scholarly journals Application of artificial intelligence in settlement development modeling

2021 ◽  
Vol 11 (5) ◽  
pp. 256-263
Author(s):  
Luca Kozák ◽  
Attila Házy ◽  
Laura Veres
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Systems ◽  
Social Innovation ◽  
Knowledge Bases ◽  
Web Based ◽  
Good Practices ◽  
Settlement Development ◽  
Innovation Potential ◽  
Neuro Fuzzy ◽  
The Social

In the book (Házy-Veres et al., 2020), we presented a model that applies artificial intelligence and neuro-fuzzy systems to the settlement development. The model is based on the creation of two knowledge bases, databases: one a database of good practices and a second one of settlements. Based on this result we created a web-based application to measure the social innovation potential of settlements and support implementing good practices.

Regional Disparities of the Social Innovation Potential in the Visegrad Countries: Causes and Consequences

2018 ◽  
Vol 5 (1) ◽  
pp. 36-42
Author(s):  
György Kocziszky ◽  
Dóra Szendi
Keyword(s):  
Social Innovation ◽  
Technical Innovation ◽  
The European Union ◽  
Social Culture ◽  
Complex Character ◽  
Innovation Potential ◽  
The Social ◽  
Territorial Disparities ◽  
Active Contribution ◽  
Visegrád Countries

Abstract The international literature is paying significant and increasing attention to the analysis of the regions’ innovation potential, and its active contribution to economic growth and competitiveness. Beside the classical, technical innovation, also the social innovation is getting even more emphasis. It can solve as alternative basically in the case of the peripheral territories. The convergence of peripheries is a stressed priority in the European Union. The territorial disparities are resulting in significant social and political problems also in the case of the Visegrad countries’ regions. The authors in their research represent a possible method for the measurement of regional (NUTS-2) level social innovation potential on the example of the Visegrad countries, and they also analyse the causes and consequences of disparities. The applied complex social innovation index can be calculated as a result of three pillars (economic, social, culture and attitude), and several components. As a result of the created patterns can be concluded that compared to the economic indicators, the disadvantage of the peripheries is not so significant in the case of the social innovation index, because of the complex character of the index. In the second part of the research, the authors analyse and evaluate also the methods, which can be adequate for increasing the social innovation potential.

Digital Social Innovation

2019 ◽  
pp. 184-211
Author(s):  
Alicia Guerra Guerra ◽  
Lyda Sánchez de Gómez ◽  
Carlos Jurado Rivas
Keyword(s):  
Conceptual Framework ◽  
Social Innovation ◽  
General Scheme ◽  
Social Economy ◽  
Digital Technologies ◽  
Good Practices ◽  
The Social ◽  
Social Innovations ◽  
Global Movement ◽  
Global Activity

The fusion of the social economy with the digital economy, together with the essential need for social organizations to innovate in order to face challenges not satisfied by using traditional methods, led to what is known as digital social innovation: the use of digital technologies to allow or help to carry out social innovations. We are facing a developing field of study, in full evolution and with a high and recent level of global activity, which makes it a true global movement. This, together with the fact that DSI practices still lack unanimous and systematized criteria, calls for identifying what DSI is and what should be understood by it. Therefore, this chapter aims to configure and illustrate the conceptual framework of DSI, detail the barriers that are limiting its momentum, and formulate a general scheme of action for good practices in DSI.

scholarly journals Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Babak Abedin
Keyword(s):  
Artificial Intelligence ◽  
System Performance ◽  
Fuzzy Systems ◽  
Data Analytics ◽  
Design Methodology ◽  
Contingency Theory ◽  
Content Type ◽  
The Social ◽  
Opposing Effects ◽  
Practical Implications

PurposeResearch into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.Design/methodology/approachThe author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.FindingsThe author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).Research limitations/implicationsAs in other systematic literature review studies, the results are limited by the content of the selected papers.Practical implicationsThe findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.Originality/valueThis study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

Responsibility and Social Innovation in Tourism Companies in Mexico

2021 ◽  
pp. 56-71
Author(s):  
Maria del Carmen De la Luz Lanzagorta ◽  
Edith Sarai Lozada Sánchez ◽  
Jessica Abigail Cortés González ◽  
Concepción Nancy De Cristobal González
Keyword(s):  
Social Responsibility ◽  
Qualitative Methodology ◽  
Social Innovation ◽  
Central Question ◽  
Socially Responsible ◽  
Good Practices ◽  
Tourism Sector ◽  
The Social ◽  
The World ◽  
Corporate Social

