHG emissions and to meet sustainable development conditions according to the numerical results of the estimation and it is aimed to prepare a base for new studies about GHG emissions in road transportation
Which households are more energy vulnerable? Energy poverty and financial inclusion in Turkey
Get PDFThis study examines the effects of financial inclusion on energy poverty using the 2018 Turkish Household Budget and Consumption Expenditure Surveys. The study adopts three different measures of energy poverty and then analyzes the impact of financial inclusion proxied by a multidimensional index on energy poverty using different estimation strategies. After addressing the endogeneity of financial inclusion by instrumenting financial inclusion with access to the nearest bank in a two-stage least squares framework, the empirical results show that financial inclusion significantly alleviates energy poverty while its impact is higher for female-headed households. These findings are robust to Oster's (2019) bounds estimates that deal with omitted variable bias. The results also suggest that health and income are significant through which financial inclusion influences energy poverty. The findings thus point to the need for policies that promote financial inclusion as a way of alleviating energy poverty
Subspace-Based Emulation of the Relationship between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things
No full textWe develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during training. We apply our methodology to the case of Joint Forecasting-Scheduling (JFS) for the Internet of Things (IoT). Our results hold potential to improve the performance of JFS in next-generation networks and can be applied to a much wider range of problems beyond IoT
Smart circular supply chains to achieving SDGs for post-pandemic preparedness
Get PDFPurpose: The coronavirus disease 2019 (COVID-19) pandemic created heavy pressure on firms, by increasing the challenges and disruptions that they have to deal with on being sustainable. For this purpose, it is aimed to reveal the role of the smart circular supply chain (SCSC) and its enablers towards achieving Sustainable Development Goals (SDGs) for post-pandemic preparedness. Design/methodology/approach: Total interpretive structural modelling and Matrice d'Impacts Croises Multipication Applique' a un Classement (MICMAC) have been applied to analyse the SCSC enablers which are supported by the natural-based resource view in Turkey's food industry. In this context, industry experts working in the food supply chain (meat sector) and academics came together to interpret the result and discuss the enablers that the supply chain experienced during the pandemic for creating a realistic framework for post-pandemic preparedness. Findings: The results of this study show that “governmental support” and “top management involvement” are the enablers that have the most driving power on other enablers, however, none of them depend on any other enablers. Originality/value: The identification of the impact and role of enablers in achieving SDGs by combining smart and circular capabilities in the supply chain for the post-pandemic
mu-Proximity structure via hereditary classes
No full textA new generalised mu-proximity structure is obtained using hereditary class on a set