Article
Computer Science, Artificial Intelligence
Muhammad Akram, Sundas Shahzadi, Rabia Bibi, Gustavo Santos-Garcia
Summary: The primary objective of this research article is to present two novel tactical approaches, 2-tuple linguistic Fermatean fuzzy TOPSIS (2TLFF-TOPSIS) and 2-tuple linguistic Fermatean fuzzy ELECTRE I (2TLFF-ELECTRE I), for multi-attribute group decision-making based on 2-tuple linguistic Fermatean fuzzy data. The proposed algorithm exploits the benefits of novel 2-tuple linguistic Fermatean averaging operator to combine the insightful viewpoints of decision-making experts. These methods demonstrate their practicality and application through numerical examples and comparative analysis.
Article
Computer Science, Artificial Intelligence
Jia-Wen Wang
Summary: This study introduces a weighted 2-tuple fuzzy linguistic representation model to simplify computational complexity in analyzing Taiwan 50 ETF stocks. Feature selection, integration of the 2-tuple linguistic representation model, and OWA information fusion technology were used to identify the most stable company among the stocks.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Mathematics, Applied
Muhammad Akram, Uzma Noreen, Mohammed M. Ali Al-Shamiri, Dragan Pamucar
Summary: This paper explores the strategic application of TOPSIS and ELECTRE-I methods in the 2-tuple linguistic m-polar fuzzy sets (2TLmFSs) context. The 2TLmF-TOPSIS method ranks alternatives using a relative closeness index, while the 2TLmF ELECTRE-I method selects the best alternative through an outranking decision graph. The structure of the proposed techniques is illustrated using a system flow diagram. Finally, two case studies demonstrate the correctness, transparency, and effectiveness of the proposed methods.
Article
Engineering, Industrial
Xiaobing Li, Zhen He
Summary: Current hospital service quality evaluation faces challenges from evaluators' subjective perception and choice of evaluation methods. A new evaluation framework using 2-tuple Borda method is introduced to reduce uncertainty and diversity, and provide more consensus evaluation results, with a detailed case study conducted to demonstrate the approach.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Alvaro Labella, Bapi Dutta, Luis Martinez
Summary: Multi-criteria group decision making (MCGDM) involves evaluating alternatives over several criteria, which has given rise to large-scale group decision making (LS-GDM) problems with challenges such as group formation and opinion polarization. Using linguistic variables and computing with words (CW) processes has been successful in addressing uncertainty in real world MCGDM problems. The Best-Worst method (BWM) reduces pairwise comparisons and inconsistency in decision makers' preferences, while an extended 2-tuple BWM aims to provide accurate and interpretable results in MCGDM scenarios, including polarization opinions and sub-group identification.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Sumera Naz, Muhammad Akram, Muhammad Muneeb ul Hassan, Areej Fatima
Summary: Vietnam aims to become the world's new production hub and this will lead to an increase in energy consumption. As fossil fuels are harmful to the environment and depleting quickly, Vietnam needs to strategically choose the best renewable energy resource. The 2TLq-ROFS is a novel development in fuzzy set theory that presents a decision-making method for selecting the most appropriate alternative.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Artificial Intelligence
Ziwei Shu, Ramon Alberto Carrasco Gonzalez, Javier Portela Garcia-Miguel, Manuel Sanchez-Montanes
Summary: With the growth of online tourism, it is important to analyze customer reviews to improve a hotel's online reputation. This paper proposes a new approach using the OWA operator, 2-tuple linguistic model, and K-means clustering to classify hotels based on online reviews.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Gabriel Marin Diaz, Ramon Alberto Carrasco, Daniel Gomez
Summary: The paper proposes a working model based on the value of customer interaction with the contact center to help increase the brand's value and perception. The model uses factors such as recency, frequency, importance and duration to rank and group customers, thus establishing personalized communication strategies. The model has been validated through a business case applied to the telecom sector.
Article
Mathematics
Ziwei Shu, Ramon Alberto Carrasco, Javier Portela Garcia-Miguel, Manuel Sanchez-Montanes
Summary: In addition to comparing GDP, it is important to assess the quality of life for a holistic perspective on economic development. However, quantifying the subjective term, quality of life index (QOLI), can be challenging. This paper proposes using linguistic quantifiers and the 2-tuple linguistic model to construct various scenarios of QOLI and applies it to estimate QOLIs in 85 countries using the Numbeo database. The results show that the proposed model enhances the linguistic interpretability of QOLI and yields different QOLIs based on diverse country contexts.