All over the world, institutions and organizations that recognize the social responsibility of companies are identified. They work on social innovation, which is companies' capacities to influence problems, generating synergies between the various sectors of society. In this research, a qualitative methodology was applied to a sample of companies from Puebla (Mexico) and the region to identify innovative practices of corporate social responsibility in the tourism sector and related companies. The central question that guides this research is, through which strategies or actions are companies in the tourism sector socially responsible and innovative? Therefore, the purpose is to show the good practices of different companies in the tourism sector in Puebla (Mexico) as well as their areas of opportunity and therefore strategies to strengthen responsibility and social innovation in the sector.

scholarly journals Impact of social innovation on population change in Borsod-Abaúj-Zemplén County

2021 ◽  
Vol 17 (Special Issue Nr. 1) ◽  
pp. 41-48
Author(s):  
Zoltán Nagy ◽  
Géza Tóth ◽  
Krisztina Varga
Keyword(s):  
Regional Differences ◽  
Social Innovation ◽  
Population Change ◽  
Social Challenges ◽  
Material Resources ◽  
Innovation Potential ◽  
Catching Up ◽  
The Social ◽  
The Relationship

Technological and economic innovations cannot respond to all social challenges. Natural and material resources are becoming ever scarcer, so it is necessary to use investment assets, maximizing social and economic efficiency. It is a major task to address the backwardness originating from regional disparities and to create opportunities for catching up in peripheral regions. The study, based on the process-oriented model defined in our previous studies and the determination of the social innovation potential, tries to determine the relationship between social innovation potential, the spatial position of developmental image, and regional differences and population change in Borsod-Abaúj-Zemplén County.

scholarly journals The impact of Industry 4.0 on the processes of social innovation

2021 ◽  
Vol 17 (Special Issue Nr. 1) ◽  
pp. 3-10
Author(s):  
Sándor Karajz
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Social Innovation ◽  
Number Of Solutions ◽  
Digital Revolution ◽  
Good Practices ◽  
Technical Developments ◽  
The Impact ◽  
The Relationship ◽  
Opening Up

Our previous research into this topic has proved that technical developments significantly affect processes and effectiveness of social innovation. The current process of this development is called Industry 4.0. The first part of the study deals with industrial evolutions and the process of Industry 4.0 is interpreted. The second part of the study presents national and international examples and good practices in order to examine the relationship between digitalisation and social innovation. The results of Industry 4.0 reveal that there is an increasing number of solutions for social innovation that are based on digitalisation and automation. The current digital revolution is radically changing societies and opening up new opportunities for social innovation. Industry 4.0 results in social innovation solutions that use artificial intelligence to improve and optimise processes.

Neuro-Fuzzy Models and Applications

2019 ◽  
pp. 78-98
Author(s):  
Sushruta Mishra ◽  
Soumya Sahoo ◽  
Brojo Kishore Mishra
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Production Control ◽  
Accurate Solution ◽  
Execution Phase ◽  
Neuro Fuzzy ◽  
Neural Network Theory

The modern techniques of artificial intelligence have found application in almost all the fields of human knowledge. Among them, two important techniques of artificial intelligence, fuzzy systems (FS) and artificial neural networks (ANNs), have found many applications in various fields such as production, control systems, diagnostic, supervision, etc. They evolved and improved throughout the years to adapt arising needs and technological advancements. However, a great emphasis is given in the engineering field. The techniques of artificial intelligence based on fuzzy logic and neural networks are frequently applied together for solving engineering problems where the classic techniques do not supply an easy and accurate solution. Separately, each one of these techniques possesses advantages and disadvantages that, when mixed together, provide better results than the ones achieved with the use of each isolated technique. As ANNs and fuzzy systems have often been applied together, the concept of a fusion between them started to take shape. Neuro-fuzzy systems were born which utilize the advantages of both techniques. Such systems show two distinct ways of behavior. In a first phase, called learning phase, it behaves like neural networks that learn internal parameters off-line. Later, in the execution phase, it behaves like a fuzzy logic system. A neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. Neural networks and fuzzy systems can be combined to join its advantages and to cure its individual illness. Neural networks introduce its computational characteristics of learning in the fuzzy systems and receive from them the interpretation and clarity of systems representation. Thus, the disadvantages of the fuzzy systems are compensated by the capacities of the neural networks. These techniques are complementary, which justifies its use together. This chapter deals with an analysis of neuro-fuzzy systems. Benefits of these systems are studied with its limitations too. Comparative analyses of various categories of neuro-fuzzy systems are discussed in detail. Apart from these, real-time applications of such systems are also presented.

Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models

2021 ◽  
pp. 1-14
Author(s):  
Kevin Otieno Gogo ◽  
Lawrence Nderu ◽  
Makau Mutua
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Linear Regression ◽  
Predictive Models ◽  
Fuzzy Systems ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Training Data ◽  
Interval Type

Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.

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