Article
Mathematics
Emili Vizuete-Luciano, Sefa Boria-Reverter, Jose M. Merigo-Lindahl, Anna Maria Gil-Lafuente, Maria Luisa Sole-Moro
Summary: This paper introduces a new assignment algorithm using the OWA operator and its extensions in the Branch-and-bound algorithm, providing more detailed information. The algorithm is applied in a consumer decision-making model in Barcelona, aiding in selecting grocery districts that best suit their needs, while considering different sources of information independently.
Article
Mathematics
Rodrigo Gomez Monge, Evaristo Galeana Figueroa, Victor G. Alfaro-Garcia, Jose M. Merigo, Ronald R. Yager
Summary: This paper introduces variance logarithmic averaging operators and analyzes their properties, families, and particular cases, providing an illustrative example from financial markets to showcase the design of these operators. Results show that the use of variance measures aids decision-making by offering new tools for information analysis and extends the available tools for decision-making under ignorance, uncertainty, and subjective environments.
Article
Computer Science, Artificial Intelligence
Jose Carlos R. Alcantud, Gustavo Santos-Garcia, Muhammad Akram
Summary: This work improves the theoretical basis of N-soft sets and demonstrates their practical applications in a multiagent context. It presents a novel theory of aggregation of N-soft sets using OWA operators and applies it to multi-agent decision-making, resulting in the first algorithms for such decisions based on N-soft sets.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Luis A. Perez-Arellano, Fabio Blanco-Mesa, Ernesto Leon-Castro, Victor Alfaro-Garcia
Summary: This article applies Bonferroni prioritized induced heavy ordered weighted average (OWA) to analyze data and presents a new aggregation operator combining prioritized induced Bonferroni and heavy induced prioritized operators. By evaluating the transparency of websites in 32 states of Mexico, it shows how rankings can change depending on different operators and expert considerations.
Article
Mathematics
Fabio Blanco-Mesa, Ernesto Leon-Castro, Jorge Romero-Munoz
Summary: This paper introduces a new operator, PMGIOWMA, which can incorporate the knowledge, expectation, and aptitude of the decision maker into the Pythagorean membership function. An application of this operator in a financial knowledge survey conducted in Boyaca, Colombia is presented.
Article
Computer Science, Theory & Methods
Radko Mesiar, Andrea Stupnanova, LeSheng Jin
Summary: This paper introduces OWA operators and their representation based on Choquet integrals, and proposes a generalization of OWA operators called BIOWA operators. The properties of BIOWA operators are studied and exemplified through various examples.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
LeSheng Jin, Zhen-Song Chen, Ronald R. Yager, Jana Spirkova, Radko Mesiar, Daniel Paternain, Humberto Bustince
Summary: This study introduces a novel induced ordered weighted averaging operator, which uses ordered weighted averaging weight vectors instead of real numbers. Three specific weight allocation methods are proposed and numerical examples are provided. Mathematical properties of these weight allocation methods are further discussed with the use of specific quantifiers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Victor G. Alfaro-Garcia, Jose M. Merigo, Anna M. Gil-Lafuente, Rodrigo Gomez Monge
Summary: This paper introduces a new weighted aggregation operator, IGOWLA, which takes into account the complex attitudes of decision makers and has strong practicality. In addition to presenting the general form of the operator and some special cases, an example of group decision-making using the IGOWLA operator in the field of innovation management is analyzed.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Abhijit Saha, Tapan Senapati, Ronald R. Yager
Summary: The study combines the advantages of the generalized Dombi operator and Bonferroni mean operator for addressing multicriteria group decision-making issues under a DPL setting. Concepts of consistency and similarity are used to determine decision-experts' weights, and grey correlation coefficient is utilized for criteria weighting. New aggregation operators are proposed to capture interrelations between criteria, and a case study focusing on biomass feedstock selection is provided to demonstrate the applicability of the proposed operators. Parameters' effects on ranking order, sensitivity assessment of criteria weights, and comparison with existing methods are also investigated.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
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
Computer Science, Artificial Intelligence
Xiang Jia, Yingming Wang
Summary: This paper improves the traditional MCDM techniques by introducing the Choquet integral-based intuitionistic fuzzy arithmetic aggregation (CIIFAA) operator and the Choquet integral-based intuitionistic fuzzy hybrid arithmetic aggregation (CIIFHAA) operator, to better handle decision-making problems in an intuitionistic fuzzy environment.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Martha Flores-Sosa, Ezequiel Aviles-Ochoa, Jose M. Merigo, Ronald R. Yager
