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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 61861

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Dr. Wojciech Sałabun

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Department of Artificial Intelligence method and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland
Interests: decision support system; decision making; MCDA; fuzzy logic; artificial intelligence
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Special Issue Information

Dear Colleagues,

Intelligent decision support systems are used to make better reliable decisions. However, it is a tough challenge because it requires the engagement of many opposing criteria. In addition to classical multi-criteria decision-making (MCDM) methods, artificial intelligence (AI) methods are also used. The modern approach involves using artificial intelligence methods for handling uncertainty data.

In both individual and group decision-making, attention must be paid to the complexity of the decision-making problem itself, and, thus, to the procedure complexity that is designed to select the best decisions from a set of candidate solutions or to establish a ranking. Moreover, new methods and modifications of existing ones are realized to utilize uncertain data to obtain reliable rankings effectively.

Therefore, the purpose of this Special Issue is to present the latest developments in artificial intelligence, MCDM methods, and new algorithms for decision support systems. Investigators in the field are invited to contribute their original, unpublished theoretical, and applied works. Both research and review papers are welcome.

Topics of interest include but are not limited to:

Artificial intelligence methods and applications:

Fuzzy sets
Interval arithmetic
Intuistionic fuzzy sets
Hesitant fuzzy sets
Machine learning
Deep learning

Decision support systems:

New MCDM methods
Selection problem
Sustainable development
Uncertain decision problems
Imprecise decision problems
PROMETHEE
AHP/ANP
TOPSIS
ELECTRE

Dr. Wojciech Sałabun
Guest Editor

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. Information is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (14 papers)

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Research

10 pages, 295 KiB

Open AccessArticle

Study of Transformed η^ζ Networks via Zagreb Connection Indices

by Muhammad Hussain, Atiq ur Rehman, Andrii Shekhovtsov, Muhammad Asif and Wojciech Sałabun

Information 2022, 13(4), 179; https://doi.org/10.3390/info13040179 - 31 Mar 2022

Viewed by 1745

Abstract

A graph is a tool for designing a system’s required interconnection network. The topology of such networks determines their compatibility. For the first time, in this work we construct subdivided

η^{ζ}

network

S (η^{ζ} Γ)

and discussed their topology. [...] Read more.

A graph is a tool for designing a system’s required interconnection network. The topology of such networks determines their compatibility. For the first time, in this work we construct subdivided

η^{ζ}

network

S (η^{ζ} Γ)

and discussed their topology. In graph theory, there are a variety of invariants to study the topology of a network, but topological indices are designed in such a way that these may transform the graph into a numeric value. In this work, we study

S (η^{ζ} Γ)

via Zagreb connection indices. Due to their predictive potential for enthalpy, entropy, and acentric factor, these indices gain value in the field of chemical graph theory in a very short time.

η^{ζ} Γ

formed by

ζ

time repeated process which consists

η^{ζ}

copies of graph

Γ

along with

(\binom{η}{2}) | V (Γ) | ζ η^{ζ - 1}

edges which used to join these

η^{ζ}

copies of

Γ

. The free hand to choose the initial graph

Γ

for desired network

S (η^{ζ} Γ)

and its relation with chemical networks along with the repute of Zagreb connection indices enhance the worth of this study. These computations are theoretically innovative and aid topological characterization of

S (η^{ζ} Γ)

. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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18 pages, 2774 KiB

Open AccessArticle

A Brief Analysis of Key Machine Learning Methods for Predicting Medicare Payments Related to Physical Therapy Practices in the United States

by Shrirang A. Kulkarni, Jodh S. Pannu, Andriy V. Koval, Gabriel J. Merrin, Varadraj P. Gurupur, Ayan Nasir, Christian King and Thomas T. H. Wan

Information 2021, 12(2), 57; https://doi.org/10.3390/info12020057 - 27 Jan 2021

Cited by 6 | Viewed by 2671

Abstract

Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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16 pages, 3092 KiB

Open AccessArticle

A Web-Based Approach for Visualizing Interactive Decision Maps

by Marco Marto, Vladimir A. Bushenkov, Keith M. Reynolds, José G. Borges and Susete Marques

