Veracity handling and instance reduction in big data using interval type-2 fuzzy sets

Shukla, Amit K.; Yadav, Megha; Kumar, Sandeep; Muhuri, Pranab K.

doi:10.1016/j.engappai.2019.103315

Cited by 35 publications

(9 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Progress and developments in social networking, e-commerce websites, advanced mobile technologies, search engines and new digital technology have resulted in an increasing amount of BD, which is paving the way for firms to produce and capture data that have the three Vs characteristics, namely, variety, velocity and volume [39,40]. Variety describes the various data forms that cannot be managed through traditional analytical system due to its complication, and these include unstructured, semi-structured and structured data [4]; velocity describes the speed of data production and analysis in real-time [41]; lastly, volume describes the considerable data amount that the firms collect to sift through for information and data patterns, which hold important knowledge [20]. According to Mikalef et al [17], the concepts and meaning of big data, BDA capabilities (BDAC), and the BDA differ, although researchers use them synonymously in their writings.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

et al. 2022

View full text Add to dashboard Cite

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

et al. 2022

View full text Add to dashboard Cite

show abstract

“…IT2 fuzzy sets aim to minimize uncertainty of interval type-1 fuzzy sets. For this purpose, upper and lower trapezoidal membership functions are considered [37,38]. The details of calculations of IT2 fuzzy sets are given in the Appendix A. DEMATEL approach is considered to find the significance values of different alternatives.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Environmental Activities for Developing Public Health Investments and Policies: A Comparative Study with Structure Equation and Interval Type 2 Fuzzy Hybrid Models

Zhang

Xia

et al. 2020

IJERPH

View full text Add to dashboard Cite

The design of elements which exert pivotal effects on leisurely physical activity (LPA) in open space is an important part of urban development. However, little research has been done about the influence and discrepancies of those elements in different types of open space. To research these issues and to guide the design of urban open space, a survey from 8 open spaces (2 curtilage, 2 neighborhood squares (NS), 2 parks, and 2 campus) is conducted and a questionnaire is administered. Simultaneous analysis of several groups (SASG) of Structure equation model (SEM) is used, and the effects and discrepancies are acquired. In addition to this situation, interval type 2 (IT2) fuzzy hybrid decision making model is proposed in the second analysis. In this framework, IT2 fuzzy decision-making trial, evaluation laboratory (DEMATEL), and IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods are used. The results show that the influence relationships between elements and LPA did exist in four groups. Another important conclusion is that there were discrepancies of influence among different space groups. Physical environment (PE) has the greatest influence on LPA in the curtilage, whereas facilities exert the most effect in NS group. Additionally, amenities only have significant impact in parks and facilities only exercise remarkable influence on duration on campus. In addition to them, it is also identified that key design elements are presented for different types of space and that design strategy is provided through 4 specific examples.

show abstract

“…The principles of fuzzy logic hold great potential for applications in big data analytics. In Shukla et al (2020), researchers proposed the use of interval type-2 fuzzy sets for handling the veracity issue in big data to prevent the unusability of data. The problem of handling big data was addressed also in Zhang et al (2020b), where a quantitative model and method based on fuzzy DEcision MAking Trial and Evaluation Laboratory (DEMATEL) were proposed.…”

Section: Use Of Fuzzy Systems In the Context Of Industry 40mentioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

View full text Add to dashboard Cite

Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

show abstract

Veracity handling and instance reduction in big data using interval type-2 fuzzy sets

Cited by 35 publications

References 48 publications

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Analysis of Environmental Activities for Developing Public Health Investments and Policies: A Comparative Study with Structure Equation and Interval Type 2 Fuzzy Hybrid Models

Computational Intelligence in the Context of Industry 4.0

Contact Info

Product

Resources

About