Data Driven Decision-Making Under Uncertainty (D3U), 2nd Edition

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 2389

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Department of Structural Analysis, Technical University of Berlin, 10623 Berlin, Germany
Interests: finite element analysis; nano materials; nano technology; materials science; modeling; mathematical modeling; experimentation; ansys; labview, transportatiom; civil engineering; structural engineering; mechanical engineering
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Guest Editor

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Guest Editor
Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Interests: fuzzy sets; soft sets; rough sets; decision making; artificial intelligence; pattern recognition; topology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Data-driven decision making under uncertainty (D3U) practically means extracting data and information, including from Big Data, to make decisions in a wide range of areas, from emergency response to healthcare to renewable energy. D3U is driven by the worldwide advancements afforded through Industry 4.0 as well as the power of hardware handling offered through Cloud and Fog computing and the like. In times of adversity, such as the COVID-19 pandemic, crucial issues such as predictive bed allocation and predicting the spread and the stages of the pandemic have made D3U an integral part of life. Furthermore, it is also becoming an ardent necessity to deal with challenges such as uncertainty, vagueness, and hesitation in order to make rational decisions, and D3U can aid us in regard to all these issues.  

All nations are also striving to achieve eco-environmental conservation, particularly ISO 14000 and ISO 14001, with industries focusing on sustenance and green habits, calling for the furthering of research in these areas.

In this Special Issue, we welcome research papers that are both conceptual and empirical, as well as qualitative and quantitative, and that focus on novel ways of exploring private and public data in order to derive innovative insights in various domains.

Dr. Dragan Marinkovic
Prof. Dr. Samarjit Kar 
Dr. Dragan Pamučar
Prof. Dr. Muhammad Riaz 
Guest Editors

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Keywords

  • decision aiding models
  • learning models
  • meta-heuristic algorithms
  • optimization models
  • predictive models
  • uncertain modeling
  • MCDM
  • computational intelligence and data science
  • machine learning algorithms
  • uncertain data-driven modelling
  • information retrieval systems

Published Papers (2 papers)

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Research

20 pages, 369 KiB  
Article
Analysis and Applications of Artificial Intelligence in Digital Education Based on Complex Fuzzy Clustering Algorithms
by Majed Albaity, Tahir Mahmood and Zeeshan Ali
Mathematics 2023, 11(14), 3184; https://doi.org/10.3390/math11143184 - 20 Jul 2023
Cited by 2 | Viewed by 698
Abstract
Digital education is very important and valuable because it is a subpart of artificial intelligence, which is used in many real-life problems. Digital education is the modern utilization of digital techniques and tools during online purchasing, teaching, research, and learning and is often [...] Read more.
Digital education is very important and valuable because it is a subpart of artificial intelligence, which is used in many real-life problems. Digital education is the modern utilization of digital techniques and tools during online purchasing, teaching, research, and learning and is often referred to as technology-enhanced learning or e-learning programs. Further, similarity measures (SM) and complex fuzzy (CF) logic are two different ideas that play a very valuable and dominant role in the environment of fuzzy decision theory. In this manuscript, we concentrate on utilizing different types of dice SM (D-SM) and generalized dice SM (GD-SM) in the environment of a CF set (CFS), called CF dice SM (CFD-SM), CF weighted dice SM (CFWD-SM), CF generalized dice SM (CFGD-SM), and CF weighted generalized dice SM (CFWGD-SM), and also derived associated outcomes. Furthermore, to evaluate or state the supremacy and effectiveness of the derived measures, we aim to evaluate the application of artificial intelligence in digital education under the consideration of derived measures for CF information and try to verify them with the help of several examples. Finally, with the help of examples, we illustrate the comparison between the presented and existing measures to show the supremacy and feasibility of the derived measures. Full article
(This article belongs to the Special Issue Data Driven Decision-Making Under Uncertainty (D3U), 2nd Edition)
12 pages, 309 KiB  
Article
The Relationship between Ordinary and Soft Algebras with an Application
by Zanyar A. Ameen, Tareq M. Al-shami, Radwan Abu-Gdairi and Abdelwaheb Mhemdi
Mathematics 2023, 11(9), 2035; https://doi.org/10.3390/math11092035 - 25 Apr 2023
Cited by 13 | Viewed by 852
Abstract
This work makes a contribution to the theory of soft sets. It studies the concepts of soft semi-algebras and soft algebras, along with some operations. Then, it examines the relations of soft algebras set to their ordinary (crisp) counterparts. Among other things, we [...] Read more.
This work makes a contribution to the theory of soft sets. It studies the concepts of soft semi-algebras and soft algebras, along with some operations. Then, it examines the relations of soft algebras set to their ordinary (crisp) counterparts. Among other things, we show that every algebra of soft sets induces a collection of ordinary algebras of sets. By using the formulas (in Theorem 7 and Corollary 1), we present a novel construction, allowing us to construct a soft algebra from a system of ordinary algebras of sets. Two examples are presented to show how these formulas can be used in practice. This approach is general enough to be applied to many other (soft) algebraic properties and shows that ordinary algebras contain instruments enabling us to construct soft algebras and to study their properties. As an application, we demonstrate how elements of the generated soft algebra can be used to describe the weather conditions of a region. Full article
(This article belongs to the Special Issue Data Driven Decision-Making Under Uncertainty (D3U), 2nd Edition)
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