Article
Computer Science, Information Systems
Wen Sheng Du
Summary: This paper investigates subtraction and division operations on intuitionistic fuzzy values/sets, derived from the Hamming distance, ensuring completeness of the operations. Fundamental properties of the modified arithmetic operations are extensively explored, and continuity and derivatives for intuitionistic fuzzy functions are introduced, providing groundwork for intuitionistic fuzzy differential calculus.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Ayush K. Varshney, Pranab K. Muhuri, Q. M. Danish Lohani
Summary: Hierarchical clustering using probabilistic intuitionistic fuzzy sets is proposed in this paper to handle data uncertainty. The novel clustering algorithm, termed as Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) Algorithm, utilizes the probabilistic Euclidean distance measure and achieves better cluster accuracies compared to existing counterparts. Experimental results on different datasets demonstrate the effectiveness of the PIFHC algorithm in improving clustering accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics, Applied
Wenjuan Ren, Zhanpeng Yang, Xipeng Li
Summary: The metric matrix theory is important for characterizing the geometric structure of a set in metric measure geometry. In this study, we defined metric information matrices (MIM) of intuitionistic fuzzy sets (IFS) using the metric matrix theory. We introduced the Gromov-Hausdorff metric to measure the distance between any two MIMs and constructed a metric information matrix distance measure for IFS. Additionally, we defined a homogenous metric information matrix distance to reduce information confusion caused by the disorder of MIM. The proposed distance measures were validated through numerical experiments in recognizing different patterns represented by IFS.
Article
Automation & Control Systems
Nursah Alkan, Cengiz Kahraman
Summary: Multi-criteria decision-making methods are useful tools for evaluating qualitative and quantitative factors simultaneously. However, current static methods may lead to ineffective decisions in dynamic and uncertain environments. This study proposes extensions of the CRITIC and DEVADA methods and a stronger multi-measurement system to address these issues. The feasibility and effectiveness of the proposed method are demonstrated through a waste disposal location selection problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xiaodi Liu, Yukun Sun, Harish Garg, Shitao Zhang
Summary: This paper defines the distance between intuitionistic fuzzy sets (IFSs) using line integral and redefines the distances between IFSs based on the analysis of geometric importance of line integral. The accuracy function is introduced to evaluate the accuracy of distance by applying the physical meaning of line integral. The numerical examples demonstrate the superiority of the proposed approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics, Applied
Shazia Kanwal, Asif Ali, Abdullah Al Mazrooei, Gustavo Santos-Garcia
Summary: A contemporary fuzzy technique is used to generalize established and recent findings. Fixed point procedures are advantageous and appealing mechanisms for researchers. The core objective of this research is to discover fuzzy fixed points of fuzzy mappings meeting Nadler's type contraction in complete fuzzy metric space and Ciric type contraction in complete metric spaces. Examples and applications are provided to support and highlight the findings, and preceding conclusions from relevant literature are given as corollaries. Our findings extend and combine numerous consequences in the significant literature.
Article
Mathematics
Mailing Zhao, Jun Ye
Summary: This study proposes an orthopair Z-number (OZN) set to depict the truth and falsehood values of fuzzy values and their reliability levels. The established operators and multiattribute decision-making model demonstrate the superiority of the proposed model in reliability and flexibility of decision results.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Lei Zhou, Kun Gao
Summary: Three novel pseudometrics on the set of intuitionistic fuzzy numbers are proposed in this paper, and a unified method to calculate the distances between different types of intuitionistic fuzzy sets based on these pseudometrics is introduced, along with corresponding proofs.
Article
Mathematics, Applied
Shazia Kanwal, Abdullah Al Mazrooei, Gustavo Santos-Garcia, Muhammad Gulzar
Summary: The main objective of this study is to investigate the existence of fuzzy fixed points for fuzzy mappings satisfying certain generalized contraction conditions of Nadler's type in complete b-metric spaces. Additionally, there are corollaries provided based on previous observations from relevant literature. Our study not only expands on these implications, but also addresses them in a significant amount of literature.
Article
Computer Science, Information Systems
Muhammad Sajjad Ali Khan, Fariha Anjum, Ikhtesham Ullah, Tapan Senapati, Sarbast Moslem
Summary: The notion of a complex hesitant fuzzy set (CHFS) is a valuable tool for dealing with complex information. This paper develops a priority degree and various distance measures for comparing complex hesitant fuzzy elements (HFEs). These measures are applied to medical diagnosis problems, and a multi-criteria decision making approach is also developed.
