Article
Mathematics
Mei Cai, Yuanyuan Hong
Summary: This paper proposes an asymmetric probabilistic linguistic cloud TOPSIS (ASPLC-TOPSIS) method for multi-attribute group decision making. By introducing cloud model and decision maker trust network, this method can effectively handle the fuzziness and randomness of decision maker preference, improving the accuracy and reliability of the decision results.
Article
Engineering, Multidisciplinary
Jirong Jiang, Min Ren, Jiqiang Wang
Summary: This paper proposes a method based on TOPSIS and weighted parameter to deal with multi-attribute decision-making problems with interval numbers. The method transforms the interval number matrix into exact number matrices and uses entropy weight to determine the weights. TOPSIS is then used to determine the order, and the average value of the ranking number is used to reflect the actual situation better. The proposed method is demonstrated to be feasible, practical, stable, and effective.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Mathematics, Applied
Juin-Han Chen, Hui-Chin Tang
Summary: This paper analyzes the properties of positively correlated weights related to a subset of finite criteria in a multi-attribute decision-making problem. It presents the exact constraints of these weights and introduces the concept of non-Archimedean number and bounded polyhedral-set. The paper also shows the increase in the number of extreme points in the bounded polyhedral-set as the number of criteria increases. It applies an efficient extreme-point method to obtain the optimal solution for pre-emptive-weights-goal-programming.
Article
Physics, Multidisciplinary
Xiaozhi Chen, Ligeng Zou
Summary: This paper combines three-way decision with multi-attribute decision-making, proposing new ideal relation models based on three-way decision and developing new methods to calculate state sets to reduce errors caused by decision-makers' subjectivity. The rationality and feasibility of the proposed models are verified through a concrete example, while the consistency and superiority of the methods are illustrated through comparative analysis and experiment analysis.
Article
Computer Science, Artificial Intelligence
Petra Groselj
Summary: To solve complex real-world problems, it is important to involve decision makers with diverse knowledge and experience. This paper proposes a new approach, DProj-DEMATEL, to determine objective weights for decision makers based on their agreement, using a projection method. Validation tests on three examples from the literature show that DProj-DEMATEL is reliable and can contribute to successful group decision making.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Artificial Intelligence
Sepehr Hendiani, Grit Walther
Summary: In this study, a new method called TOPSISort-L is proposed to classify alternative solutions under different circumstances by using the likelihoods of IVIFSs. By developing the conventional fuzzy TOPSIS technique with a newly proposed decision matrix, a novel selection mechanism for ideal solutions, and a likelihood-based closeness metric, this method can achieve accurate classification when characteristic profiles information is available and approximate classification when it is missing. Finally, the validity and adaptability of the method are demonstrated by comparing it with various existing methodologies.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shuli Yan, Yingying Zeng, Na Zhang
Summary: A multi-attribute quantum group decision-making method considering decision-maker's risk attitude is developed to address the entanglement issue among decision-makers' opinions. The method utilizes interval-valued intuitionistic fuzzy number to represent decision information in uncertain environment, derives the interference entanglement behavior using quantum probability, and reflects decision-makers' irrational risk attitude through comprehensive prospect value for alternative ranking.
Article
Automation & Control Systems
K. Janani, R. Rakkiyappan
Summary: This article introduces the concept of complex probabilistic fuzzy set to combine statistical and non-statistical uncertainties. It also develops various aggregation operators and extends them to the TOPSIS method for practical applications. The importance of this research lies in its ability to accurately depict real-life situations by incorporating different types of uncertainty.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Multidisciplinary
Dongsheng Xu, Lijuan Peng
Summary: Decision-making problems are complex and subjective, requiring different forms of information expression and the best method for specific situations; multi-valued neutrosophic set effectively describes incomplete, uncertain, or inconsistent information; a new method combining TODIM and TOPSIS is proposed to solve multi-attribute decision-making problems.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Hengshan Zhang, Yimin Zhou, Tianhua Chen, Richard Hill, Zhongmin Wang, Yanping Chen
Summary: This article introduces a method for adjusting attribute weights in multiple attribute decision making, which can accommodate changing data patterns. By constructing a model and introducing the concepts of attribute support and consensus, the weights can be easily modified to suit the specific application's requirements.
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Salman Ahmed Shaikh, Mohsin Memon, Kyoung-Sook Kim
Summary: Selecting an ideal business site involves various criteria such as traffic accessibility, visibility, ease of access, parking facilities, and customer availability. This paper introduces a hybrid approach based on AHP and TOPSIS methods to assist business managers in making optimal decisions for candidate locations, improving efficiency and accuracy.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Xin Zheng, Tengteng Hao, Huiyu Wang, Kaili Xu
Summary: In this study, an evaluation method was developed to quantitatively assess the mental load state of workers by establishing an evaluation index system based on physiological parameters, subjective feelings, and time perception. The proposed extended cloud evaluation model combined cloud model theory with analytic hierarchy process and technique for order preference to provide mental load levels. The method was validated through an energetic material initiation experiment and fuzzy comprehensive evaluation and subjective questionnaire. The results indicate that the extended CM evaluation method effectively quantifies the mental load state and can be applied to assess personnel mental state and occupational suitability in hazardous environments.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Dariusz Kacprzak
Summary: This paper proposes an extension of the TOPSIS method for Group Decision Making with Interval Numbers, which does not use aggregation operators and ensures that all decision makers' evaluations are taken into account. The numerical example demonstrates the ease of implementation in common data analysis software.
Article
Computer Science, Artificial Intelligence
Bapi Dutta, Son Duy Dao, Luis Martinez, Mark Goh
Summary: This study investigates strategic manipulation of weight information in a TOPSIS MADM method under two scenarios and formulates the problem as a mixed integer non-linear programming (MINLP) problem. A genetic algorithm based solution procedure is developed to solve this highly constrained problem.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Information Systems
Yumei Wang, Peide Liu, Yiyu Yao
Summary: In this paper, the classical TOPSIS method is generalized by adding a third middle reference point. Three classes of reference-point-based TOPSIS-style multi-criteria decision-making methods are examined, and the experimental results show that the BMW-TOPSIS model is feasible and effective.
INFORMATION SCIENCES
(2022)
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)