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Applied Sciences
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Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI)
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Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 10488

Special Issue Editors

Dr. Rahman Ali

E-Mail Website
Guest Editor
Quaid-e-Azam College of Commerce, University of Peshawar, Peshawar 25120, Pakistan
Interests: artificial intelligence; intelligent decision making; data mining; machine learning; reasoning and inference; recommender systems and natural language processing
Dr. Asad Masood Khattak

E-Mail Website
Guest Editor
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
Interests: semantic web; data mining; context-aware computing; secure computing; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Farkhund Iqbal

E-Mail Website
Guest Editor
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
Interests: information security; digital forensics; deep learning; data science; privacy and trust

Special Issue Information

Dear Colleagues,

Organizations face problems during the process of complex decision making in multifaceted situations where multiple criteria and factors are involved. Real-world decision-support systems require consideration and analysis on the basis of multiple criteria which, in turn, affect the final decisions. Researchers concerned with the design and development of intelligent decision-making systems hunt for innovative scientific techniques, tools and models to improve the quality of the anticipated decisions. 

To achieve this goal of improved decision making, multi-criteria decision making (MCDM) and artificial intelligence (AI) techniques have recently been extensively practiced by researchers. As a result, significant improvements have been observed in decisions for a wide range of real-world complex problems. The integration of MCDM and AI offers new competencies to the configuration of complex decision making in different environments (e.g., static and distributed). These comprise the management of large datasets, the construction and modelling of innovative decision models, and the development of effective computational optimization algorithms for problem solving.

This Special Issue aims to solicit high-quality original research and review articles that cover novel, cutting-edge technologies and methods concerned with the scientific design, development and implementation of decision-support systems on the basis of MCDM and AI.

Potential topics include:

  • Intelligent decision-support technologies;
  • Data mining models for decision making;
  • Evidential reasoning;
  • Machine learning and deep learning models for decision making;
  • Evolutionary multiobjective optimization;
  • Fuzzy modelling;
  • Computational intelligence in MDCM;
  • Multi-criteria models for intelligent decision support system (IDSS) assessment;
  • Multi-attribute decision support;
  • Rule-based approach to multicriteria decision making;
  • Application of evidence theory in MCDM;
  • Multiobjective optimization problems;
  • Fuzzy multiobjective optimization;
  • Applications of the mentioned techniques across a wide range of areas, including, business, healthcare, education, industry, research, management, engineering, etc.

Dr. Rahman Ali
Dr. Asad Masood Khattak
Dr. Farkhund Iqbal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • multi-criteria decision making
  • intelligent decision making
  • intelligent decision-support systems
  • machine learning
  • data mining
  • deep learning

Published Papers (5 papers)

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Research

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17 pages, 1516 KiB  
Article
A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best–Worst Method
by Qazi Shoeb Ahmad, Mohammad Faisal Khan and Naeem Ahmad
Appl. Sci. 2023, 13(12), 6882; https://doi.org/10.3390/app13126882 - 06 Jun 2023
Cited by 1 | Viewed by 1152
Abstract
Multicriteria decision-making (MCDM) techniques have successfully been used to address a wide range of real-world decision-making issues. The best–worst method (BWM) is one of the several deterministic MCDM approaches. A recently proposed method called the multichoice best–worst method (MCBWM) takes into account several [...] Read more.
Multicriteria decision-making (MCDM) techniques have successfully been used to address a wide range of real-world decision-making issues. The best–worst method (BWM) is one of the several deterministic MCDM approaches. A recently proposed method called the multichoice best–worst method (MCBWM) takes into account several linguistic terms for pairwise comparisons of relative preferences among the criteria. It has been shown that the MCBWM approach has advantages over BWM: it reduces the calculation and determines optimal weight values by providing the choices for the optimal solution. This paper proposes a unique method for group decision-making based on MCBWM. We extended the MCBWM to solve group decision-making problems. A novel solution approach was developed and validated for multiple problems. Two examples and one case study were solved using the proposed approach to demonstrate the validity and application of the proposed method. The results were further compared with existing models to validate the proposed approach. We found that the obtained ranking order for all problems is the same and that the proposed model has a higher consistency ratio than the existing approaches. This method can be extended to other mathematical programming models for collective decision making in uncertain situations. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI))
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14 pages, 1159 KiB  
Article
Enhancing Utility in Anonymized Data against the Adversary’s Background Knowledge
by Fatemeh Amiri, Razaullah Khan, Adeel Anjum, Madiha Haider Syed and Semeen Rehman
Appl. Sci. 2023, 13(7), 4091; https://doi.org/10.3390/app13074091 - 23 Mar 2023
Cited by 1 | Viewed by 997
Abstract
Recent studies have shown that data are some of the most valuable resources for making government policies and business decisions in different organizations. In privacy preserving, the challenging task is to keep an individual’s data protected and private, and at the same time [...] Read more.
Recent studies have shown that data are some of the most valuable resources for making government policies and business decisions in different organizations. In privacy preserving, the challenging task is to keep an individual’s data protected and private, and at the same time the modified data must have sufficient accuracy for answering data mining queries. However, it is difficult to implement sufficient privacy where re-identification of a record is claimed to be impossible because the adversary has background knowledge from different sources. The k-anonymity model is prone to attribute disclosure, while the t-closeness model does not prevent identity disclosure. Moreover, both models do not consider background knowledge attacks. This paper proposes an anonymization algorithm called the utility-based hierarchical algorithm (UHRA) for producing k-anonymous t-closed data that can prevent background knowledge attacks. The proposed framework satisfies the privacy requirements using a hierarchical approach. Finally, to enhance utility of the anonymized data, records are moved between different anonymized groups, while the requirements of the privacy model are not violated. Our experiments indicate that our proposed algorithm outperforms its counterparts in terms of data utility and privacy. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI))
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20 pages, 2537 KiB  
Article
Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining
by Wajid Ali, Wanli Zuo, Ying Wang and Rahman Ali
Appl. Sci. 2023, 13(5), 3047; https://doi.org/10.3390/app13053047 - 27 Feb 2023
Viewed by 1162
Abstract
In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as [...] Read more.
In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN’s dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI))
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Review

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31 pages, 3119 KiB  
Review
Intelligent Decision Support Systems—An Analysis of Machine Learning and Multicriteria Decision-Making Methods
by Rahman Ali, Anwar Hussain, Shah Nazir, Sulaiman Khan and Habib Ullah Khan
Appl. Sci. 2023, 13(22), 12426; https://doi.org/10.3390/app132212426 - 17 Nov 2023
Cited by 1 | Viewed by 1997
Abstract
Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in [...] Read more.
Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI))
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27 pages, 7131 KiB  
Review
Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
by Qiuyan Xiang, Lingling Zi, Xin Cong and Yan Wang
Appl. Sci. 2023, 13(11), 6515; https://doi.org/10.3390/app13116515 - 26 May 2023
Cited by 4 | Viewed by 3036
Abstract
With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision [...] Read more.
With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI))
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