Article
Mathematics, Applied
Admi Nazra, Jenizon, Yudiantri Asdi, Zulvera
Summary: This article introduces a new hybrid model called hesitant intuitionistic fuzzy N-soft sets (HIFNSSs), which combines intuitionistic fuzzy N-soft sets and hesitant fuzzy N-soft sets. In addition, HIFNSSs are generalized to generalized hesitant intuitionistic fuzzy N-soft sets (GHIFNSSs), serving as a hybrid model between generalized hesitant intuitionistic fuzzy sets and N-soft sets.
Article
Computer Science, Artificial Intelligence
Zubair Ashraf, Mohd Shoaib Khan, Ashutosh Tiwari, Q. M. Danish Lohani
Summary: This paper introduces a new distance measure among intuitionistic fuzzy sets and applies it to multi-attribute decision-making methods, demonstrating its superiority. The proposed distance measure exhibits topological uniqueness and meets the characteristics of distance metrics and inclusive relations.
Article
Mathematics
Suara Onbasioglu, Banu Pazar Varol
Summary: The objective of this paper is to introduce the concept of intuitionistic fuzzy metric-like spaces, which extends the concepts of metric-like spaces, fuzzy metric spaces, and intuitionistic fuzzy metric spaces. The paper discusses convergence sequences, contractive mapping, and fixed-point theorems in intuitionistic fuzzy metric-like space. Examples and counterexamples are provided to validate the superiority of these results. The results of this paper significantly expand upon important findings in fuzzy metric-like spaces.
Article
Computer Science, Artificial Intelligence
Z. Shao, S. Kosari, Hossein Rashmanlou, F. Mofidnakhaei
Summary: This paper introduces the application of fuzzy sets theory and its related concepts. By using fuzzy logic to handle imprecise information, various real-world problems can be solved. This is particularly important for decision-making problems in fields such as medical diagnosis.
JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
(2023)
Article
Operations Research & Management Science
Gholamreza Hesamian, Mohamad Ghasem Akbari
Summary: The study extends a process control criterion based on intuitionistic fuzzy information in cases where the underlying population is normal. The proposed method was examined through a practical example to assess its effectiveness.
OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
James Izzard, Fabio Caraffini, Francisco Chiclana
Summary: This paper presents a software solution for designing general meal plans based on user's nutritional characteristics. Existing literature lacks a software solution in its most general form for this problem. The proposed software model is flexible and equipped with a simple optimization algorithm for the prototype system. Results from ten test problems suggest that the prototype system can address the general meal optimization problem.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Kai Xiong, Yucheng Dong, Zhaoxia Guo, Francisco Chiclana, Enrique Herrera-Viedma
Summary: This study aims to explore the ranking, classifications, and evolution mechanisms of research fronts in the Web of Science Essential Science Indicators (ESI) database using a multiattribute decision-making (MADM) and clustering method. The study reveals the performance differences between different countries and identifies the USA and China as the leading countries in most research fronts.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Engineering, Industrial
Yucheng Dong, Siqi Wu, Xiaoping Shi, Yao Li, Francisco Chiclana
Summary: This article investigates the clustering of failure modes based on their risks in FMEA practice. It proposes the additive N-clustering problem and explores the characteristics of exogenous clustering methods and endogenous clustering methods. It also introduces the Consensus-based ENdogenous Clustering Method (CENCM) as a solution for cases where accurate category thresholds are difficult to provide, and validates its effectiveness through comparisons and simulation experiments.
Article
Management
Tiantian Gai, Mingshuo Cao, Francisco Chiclana, Zhen Zhang, Yucheng Dong, Enrique Herrera-Viedma, Jian Wu
Summary: This paper proposes a consensus-trust driven framework for bidirectional interaction in social network large-group decision making. The framework includes defining interaction consensus threshold and interaction trust threshold, designing hybrid feedback strategies, and developing a minimum adjustment bidirectional feedback model considering cohesion. The effectiveness and applicability of the model are demonstrated through its application to a blockchain platform selection problem in supply chain.