Information 2021, 12(1), 9; https://doi.org/10.3390/info12010009 - 24 Dec 2020

Cited by 2 | Viewed by 2384

Abstract

This research expands the applicability of the Feasible Goals (FGoal) Pareto frontier multiple criteria method to display the Edgeworth–Pareto hull using interactive decision maps (IDMs). Emphasis is placed upon the development of a communication architecture to display the Pareto frontiers, which includes a client device, a web server, and a dedicated computation server implemented with sockets. A standalone application on the latter processes client-server requests and responses to display updated information on the client. Specifically, the dedicated computation server is responsible for calculating the information needed to generate the Edgeworth–Pareto hull. This is delivered to the web server to generate the IDM to be displayed on the client device. The key innovation of this work is a tool that is developed to aid decision-makers with a network-based computational architecture that includes a computational server constantly in communication with a web server for fast responses to client requests to represent IDMs. Results show that this innovation avoids time-consuming communication, and this approach to represent IDMs on the web facilitates collaboration among decision-makers because they can analyze several complex problems in different browser windows and decide which problem and solution better correspond to their aims. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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14 pages, 2387 KiB

Open AccessArticle

Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach

by Giorgio Guariso and Matteo Sangiorgio

Information 2020, 11(12), 587; https://doi.org/10.3390/info11120587 - 18 Dec 2020

Cited by 17 | Viewed by 4907

Abstract

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They apply the evolution mechanism of a natural population to a “numerical” population of solutions to optimize a fitness function. GA implementations must find a compromise between the breath of the search (to avoid being trapped into local minima) and its depth (to prevent a rough approximation of the optimal solution). Most algorithms use “elitism”, which allows preserving some of the current best solutions in the successive generations. If the initial population is randomly selected, as in many GA packages, the elite may concentrate in a limited part of the Pareto frontier preventing its complete spanning. A full view of the frontier is possible if one, first, solves the single-objective problems that correspond to the extremes of the Pareto boundary, and then uses such solutions as elite members of the initial population. The paper compares this approach with more conventional initializations by using some classical tests with a variable number of objectives and known analytical solutions. Then we show the results of the proposed algorithm in the optimization of a real-world system, contrasting its performances with those of standard packages. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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Graphical abstract

24 pages, 1161 KiB

Open AccessArticle

SyrAgri: A Recommender System for Agriculture in Mali

by Jacqueline Konaté, Amadou G. Diarra, Seydina O. Diarra and Aminata Diallo

Information 2020, 11(12), 561; https://doi.org/10.3390/info11120561 - 30 Nov 2020

Cited by 9 | Viewed by 3368

Abstract

This paper focuses on recommender system for agriculture in Mali called SyrAgri. The goal is to guide and improve the quality-of-experience of farmers by offering them good farming practices according to their needs. Two types of recommendations are essentially taken into account: the recommendation of crops and the recommendation of farming practices based on some predefined criteria which are: yield, life cycle of the crop, type of soil, growing season, etc. SyrAgri also informs farmers about crop rotation and the similarity between different types of crops based on the following parameters: crop families, growing seasons and appropriate soil types. For the development of this system a hybrid recommendation approach was used: demographic, semantic and collaborative methods. Each method is adapted to a specific stage of a user’s visit to the system. The demographic approach is first activated in order to offer recommendations to new users of the system, which resolves the concept of cold start (immediate inclusion of a new item or a new user in the system). The semantic approach is then activated to recommend to the user items (crops, agricultural practices) semantically close to those (s)he has appreciated. Finally, the collaborative approach is used to recommend items that similar users have liked. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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22 pages, 2933 KiB

Open AccessArticle

The Limitations of Decision-Making

by Paul Walton

Information 2020, 11(12), 559; https://doi.org/10.3390/info11120559 - 29 Nov 2020

