Article
Computer Science, Artificial Intelligence
Ashraf Norouzi, Hossein Razavi Hajiagha
Summary: This study aims to extend the Best-Worst method using a combination of hesitant and interval type-2 fuzzy sets, providing a flexible way to depict experts' hesitant opinions in group decision-making. Numerical case studies demonstrate the feasibility and effectiveness of the proposed approach, which outperforms traditional methods in comparative analysis. Results indicate that the proposed method not only provides acceptable outcomes but also exceeds the performance of traditional methods.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Peide Liu, Mengjiao Shen, Fei Teng, Baoying Zhu, Lili Rong, Yushui Geng
Summary: The study proposed a multiple criteria group decision-making method based on DSET and TODIM under DHHFLTSs. By improving the cumulative functions and distance measure of DHHFLTSs, fusing information provided by a group of experts, and considering loss aversion behavior, the final ranking results were obtained.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
R. Krishankumar, Karthik Arun, Arun Kumar, Pratibha Rani, K. S. Ravichandran, Amir H. Gandomi
Summary: Green supplier selection (GSS) is a crucial issue in green supply chain management, with existing literature highlighting some challenges that researchers have not yet addressed. A two-stage decision framework was proposed to handle these challenges, incorporating a double-hierarchy linguistic model for handling complex expressions. The framework was evaluated through a case study and comparison with other methods, demonstrating its practicality, strengths, and weaknesses.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ruichen Zhang, Zeshui Xu, Xunjie Gou
Summary: The Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS) has been effectively applied to multi-criteria decision-making (MCDM) problems, however, determination of criteria weights and innovation in decision-making methods are still areas of interest. This paper extends the Best-Worst Method (BWM) and Dempster-Shafer evidence theory (DSET) to the DHHFL environment to address these issues, proposing the DHHFL-BWM-DSET method. By deriving criterion weights using the BWM and incorporating a DSET-based MCDM method, the proposed approach aims to provide more rational decision results by integrating information from multiple decision makers. This method offers a simplification of the calculation process and improved consistency in results, demonstrating breakthroughs and advantages in the selection of financial products.
APPLIED INTELLIGENCE
(2021)
Article
Automation & Control Systems
Quanyu Ding, Cheng Zhang, Ying-Ming Wang, Mark Goh
Summary: This study proposes an enhanced TODIM approach that solves the problem of uncertain ambiguity in decision-making by introducing hesitancy and bidirectional projection models. The approach incorporates attribute weights, decision maker weights, and hesitant fuzzy linguistic weighted operators to accurately capture decision makers' subjective characteristics.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jia-Wei Gong, Hu-Chen Liu, Xiao-Yue You, Linsen Yin
Summary: This article introduces a new integrated MCDM approach based on linguistic hesitant fuzzy sets and the TODIM method for evaluating and selecting the best e-learning website in order to solve the complex multi-criteria decision-making problem in network teaching. Empirical results demonstrate that the proposed method is more practical and effective in addressing the e-learning website selection problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Tongtong Zhou, Zhihua Chen, Xinguo Ming
Summary: This paper introduces a novel hesitant fuzzy linguistic hybrid cloud model to effectively handle the complex uncertainties in multicriteria decision making. By integrating hesitant fuzzy linguistic term sets and cloud models, this model can avoid information loss and distortion, providing more consistency and reliability compared to traditional methods. The proposed method also includes an improved approach for aggregating multiple linguistic concepts.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel Saz-Carranza
Summary: Global policy makers need to monitor global governance and utilize analytical tools such as global news dashboards to provide current information on global sentiment changes, particularly by identifying unexpected shifts in sentiment following major events. This paper introduces a methodology to evaluate global sentiment before, during, and after significant events, utilizing hesitant linguistic terms to represent sentiment in news articles and aggregating them into centralized sentiments for each period. The approach is able to detect changes in aggregated sentiment and consensus, providing a more sensitive model compared to traditional crisp aggregation methods for informing policy makers about public opinion and discourse.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shahzad Faizi, Wojciech Salabun, Shoaib Nawaz, Atiq ur Rehman, Jaroslaw Watrobski
Summary: This study proposes two new methods based on the Best-Worst method, namely the Linear Best-Worst method and the Euclidean Best-Worst method, for solving multi-criteria group decision-making problems, and analyzes the related properties of intuitionistic 2-tuple linguistic elements.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Guangquan Huang, Liming Xiao, Genbao Zhang
Summary: Design concept evaluation (DCE) is crucial in the early design stage of new product development as it determines the subsequent design and manufacturing activities. However, previous methods had limitations in handling multiple uncertainties in group decision-making, incomplete criteria weight calculations, and ignoring experts’ psychological behaviors. This study presents an integrated DCE model that utilizes interval-valued intuitionistic fuzzy rough clouds (IVIFRCs) to handle uncertainties, incorporates best-worst entropy (BWE) method for criteria weight calculation, and integrates the generalized TODIM method to consider experts’ psychological behaviors and rank design concepts. The results demonstrate the superiority of the developed model compared to existing approaches.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Han Qi, Li Weimin, Xu Qiling, Zhao Minrui, Huo Runze, Zhang Tao
Summary: The paper proposes a Lanchester equation that considers the cognitive domain to fit the development of intelligent wars. The importance of the cognitive domain and the evaluation of operational effectiveness are analyzed.