Article
Automation & Control Systems
Chenliang Li, Xiaobing Yu
Summary: Dempster-Shafer evidence theory is a multi-source technology that utilizes information from different sources to solve uncertain problems. However, conflicting evidence can lead to counter-intuitive results and confuse decision-makers. In this paper, a consensus reaching model is proposed to overcome the influence of conflicting evidence. The 2-tuple linguistic representation method is used to model and manage vague decision information. A case study on the selection of plant protection machine suppliers is conducted to verify the effectiveness of the proposed method, revealing a strong correlation between consistency value and conflicting value of evidence.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Musavarah Sarwar
Summary: The management of medical waste disposal is a challenging task in developing countries. This research proposes a novel mathematical model that integrates 2-tuple linguistic setting into rough approximations and cloud theory to handle uncertainty, randomness, and multi-granularity in the selection of health care waste management technologies.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Hongyue Diao, Ansheng Deng, Hui Cui, Xin Liu, Li Zou
Summary: This paper introduces a novel approach for solving fuzzy multi-criteria decision problem under linguistic information based on linguistic truth-valued lattice implication algebra (LTV-LIA). The approach includes concepts such as LLVPR, LLV2-tuple model, and multiple preferences implication operation, and is illustrated with new algorithms and numerical examples to demonstrate rationality and comprehensiveness in linguistic information processing.
FUZZY OPTIMIZATION AND DECISION MAKING
(2022)
Article
Mathematical & Computational Biology
Muhammad Akram, Ayesha Khan, Uzma Ahmad, Jose Carlos R. Alcantud, Mohammed M. Ali Al-Shamiri
Summary: This paper introduces a new decision-making framework model, called 2-tuple linguistic complex q-rung picture fuzzy sets (2TLCq-RPFSs), and proposes corresponding aggregation operators. This model helps better represent two-dimensional information and plays an important role in multi-attribute decision making.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Muhammad Qiyas, Muhammad Naeem, Lazim Abdullah, Muhammad Riaz, Neelam Khan
Summary: In this research, tools are provided to overcome information loss limitation in estimating results in the discrete initial expression domain. 2-tuples, consisting of a linguistic term and a numerical value calculated between [0.5,0.5), are used to express linguistic information. A novel linguistic multi-attribute group decision-making (MAGDM) approach with complex fractional orthotriple fuzzy 2-tuple linguistic (CFOF2TL) assessment details is developed.
Article
Computer Science, Artificial Intelligence
Imran Khan, Anjana Gupta, Aparna Mehra
Summary: This paper examines the use of an unbalanced 2-tuple linguistic term set in multiple-criteria group decision-making problems. A data envelopment analysis approach and a linear programming model are used to evaluate the weights of alternative criteria. The non-rational factor of risk is modeled using the value function from the prospect theory. Cross-efficiency scores and entropy values are used to rank the alternatives.
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
Engineering, Electrical & Electronic
Amartya Mukherjee, Debashis De, Nilanjan Dey, Ruben Gonzalez Crespo, Enrique Herrera-Viedma
Summary: Internet of Things (IoT) application in disaster responses and management is a significant research domain. The introduction of consumer drones, flying ad-hoc networks, low latency 5G, and beyond 5G has greatly accelerated this research. This study proposes the implementation of the Consumer Internet of Drone Things (CIoDT) framework for emergency message transfer and stable network connectivity in disaster scenarios. The results show high message delivery probability, low latency, and suitability for mass production of light weight drone networks for disaster management.
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Enrique Herrera-Viedma
Summary: To manage consensus in opinion dynamics models (ODMs), it is crucial to remove bias from agents' interactions and consider their willingness. We propose a linguistic ODM based on Blockchain technology to build trust and consensus opinion. A Blockchain-enabled trust-building mechanism is used to improve agents' trust and guide them toward consensus opinion.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(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
Cristina Zuheros, Eugenio Martinez-Camara, Enrique Herrera-Viedma, Francisco Herrera
Summary: The wisdom of the crowd theory states that a nonexpert crowd makes smarter decisions than a reduced set of experts. Evaluations from social networks can enhance the quality of decision-making models. We propose a crowd decision-making model guided by sentiment analysis, which incorporates all the evaluation shades and tackles the lack of information using sparse representation. The results show that integrating the wisdom of the crowd and the different shades of the evaluations enhances the quality of the decision.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Yinfeng Du, Zhen-Song Chen, Jie Yang, Juan Antonio Morente-Molinera, Lu Zhang, Enrique Herrera-Viedma
Summary: This study explores how to develop a systematic framework for doctor selection in online health communities (OHCs) based on doctor basic information and online patient reviews. The study defines quantification method for doctor basic information and uses data analysis techniques to extract core attributes and evaluations from online patient reviews. Selection rules are made according to doctor influence and patient satisfaction to choose optimal and suboptimal doctors for rational or emotional patients.
Article
Mathematics
Yi Zhou, Chonglan Guo, Guo Wei, Enrique Herrera-Viedma
Summary: In this paper, sorted negotiation is introduced into consensus decision making to improve the speed and effectiveness of consensus. The authors construct negotiation models considering efficiency and time, and use an improved genetic algorithm to solve the optimal solution in the context of China's urban demolition negotiation. Assessment criteria for the reasonableness of the sorting sequence are determined by introducing an optimum set of influential individuals.
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Enrique Herrera-Viedma, Francisco Herrera
Summary: Behavioral decision theory modifies classic decision-making theories to make them more applicable in realistic scenarios. Regret theory, an important component of behavioral decision theories, has been widely used in theories and applications. In this study, a generalized three-way decision method is proposed based on regret theory for incomplete multiscale decision information systems. Experimental results show that the decision-making results of the proposed method maintain over 97% consistency in incomplete information systems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(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
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
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)