Cited by 2 | Viewed by 4386

Abstract

In a world faced with technological, health and environmental change and uncertainty, decision-making is challenging. In addition, decision-making itself is becoming a collaborative activity between people and artificial intelligence. This paper analyses decision-making as a form of information processing, using the ideas of information evolution. Information evolution studies the effect of selection pressures and change on information processing and the consequent limitations of that processing. The analysis identifies underlying information evolution factors that affect the quality of information used throughout decision-making and, hence, affect the quality of decisions. These factors imply a set of challenges in which the pressures that drive useful trade-offs in a static environment also hinder decision-making of the required quality in times of change. The analysis indicates the information evolution characteristics of a good decision-making approach and establishes the theoretical basis for tools to demonstrate the information evolution limitations of decision-making. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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13 pages, 850 KiB

Open AccessArticle

Factors Affecting Decision-Making Processes in Virtual Teams in the UAE

by Vida Davidaviciene, Khaled Al Majzoub and Ieva Meidute-Kavaliauskiene

Information 2020, 11(10), 490; https://doi.org/10.3390/info11100490 - 21 Oct 2020

Cited by 7 | Viewed by 5750

Abstract

Organizational reliance on virtual teams (VTs) is increasing tremendously due to the significant benefits they offer, such as efficiently reaching objectives and increasing organizational performance. However, VTs face a lot of challenges that, if overlooked, will prevent them from yielding the required benefits. One of the major issues that hinders the effectiveness of VTs is the decision-making process. There is a lack of scientific research that attempts to understand the factors affecting decision making processes in VTs. Studies in this area have only been done in the United States and Europe. However, such research has not been conducted in the Middle East, where specific scientific solutions are still required to improve the performance of VTs. Therefore, this study is conducted in the Middle East, namely in the United Arab Emirates, to gain scientific knowledge on this region’s specificity. An online questionnaire (Google forms) was used to obtain the necessary data. Hypotheses were developed to test the influence of ICT (Information and communications technologies), language, information sharing, and trust on the decision-making processes, and the effect of decision making on team performance. Structural equational model (SEM) methodology was used to test our proposed model. The results showed that factors such as trust, ICT, and information sharing have a direct effect on decision-making processes, while language has no effect on decision making, and decision-making processes have a direct effect on the performance of the VTs. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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16 pages, 4659 KiB

Open AccessArticle

Traffic Sign Detection Method Based on Improved SSD

by Shuai You, Qiang Bi, Yimu Ji, Shangdong Liu, Yujian Feng and Fei Wu

Information 2020, 11(10), 475; https://doi.org/10.3390/info11100475 - 9 Oct 2020

Cited by 19 | Viewed by 4317

Abstract

Due to changes in illumination, adverse weather conditions, and interference from signs similar to real traffic signs, the false detection of traffic signs is possible. Nevertheless, in order to improve the detection effect of small targets, baseline SSD (single shot multibox detector) adopts a multi-scale feature detection method to improve the detection effect to some extent. The detection effect of small targets is improved, but the number of calculations needed for the baseline SSD network is large. To this end, we propose a lightweight SSD network algorithm. This method uses some 1 × 1 convolution kernels to replace some of the 3 × 3 convolution kernels in the baseline network and deletes some convolutional layers to reduce the calculation load of the baseline SSD network. Then the color detection algorithm based on the phase difference method and the connected component calculation are used to further filter the detection results, and finally, the data enhancement strategy based on the image appearance transformation is used to improve the balance of the dataset. The experimental results show that the proposed method is 3% more accurate than the baseline SSD network, and more importantly, the detection speed is also increased by 1.2 times. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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16 pages, 3871 KiB

Open AccessArticle

A Method of Personalized Driving Decision for Smart Car Based on Deep Reinforcement Learning

by Xinpeng Wang, Chaozhong Wu, Jie Xue and Zhijun Chen

Information 2020, 11(6), 295; https://doi.org/10.3390/info11060295 - 31 May 2020

Cited by 6 | Viewed by 3474

Abstract

To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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28 pages, 4692 KiB

Open AccessArticle

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

by Asma Baccouche, Begonya Garcia-Zapirain, Cristian Castillo Olea and Adel Elmaghraby

Information 2020, 11(4), 207; https://doi.org/10.3390/info11040207 - 14 Apr 2020

Cited by 80 | Viewed by 8539

Abstract

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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18 pages, 556 KiB