GROUP DECISION AND NEGOTIATION
(2023)
Article
Computer Science, Artificial Intelligence
Qi Sun, Jian Wu, Francisco Chiclana, Sha Wang, Enrique Herrera-Viedma, Ronald R. Yager
Summary: In the problem of social network group decision making, this paper proposes a theoretical framework to prevent weight manipulation behavior by utilizing community detection and power index measurement. Through the combination of minimum adjustment and maximum entropy rules, the proposed method ensures the rationality and fairness of weight distribution, achieving consensus efficiently.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Feixia Ji, Jian Wu, Francisco Chiclana, Sha Wang, Hamido Fujita, Enrique Herrera-Viedma
Summary: This study proposes an overlapping community-driven feedback mechanism to improve consensus in social network group decision making. By guiding inconsistent subgroups to interact with each other and selecting personalized feedback parameters, this mechanism helps achieve higher levels of consensus.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Carlos Saenz-Royo, Francisco Chiclana, Enrique Herrera-Viedma
Summary: Expert judgments are crucial in decision theory, but the criterion for selecting experts remains an unresolved issue. This paper proposes a simulation methodology to assess the cost-benefit of decision support techniques and examines the impact of imposing consistency as a criterion for selecting experts. The findings suggest that the use of Saaty's consistency criterion can lead to a maximum 5% increase in the expected payoff of the Analytical Hierarchy Process (AHP) decision support technique.
INFORMATION FUSION
(2023)
Article
Automation & Control Systems
Mengqi Li, Yejun Xu, Xia Liu, Francisco Chiclana, Francisco Herrera
Summary: Every decision involves risks, and real-world risk issues are usually supervised by third parties. This article presents a conflict-eliminating model based on trust risk analysis to manage trust risks in social network group decision making (SNGDM) through third-party monitoring. The model measures trust risk types and index, and provides management policies and optimization methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Mathematics, Applied
Tamas Galli, Francisco Chiclana, Francois Siewe
Summary: This paper builds upon previous research on software product quality modelling and aims to provide a practical approach for software professionals. It explains how to interpret and utilize the established taxonomy of software product quality models, and how to determine the validity of statements based on these models. It also discusses tailoring and adjusting quality models to fit the specific needs of a project.
Article
Computer Science, Artificial Intelligence
Xinli You, Fujun Hou, Francisco Chiclana
Summary: Large-scale group decision-making problems have been a subject of interest for scholars. The distribution linguistic preference relation is a practical tool to describe decision makers' preferences. Opinion conflict and non-cooperative behaviors can occur among large-scale decision makers, making it necessary to establish a consensus reaching process.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Hengjie Zhang, Shenghua Liu, Yucheng Dong, Francisco Chiclana, Enrique Enrique Herrera-Viedma
Summary: This study presents a framework called minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) that considers experts' limited compromise and tolerance behaviors. It introduces two types of behaviors, limited compromise behavior and tolerance behavior, to the MCC-FMEA. The study develops and analyzes a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors, resulting in an interactive MCC-FMEA framework.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yejun Xu, Qianqian Wang, Francisco Chiclana, Enrique Herrera-Viedma
Summary: This article presents a new inconsistency identification and modification (IIM) method to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) while retaining the original preference values. The method also addresses the issue of inconsistent FPRs with missing values and provides an estimation approach for the missing preferences. Numerical examples, simulation experiments, and comparisons with existing methods demonstrate the correctness, effectiveness, and robustness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hengjie Zhang, Yucheng Dong, Francisco Chiclana
Summary: This study investigates conflict resolution and mediation in technology transfer disputes in new product collaborative development using a graph model and minimum cost. It analyzes the stakeholders, their options, feasible states, and decision makers' preferences using a graph model theory. It also designs an inverse graph model with minimum cost to specify which decision-makers' preferences lead to a desired solution, facilitating mediation or third-party influence in the conflict. The methodology is applied to a technology transfer dispute case study.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ningning Lu, Sifeng Liu, Junliang Du, Zhigeng Fang, Wenjie Dong, Liangyan Tao, Yingjie Yang
Summary: It is important to detect internal operating regularity in system developing with poor information. A grey relational analysis (GRA) method is proposed to identify the real relationship among multi factors, considering the changes of fluctuating sequences. The proposed GRA model can effectively identify the relationship and has small time-delay impact.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xinli You, Fujun Hou, Francisco Chiclana
Summary: This study aims to develop a reputation-based trust model for establishing trust relationships among experts in a group decision-making framework. The research achieves this by defining a trust credibility measure, designing direct trust feedback, and proposing a global reputation model.
INFORMATION FUSION
(2024)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
