A review on the applications of neuro-fuzzy systems in business

Rajab, Sharifa; Sharma, Vinod

doi:10.1007/s10462-016-9536-0

Cited by 49 publications

(28 citation statements)

References 139 publications

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“…Indeed, AI and Big Data have been supporting business activities and managers (e.g., finance, marketing, distribution, business planning, information systems, production, and operations) for a long time (Rajab and Sharma 2018), but the way and scope in which machines and humans can interact and collaborate in this field might be new. Wilson and Daugherty (2018), for example, describe five elements of business process improvement where AI and humans can collaborate: flexibility, speed, scale, decision-making, and personalization.…”

Section: Ai and Big Data Plus Humansmentioning

confidence: 99%

Artificial intelligence and big data in entrepreneurship: a new era has begun

Obschonka¹,

2019

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While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., "smart entrepreneurship")? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.

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Section: Ai and Big Data Plus Humansmentioning

confidence: 99%

Artificial intelligence and big data in entrepreneurship: a new era has begun

Obschonka¹,

2019

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“…Neuro-fuzzy systems naturally inherit strengths of artificial neural networks and fuzzy inference systems, and have been successfully applied to a variety of problems including forecasting. Rajab and Sharma [1] made a comprehensive review of the application of neuro-fuzzy systems in business.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Vlasenko

Vynokurova

et al. 2019

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Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

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“…For the most popular MCDM methods, such as DEMATEL, PROMETHEE, TOPSIS, AHP, ANP, VIKOR, COPRAS, ARAS, and WASPAS, fuzzy extensions have been proposed [2][3][4].Neuro-fuzzy systems have been proposed to cover more complicated formulations of decision-making problems. A comprehensive review, concerning numerous innovation aspects in neuro-fuzzy systems and the applications of these systems in various real-life issues, is presented in [5].Intensive research has been performed in order to extend their capabilities, concerning the more accurate modelling of the uncertain and vague initial information in decision-making problems. The various "fuzzy" approaches have been proposed to model different aspects of the information uncertainty.…”

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confidence: 99%

“…Neuro-fuzzy systems have been proposed to cover more complicated formulations of decision-making problems. A comprehensive review, concerning numerous innovation aspects in neuro-fuzzy systems and the applications of these systems in various real-life issues, is presented in [5].…”

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confidence: 99%

Civil Engineering and Symmetry

2019

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A topic of utmost importance in civil engineering is finding optimal solutions throughout the life cycle of buildings and infrastructural objects, including their design, manufacturing, use, and maintenance. Operational research, management science, and optimisation methods provide a consistent and applicable groundwork for engineering decision-making. These topics have received the interest of researchers, and, after a rigorous peer-review process, eight papers have been published in the current special issue. The articles in this issue demonstrate how solutions in civil engineering, which bring economic, social and environmental benefits, are obtained through a variety of methodologies and tools. Usually, decision-makers need to take into account not just a single criterion, but several different criteria and, therefore, multi-criteria decision-making (MCDM) approaches have been suggested for application in five of the published papers; the rest of the papers apply other research methods. The methods and application case studies are shortly described further in the editorial.Symmetry 2019, 11, 501 2 of 6 quite an important role in considering systems involving uncertainty in the information. Considerable research concerning decision-making has been performed by applying neural networks, fuzzy logic, and interval numbers. The success of these approaches relies on the fact that all these methods are derived through utilising the appropriate symmetries. Therefore, different fuzzy approaches have been proposed to model this type of information. For the most popular MCDM methods, such as DEMATEL, PROMETHEE, TOPSIS, AHP, ANP, VIKOR, COPRAS, ARAS, and WASPAS, fuzzy extensions have been proposed [2][3][4].Neuro-fuzzy systems have been proposed to cover more complicated formulations of decision-making problems. A comprehensive review, concerning numerous innovation aspects in neuro-fuzzy systems and the applications of these systems in various real-life issues, is presented in [5].Intensive research has been performed in order to extend their capabilities, concerning the more accurate modelling of the uncertain and vague initial information in decision-making problems. The various "fuzzy" approaches have been proposed to model different aspects of the information uncertainty. Neutrosophic sets, recently introduced by Smarandache, opened up new possibilities for representing the uncertain and inconsistent information encountered in decision-making formulations [6]. Fuzzy-rough sets have been applied in various fields, such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning [7].Additionally, fuzzy sets have been intensively applied in decision-making problems modelled within the aggregation operator framework. Various aggregation operators under different fuzzy sets are reviewed in [8].The methodological aspects of the decision-making problems in civil engineering, concerning the combination and integ...

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A review on the applications of neuro-fuzzy systems in business

Cited by 49 publications

References 139 publications

Artificial intelligence and big data in entrepreneurship: a new era has begun

Artificial intelligence and big data in entrepreneurship: a new era has begun

A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Civil Engineering and Symmetry

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