Big data-driven large-scale group decision making under uncertainty

  in Special Issue   Posted on November 4, 2020

Information for the Special Issue

Submission Deadline: Fri 30 Apr 2021
Journal Impact Factor : 3.325
Journal Name : Applied Intelligence
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/10489/updates/18550380
Journal & Submission Website: https://www.springer.com/journal/10489

Special Issue Call for Papers:

Cutting-edge technologies, including big data, have a potential role in adopting in the society, healthcare organizations, and our daily lives. Big data would play an important role in terms of acting as a facilitator to achieve the desired information in the decision-making process. The concept of big data brings a complex and large volume of the data generated from many resources and clinical datasets, and it delivers crucial insights for the patient’s care. In addition, big data has the vast potential to enhance healthcare operations through data-driven decision-making under the fuzzy environment in the area of Industry 4.0. Big data analytics offer considerable advantages for the evaluation and assimilation of large amounts of complex healthcare data. Moreover, in the present data-driven of the digital economy, healthcare organizations attempt to attach the power of big data in order to make efficient and effective decisions in the organizations.

The growth of the world population and the increasing opportunities for effective treatments and overall better quality of life, putting increasing pressure on the healthcare systems. Therefore, healthcare keeps being one of the most significant economic and social challenges globally to seek new advanced solutions from technology and science. The healthcare industry 4.0 concept is being considered as a relevant topic within the Industry 4.0. For the first time, in 2011, the Industry 4.0 paradigm was coined, and originally it refers to manufacturing or production processes. In this regard, Industry 4.0 and healthcare services are complementary approaches, and their integration is becoming a need. Moreover, the advent of big data and the extensive use of electronic health records for the patients enabled the healthcare organizations to chase solutions to population health issues previously thought impossible. However, the use of big data for better decision making provides some healthcare challenges.

Group decision making under the fuzzy environment is measured as a decision situation in which a group of experts is requested to present their preference information to gain a common solution to a problem consisting of more than two alternatives or objects. In recent years, group decision making has been widely studied in different application areas, such as healthcare organizations. However, with the rapid development of society and the increasingly complex management, economic situation, and decision-making responsibilities are becoming increasingly difficult. Therefore, large-scale group decision making has become the focus of decision-making problems in healthcare Industry 4.0. Large-scale group decision making or complex group decision-making problems under fuzzy environment are very generally come upon in real life, particularly in the era of big data. Because of the growth of large-scale interaction among employers, users, and experts, the current literature emphasizes the role of large-scale group decision making in healthcare Industry 4.0 problems.

The fast-growing field of big data analytics has started to play a crucial part in evolving healthcare research and practices. It has provided tools to accumulate, analyze, manage, and adapt the large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently used to aid the process of disease exploration and care delivery. However, although the current literature indicated the large-scale group decision-making problems under different fuzzy sets play a critical role in the adaption of big data-driven, a few attention has been paid to using the large-scale group decision making in the areas of big data-driven for healthcare Industry 4.0. Therefore, it is vital for scholars interested in this topic to understand the status quo of studies being undertaken worldwide and to have the overall picture of the application of big data using large-scale group decision making methods to analyze, manage, and adapt the large volumes by current healthcare systems. However, the application of large-scale group decision making methods in the context of big data studies and healthcare Industry 4.0 is still rare, even though the volume of literature discussing big data is growing fast. Therefore, in this special issue, an attempt has been paid to present the state of the art of the application of large-scale group decision making methods for the adaption of big data-driven to analyze the healthcare Industry 4.0. Academics and experts are invited to submit original research and critical survey manuscripts that propose novel large-scale group decision-making methods based on big data on the following potential topics and applications, but are not limited to:

  • Big data for healthcare Industry 4.0 using LSGDM methods
  • Big data security intelligence for healthcare Industry 4.0 using fuzzy LSGDM methods
  • Analysis of big data using fuzzy LSGDM methods for healthcare Industry 4.0
  • Healthcare business model transformation and big data using fuzzy LSGDM methods
  • Healthcare circular economy using big data and LSGDM methods
  • Big data challenges and benefits in the build-up of healthcare Industry 4.0 using fuzzy LSGDM
  • Big data for managerial and policy implications of healthcare Industry 4.0 using LSGDM methods
  • Big data for healthcare closed-loop recycling practices using LSGDM methods
  • Adoption of big data analytics in healthcare Industry 4.0 using fuzzy LSGDM
  • Healthcare sustainable practices and big data using fuzzy LSGDM approaches

Important Dates 

Paper submission:     April 30, 2021

First notification:      June 30, 2021 

First revision:           August 30, 2021 

Second notification:  October 15, 2021 

Second revision:       November 30, 2021 

Final decision:          December 30, 2021 

Guest Editors

Dr. Abbas Mardani (lead Guest Editor)University of South Florida (USF), Tampa, Florida, United States.Email: abbasmardani@mail. usf.edu

Edmundas Kazimieras ZavadskasVilnius Gediminas Technical University, Vilnius, LithuaniaEmail: edmundas.zavadskas@vgtu.lt

Prof. Hamido FujitaIwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, Japan. Email: hfujita-799@acm.org

Prof. Dr. Mario KöppenKyuhsu Institute of Technology, JapanEmail: mkoeppen@ieee.org

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