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Jiancheng Tu, Zhibin Wu, Witold Pedrycz
Summary: The best-worst method is a recently developed approach for pairwise comparisons in multi-criteria decision-making. It is widely used due to its effectiveness in reducing comparison time and maintaining consistency. However, existing prioritization methods for the best-worst method fail to consider indirect judgments and meet established criteria. This paper introduces two new prioritization methods, the approximate eigenvalue method and the logarithmic least squares method, and evaluates their performance using Monte Carlo simulation, concluding that the logarithmic least squares method is the best due to its simplicity, consideration of indirect judgments, and minimal violations.
INFORMATION SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Selvaraj Geetha, Samayan Narayanamoorthy, Joseph Varghese Kureethara, Dumitru Baleanu, Daekook Kang
Summary: The research introduces a novel decision making method called HPF-ELECTRE method, which extends the ELECTRE III method with HPF set for the plastic recycling problem. Plastic, being a non-biodegradable synthetic chemical, poses a serious threat to the environment and human life, highlighting the importance of finding suitable recycling methods. The HPF-ELECTRE III method, with its outranking based on concordance and discordance acceptability values, is proposed as an effective tool for addressing decision making problems in plastic recycling.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Mehdi Divsalar, Marzieh Ahmadi, Elnaz Ebrahimi, Alessio Ishizaka
Summary: A probabilistic hesitant fuzzy set is introduced as a generalization of the hesitant fuzzy set for handling uncertain information when there is no complete consensus among decision-makers. This paper presents a novel Choquet integral-based TODIM method and validates its effectiveness through a supplier selection problem in the dairy industry.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chenyang Song, Zeshui Xu, Jian Hou
Summary: The hesitant fuzzy psychological distance measure proposes a new similarity measure by considering the preference relationships between alternatives, providing more accurate guidance for decision-making.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Weiping Ding, Peide Liu, Witold Pedrycz
Summary: This article introduces a new three-way decision model based on probability theory, aiming to solve multi-attribute decision-making problems. By designing a new value function, it can better reflect the relative position of the object and avoid the issue of subjectivity. The effectiveness and stability of the method are demonstrated through verification and analysis.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Biology
Hongwei Du, Xinyue Zhang, Gang Song, Fangxun Bao, Yunfeng Zhang, Wei Wu, Peide Liu
Summary: This paper proposes a novel retinal blood vessel segmentation strategy, including three stages: vessel structure detection, vessel branch extraction, and broken vessel segment reconnection. Experimental results show that compared with current algorithms, our strategy effectively maintains the connectivity of retinal vascular tree structure.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Peide Liu, Yueyuan Li, Peng Wang
Summary: Reaching a consensus in large-scale group decision making is more challenging compared to group decision making due to the large number of decision makers involved. However, there is a lack of research on large-scale group decision making under social trust networks, and opinion dynamics are rarely utilized to analyze the interaction of decision makers. Therefore, this study proposes a multi-criteria large-scale group decision making consensus decision framework and a bounded confidence-based consensus optimization model. The proposed methods and models are further justified and demonstrated through a numerical example and simulation experiments.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Zhengmin Liu, Yawen Bi, Peide Liu
Summary: This study constructs a FMEA model with internal and external team members for medical waste management system and focuses on conflict elimination under social network to improve team harmony and enhance the reliability of FMEA.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Engineering, Civil
Yunfeng Zhang, Qun Jiang, Peide Liu, Shanshan Gao, Xiao Pan, Caiming Zhang
Summary: In this article, an underwater image enhancement framework based on transfer learning is proposed. The framework consists of a domain transformation module and an image enhancement module. The experimental results show that the presented method is superior to some advanced underwater image enhancement algorithms both qualitatively and quantitatively. Furthermore, ablation experiments and application tests are conducted to validate the effectiveness of the method.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xinxing Wu, Zhiyi Zhu, Chuan Chen, Guanrong Chen, Peide Liu
Summary: All intuitionistic fuzzy TOPSIS methods contain two key elements: the order structure and the distance/similarity measure. This paper proves that there is no score function that can strictly distinguish different intuitionistic fuzzy values and preserve the natural partial order. It also shows that classical similarity measures and intuitionistic fuzzy TOPSIS methods do not ensure monotonicity with linear orders. To overcome this limitation, a novel intuitionistic fuzzy TOPSIS method is proposed and its monotonicity with linear orders is mathematically proved.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guolin Tang, Xiaoyang Zhang, Baoying Zhu, Hamidreza Seiti, Francisco Chiclana, Peide Liu
Summary: This study presents an R-mathematical programming method for multiple attribute group decision-making problems with subjective bounded rationality. It proposes a novel scalar multiplication operation and defuzzification method for R-sets to be used in MAGDM. It also introduces a new technique based on prospect theory and R-sets to compute the individual overall prospect value of an alternative. The developed method estimates the decision makers' weights, attribute weights, positive ideal solution, and negative ideal solution using a novel R-mathematical programming model. The applicability, validity, and superiority of the method are verified through a practical instance and sensitive and comparative analyses.