Summary: The research discusses the importance of volatility and proposes an estimator that combines OWA operators with OLS. By incorporating OWA operators into ARCH-GARCH models, a method that can handle high levels of uncertainty is developed, ultimately achieving efficient forecasting in MX/US exchange rate volatility.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yibo Wang, Xiuqin Ma, Hongwu Qin, Huanling Sun, Weiyi Wei
Summary: This research presents the definition of hesitant Fermatean fuzzy Bonferroni mean operator (HFFBM) and derives the hesitant Fermatean fuzzy Einstein Bonferroni mean operator (HFFEBM) using basic operations of hesitant Fermatean fuzzy sets in Einstein t-norms. It also develops the hesitant Fermatean fuzzy weighted Bonferroni mean (HFFWBM) operator and the hesitant Fermatean fuzzy Einstein weighted Bonferroni mean operator (HFFEWBM), considering the influence of weights on decision-making outcomes. Moreover, a new multi-attribute decision-making (MADM) approach based on HFFWBM and HFFEWBM operator is provided and applied to a depression diagnostic evaluation with satisfactory results.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dejian Yu, Tianxing Pan, Zeshui Xu, Ronald R. R. Yager
Summary: In recent years, the OWA operator has received increasing attention in the academic community. Growth curve analysis, commonly used in ecosystem studies, suggests that this trend will continue. However, previous literature has not provided a comprehensive overview of the field's development and evolution. This study employed classic main path analysis and its variations on a citation network of 1474 papers to uncover the development trajectories and research topics of OWA. The findings reveal the pervasive presence of weight generation and operator generalization in the OWA domain, the dynamic and multi-period nature of the multiple criteria decision-making process, and the incorporation of theories like social network theory and expanded applications of the OWA operator.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Chemistry, Multidisciplinary
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
Summary: In this study, OWA operators are integrated into Convolutional Neural Networks through the OWA layer for image classification, enabling the CNN to leverage global information about the image in the early stages of processing. The OWA layer also serves as a practical method for determining OWA operator weights, which can be challenging in other fields. The weights learned for OWA operators within the OWA layer are characterized based on their orness and dispersion, with comparisons made to other families of OWA operators to highlight unique examples that cannot be generalized through current parameterizations.
APPLIED SCIENCES-BASEL
(2021)
Review
Economics
Shashi, Piera Centobelli, Roberto Cerchione, Jose M. Merigo
Summary: This paper presents a comprehensive bibliometric and network analysis on knowledge management (KM) to understand its development from the perspective of academic communities. The study evaluates 8,721 KM papers published in the last 30 years and uses co-citation analysis to identify associations between themes and predict emerging trends. The findings provide valuable insights for researchers and academicians in specific sub-fields.
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
(2022)
Article
Computer Science, Cybernetics
Miriam Edith Perez-Romero, Victor G. Alfaro-Garcia, Jose M. Merigo, Martha Beatriz Flores-Romero
Summary: The objective of this study is to introduce the covariance ordered weighted logarithmic averaging (Cov-OWLA) operators for decision-making processes in uncertain environments. An illustrative example using real-world data shows a positive linear relation between the introduced variables. The aggregation using Cov-OWLA operators demonstrates a better fit compared to traditional methods.
CYBERNETICS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Walayat Hussain, Honghao Gao, Muhammad Raheel Raza, Fethi A. Rabhi, Jose M. Merigo
Summary: This paper focuses on the measurement of the overall performance of service-oriented applications, specifically the Quality of Service (QoS). By implementing an Induced Ordered Weighted Average (IOWA) layer to reduce data dimensions, the authors were able to achieve better prediction accuracy and handle complex QoS data more effectively.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Business
Shouzhen Zeng, Jiamin Zhou, Chonghui Zhang, Jose M. Merigo
Summary: Digital reform requires enterprises to use digital technology to integrate their production, management, and operational processes and meet personalized customer requirements. This paper proposes a multi-criteria evaluation model based on a social network to scientifically assess the achievements of digital reform. The model utilizes an intuitionistic fuzzy hybrid average and geometric operator for effective information aggregation and assigns weights to experts based on trust relationships in the social network.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Business
Leslier Valenzuela-Fernandez, Ignacio Munoz Quezada, Jose M. Merigo
Summary: This research presents a bibliometric analysis of advertising trends in the business area and provides valuable information for future research. The study shows a growing trend in the number of advertising publications and identifies the most cited and influential journals in the field, such as the Journal of Advertising and the Journal of Advertising Research.