Open AccessArticle

A New Evaluation Methodology for Quality Goals Extended by D Number Theory and FAHP

by Hongming Mo

Information 2020, 11(4), 206; https://doi.org/10.3390/info11040206 - 13 Apr 2020

Cited by 5 | Viewed by 2519

Abstract

Evaluation of quality goals is an important issue in process management, which essentially is a multi-attribute decision-making (MADM) problem. The process of assessment inevitably involves uncertain information. The two crucial points in an MADM problem are to obtain weight of attributes and to handle uncertain information. D number theory is a new mathematical tool to deal with uncertain information, which is an extension of evidence theory. The fuzzy analytic hierarchy process (FAHP) provides a hierarchical way to model MADM problems, and the comparison analysis among attributes is applied to obtain the weight of attributes. FAHP uses a triangle fuzzy number rather than a crisp number to represent the evaluation information, which fully considers the hesitation to give a evaluation. Inspired by the features of D number theory and FAHP, a D-FAHP method is proposed to evaluate quality goals in this paper. Within the proposed method, FAHP is used to obtain the weight of each attribute, and the integration property of D number theory is carried out to fuse information. A numerical example is presented to demonstrate the effectiveness of the proposed method. Some necessary discussions are provided to illustrate the advantages of the proposed method. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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23 pages, 485 KiB

Open AccessArticle

Linguistic Pythagorean Einstein Operators and Their Application to Decision Making

by Yuan Rong, Zheng Pei and Yi Liu

Information 2020, 11(1), 46; https://doi.org/10.3390/info11010046 - 16 Jan 2020

Cited by 19 | Viewed by 2687

Abstract

Linguistic Pythagorean fuzzy (LPF) set is an efficacious technique to comprehensively represent uncertain assessment information by combining the Pythagorean fuzzy numbers and linguistic variables. In this paper, we define several novel essential operations of LPF numbers based upon Einstein operations and discuss several relations between these operations. For solving the LPF numbers fusion problem, several LPF aggregation operators, including LPF Einstein weighted averaging (LPFEWA) operator, LPF Einstein weighted geometric (LPFEWG) operator and LPF Einstein hybrid operator, are propounded; the prominent characteristics of these operators are investigated as well. Furthermore, a multi-attribute group decision making (MAGDM) approach is presented on the basis of the developed operators under an LPF environment. Ultimately, two application cases are utilized to demonstrate the practicality and feasibility of the developed decision approach and the comparison analysis is provided to manifest the merits of it. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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24 pages, 5883 KiB

Open AccessArticle

Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

by Meng-Leong How, Sin-Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor and Eunice Mei Ping Say

Information 2020, 11(1), 39; https://doi.org/10.3390/info11010039 - 11 Jan 2020

Cited by 29 | Viewed by 7274

Abstract

Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic reasoning approach can also be used as cognitive scaffolding to educe AI-Thinking in the analysts to ask more questions and provide decision making support to inform policy making in sustainable development. This paper uses the 2018 Environmental Performance Index (EPI) data from 180 countries which includes performance indicators covering environmental health and ecosystem vitality. AI-based predictive modeling techniques are applied on 2018 EPI data to reveal the hidden tensions between the two fundamental dimensions of sustainable development: (1) environmental health; which improves with economic growth and increasing affluence; and (2) ecosystem vitality, which worsens due to industrialization and urbanization. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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27 pages, 995 KiB

Open AccessArticle

Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Multi-Attribute Group Decision Making

by Peide Liu, Tahir Mahmood and Zeeshan Ali

Information 2020, 11(1), 5; https://doi.org/10.3390/info11010005 - 20 Dec 2019

Cited by 116 | Viewed by 4572

Abstract

In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and they are superior to the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the real part (similarly for imaginary part) of complex-valued membership degree and the qth power of the real part (similarly for imaginary part) of complex-valued non‐membership degree is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we develop the score function, accuracy function and comparison method for two Cq-ROFNs. Based on Cq-ROFSs, some new aggregation operators are called complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) and complex q-rung orthopair fuzzy weighted geometric (Cq-ROFWG) operators are investigated, and their properties are described. Further, based on proposed operators, we present a new method to deal with the multi‐attribute group decision making (MAGDM) problems under the environment of fuzzy set theory. Finally, we use some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)

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