INFORMATION FUSION
(2023)
Article
Mathematics, Applied
Peide Liu, Zeeshan Ali, Tahir Mahmood
Summary: The complex q-rung orthopair fuzzy (CQROF) set is proposed to represent complex uncertain information, and the improved Einstein operational laws are analyzed in this manuscript. Based on the improved laws, the complex q-rung orthopair fuzzy Einstein interaction weighted geometric (CQROFEIWG) operator, complex q-rung orthopair fuzzy Einstein interaction ordered weighted geometric (CQROFEIOWG) operator, and complex q-rung orthopair fuzzy Einstein interaction hybrid geometric (CQROFEIHG) operator are developed. The major results and properties such as idempotency, boundedness, and monotonicity are analyzed. Furthermore, a decision-making approach with complex q-rung orthopair fuzzy information is developed, demonstrating its superiority and feasibility through illustrated examples compared with existing approaches.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Peide Liu, Ying Li, Xiaohong Zhang, Witold Pedrycz
Summary: This article proposes a new multiattribute group decision-making method that considers the allocation of ignorance information, realization of group consensus, and aggregation of assessments. The method achieves adaptive group consensus through optimization modeling and the particle swarm optimization algorithm, and generates comprehensive alternative assessments using the evidential reasoning algorithm.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Hongxue Xu, Peide Liu, Ke Xu
Summary: This paper proposes a cross-efficiency evaluation method to handle the non-homogeneous DMUs in the parallel network system. The method considers the scenario wherein quantitative and qualitative data exist in the inputs and outputs simultaneously. It divides the NHDMUs into different mutually exclusive unit groups according to the subsystems and constructs interval cross-efficiency models to obtain the interval cross-efficiencies of NHDMUs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Peide Liu, Yueyuan Li, Peng Wang
Summary: With the development of social media, social networks have become an important platform for information exchange. This article presents a consensus decision framework for multiattribute group decision making based on the social trust network. By considering trust propagation and aggregation processes, consensus is achieved. Numerical examples and simulation experiments are used to demonstrate the applicability and superiority of these methods.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Peide Liu, Yiqiao Xu, Ying Li
Summary: Failure mode and effect analysis (FMEA) is a powerful tool for reliability management that has been widely applied in various fields. However, the interaction among experts, risk factors, and failure modes in the FMEA framework is often neglected. Therefore, this study proposes an improved FMEA method that comprehensively considers the interaction between each part. The effectiveness and superiority of the method are verified through a case study of automatic transmission (AMT) in new energy vehicles, enriching the theoretical research of FMEA and improving the reliability of AMT risk assessment.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Industrial
Ying Li, Peide Liu, Gang Li
Summary: This paper proposes an improved failure mode and effect analysis (FMEA) method based on risk attitude and asymmetric cost consensus, aiming to enhance the quality and efficiency of reliability management. The effectiveness of the proposed method is demonstrated through practical case studies and simulation experiments. The improved FMEA method can improve the flexibility and quality of risk management, while also saving enterprise costs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Management
Peide Liu, Hongxue Xu, Ke Xu
Summary: This study addresses two major problems in the existing research on the integrated model of slack-based measure (SBM) and SuperSBM models, and proposes a modified SuperSBM (MSuperSBM) model. The MSuperSBM model can differentiate decision-making units and determine their strongly efficient projections, thereby accurately evaluating efficiency scores without overestimation. Moreover, the model only includes necessary decision variables and constraints, reducing the computational complexity for large-scale performance evaluation.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Peng Wang, Yingxin Fu, Peide Liu, Baoying Zhu, Fubin Wang, Dragan Pamucar
Summary: This study explores the ecological governance of the Yellow River and proposes a multi-perspective evaluation method. By using BWM and IGR methods to calculate the weights of the indicators and combining multiple methods for comprehensive evaluation, policy recommendations for the ecological governance of the Yellow River Basin are obtained.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