JOURNAL OF GLOBAL SCHOLARS OF MARKETING SCIENCE
(2023)
Article
Business
Nelson A. Andrade-Valbuena, Leslier Valenzuela-Fernandez, Jose M. Merigo
Summary: This paper explores the development trends of strategic management research over 35 years and the differences among different countries through bibliometric analysis. The findings suggest the existence of common research subjects in the field, as well as diverse research agendas at the national level.
CUADERNOS DE GESTION
(2022)
Article
Computer Science, Artificial Intelligence
Dalia Garcia-Orozco, Victor G. Alfaro-Garcia, Jose M. Merigo, Irma C. Espitia Moreno, Rodrigo Gomez Monge
Summary: This paper analyzes the evolution of the FSR field by examining journals from 1965 to 2019 and visualizes the current landscape of key FSR journals, focusing on productivity, influence, and synergy. The results show the journals with the highest productivity in different periods, the most cited journals in the past 20 years, and the journals with the highest impact factor. Additionally, a bibliographic coupling analysis reveals the impact of communication development on the diversification and dissemination of influence, citations, and productivity of scientific journals.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Martha Flores-Sosa, Ernesto Leon-Castro, Jose M. Merigo, Ronald R. Yager
Summary: This paper introduces the MLR-HOWA operator, which uses HOWA means to obtain beta values. It provides the possibility of under or overestimating results based on the decision maker's expectations and knowledge, allowing for analysis of multiple scenarios from minimum to maximum. The paper also presents the main properties of the operator and two extensions using induced and generalized variables. An application in exchange rate forecasting for five Latin American countries is provided, demonstrating that using different combinations of MLR with OWA operators can reduce forecasting errors.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Victor G. Alfaro-Garcia, Fabio Blanco-Mesa, Ernesto Leon-Castro, Jose M. Merigo
Summary: This paper introduces extended distance measures and logarithmic OWA-based decision making operators for analyzing financial investment options. Several operators and their characteristic designs are presented, and a financial decision making example is proposed to demonstrate the advantages of the operators.
Article
Economics
Martha Flores-Sosa, Ernesto Leon-Castro, Ezequiel Aviles-Ochoa, Jose Merigo
Summary: Estimating and forecasting volatility is crucial for financial decision-makers. This study proposes a new approach that combines linear regression and ordered weighted average (OWA) operators to adapt to uncertainty and known information. The applicability of this approach is analyzed in the context of exchange rate volatility forecasting.
ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
(2022)
Correction
Business
Alicia Mas-Tur, Norat Roig-Tierno, Shikhar Sarin, Christophe Haon, Trina Sego, Mustapha Belkhouja, Alan Porter, Jose M. Merigo
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Business, Finance
Emili Vizuete-Luciano, Oktay Guzel, Jose M. Merigo
Summary: This study utilizes bibliometric analyses to examine the research field of Pay-What-You-Want (PWYW) pricing strategy. The analysis identifies influential cited works and authors, and reveals the intellectual and thematic structure of the field. Based on the findings, future research paths are suggested.
JOURNAL OF REVENUE AND PRICING MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Walayaty Hussain, Jose M. Merigo, Jaime Gil-Lafuente, Honghao Gao
Summary: Neural network methods are widely used in business for prediction, clustering, and risk management. This paper proposes a neural network sorting mechanism to handle complex nonlinear relationships in datasets, reducing computational complexities. The research opens up a new area for complex nonlinear predictions with large datasets.
Article
Computer Science, Information Systems
Walayat Hussain, Jose M. Merigo, Honghao Gao, Asma Musabah Alkalbani, Fethi A. Rabhi
Summary: Due to the lack of a common framework, the selection of cloud providers and allocation of marginal resources have become complicated. Existing frameworks have ignored the complex nonlinear relationships between service evaluation criteria, leading to ineffective decision-making systems. This paper proposes a centralized framework that considers consumer's customized priority criteria, determines relative importance, and intelligently assigns weights to each criterion. The framework enables optimal service provider selection and wise resource management, fostering sustainable and trusted relationships.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Management
Christian A. Cancino, Jose M. Merigo, David Urbano, J. Ernesto Amoros
Summary: This study analyzes the journals and universities that published research on entrepreneurship and innovation by Ibero-American authors between 1986 and 2015. The results show that the most outstanding researchers in the region are mainly from Spain and Portugal. Spanish researchers, in particular, are the most productive and influential authors in the region. A small group of researchers from Chile, Argentina, and Mexico are also highly influential. Latin American researchers need to deepen their international academic networks.
JOURNAL OF SMALL BUSINESS MANAGEMENT
(2023)
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)
