Article
Computer Science, Interdisciplinary Applications
Demmelash Mollalign, Allen Mushi, Berhanu Guta
Summary: This paper introduces a two-phase intuitionistic fuzzy goal programming algorithm to solve multi-objective multilevel programming problems, modeling the problem in a fuzzy environment and optimizing the objective functions to address decision-making issues at multiple levels. The method can generate compromise solutions that satisfy both MN-Pareto optimal solution and Pareto optimal solution at each level.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Horticulture
Qiang Zhang, Minji Li, Beibei Zhou, Junke Zhang, Qinping Wei
Summary: The study aimed to understand the impact of meteorological factors on the quality of 'Fuji' apples in the Circum-Bohai and Loess Plateau regions of China. Different meteorological factors, such as precipitation, temperature and sunshine, were found to significantly affect apple quality in the two production regions. Optimum meteorological conditions for high-quality apple production varied between the two regions, with factors like temperature, humidity, and precipitation playing key roles.
Article
Computer Science, Artificial Intelligence
Murshid Kamal, Umar Muhammad Modibbo, Ali AlArjani, Irfan Ali
Summary: This paper addresses the multi-objective selective maintenance allocation problem in a neutrosophic environment, introducing a new defuzzification technique and using neutrosophic goal programming to determine compromise allocation for system reliability optimization. The model is validated with a numerical illustration and found to be better compared to other methods.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Gao-Feng Yu, Deng-Feng Li
Summary: This paper proposes a new method for regional green manufacturing level assessment, which takes into account the interactions between various sources of information and expert preference information. By defining consistency and inconsistency indices as well as attribute weights, the problem of multi-attribute decision making is successfully addressed.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Green & Sustainable Science & Technology
Mike Spiliotis, Dionissis Latinopoulos, Lampros Vasiliades, Kyriakos Rafailidis, Eleni Koutsokera, Ifigenia Kagalou
Summary: This article examines how to achieve a compromise between the economic benefits of agricultural water use and environmental protection in order to achieve sustainable management of a lake. By introducing new management practices and using flexible programming methods, a feasible solution that maximizes economic benefits and minimizes water retention time is found, and the solution is verified through the values of membership functions.
Article
Computer Science, Artificial Intelligence
Gao-Feng Yu, Deng-Feng Li, De-Cui Liang, Guang-Xu Li
Summary: This paper proposes a novel and unified Intuitionistic Fuzzy Multi-Objective Linear Programming (IFMOLP) model to solve multi-objective decision problems in portfolio selection. By constructing nonmembership functions and utilizing IF inequalities to represent decision maker's hesitation degrees towards multiple objectives, the model avoids tedious computational burden of traditional methods and enhances solution efficiency while reducing complexity.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Computer Science, Artificial Intelligence
Xue Deng, Chuangjie Chen
Summary: In this paper, a new approach is proposed to assist investors in making rational portfolio selections without time series data. The problem is formulated as a multi-criteria decision making problem using intuitionistic fuzzy sets, and the TOPSIS method is modified to better balance returns and risks. Several novel linear programming models are introduced to allocate investment ratios according to investors' demands. Compared to traditional methods, the new approach demonstrates greater effectiveness and flexibility in providing appropriate investment strategies based on investors' preferences and demands.
EGYPTIAN INFORMATICS JOURNAL
(2022)
Article
Green & Sustainable Science & Technology
Jose Ramon Segarra-Moliner, Inmaculada Bel-Oms
Summary: The aim of this study is to analyze the relationship between environmental, social and governance dimensions of corporate sustainability initiatives and customer lifetime value. A data sample of 547 US listed firms was divided into three segments and a prediction-oriented modeling segmentation was used to test hypotheses and evaluate the predictive validity of a partial least squares model. The results showed that ESG dimensions encompass ten sustainability initiatives that are precursors of future financial firm performance represented by CLV.
Article
Multidisciplinary Sciences
Qinqing Xiong, Wenju Wang, Mingya Wang, Chunhui Zhang, Xuechun Zhang, Chun Chen, Mingshi Wang
Summary: This study proposes a hybrid neural network model SOM-NARX based on the correlation of predictors for ozone prediction. The model filters predictors using MIC, transforms them into feature sequences using SOM, and makes predictions using NARX networks. The results show that the correlation of predictors, classification numbers of SOM, neuron numbers, and delay steps can affect prediction accuracy. Model comparison shows that the SOM-NARX model outperforms other models in terms of RMSE, MAE, and MAEP.
Article
Environmental Studies
Ezzeddin Bakhtavar, Sahra Saberi, Guangji Hu, Rehan Sadiq, Kasun Hewage
Summary: A fuzzy-weighted multi-objective programming model was developed to determine the best feasible waste scheduling strategy. It was found that entirely dumping waste rock in the internal dumps could lead to the lowest social, environmental, and operational issues.
Article
Agronomy
Xiangcheng Ma, Mengfan Lv, Tie Cai, Zhikuan Jia
Summary: Studying carbon dioxide fluxes in wheat fields in dry semi-humid areas is important, but this area has been rarely studied systematically. Therefore, we conducted a monitoring experiment to clarify the response of CO2-C fluxes to meteorological factors and water-nitrogen management in wheat fields in this area. The results showed that precipitation, air temperature, water vapor pressure, irrigation, and nitrogen application all had positive effects on CO2-C fluxes in wheat fields.
Article
Computer Science, Artificial Intelligence
R. Isachenko, V. V. Strijov
Summary: This paper investigates dimensionality reduction problem in signal decoding, specifically in the field of brain-computer interface modeling. It proposes a feature selection algorithm to construct a simple and stable forecasting model. The algorithm considers the correlations in both design and target spaces and selects features that fit both. Experimental results demonstrate that the algorithm achieves the best result in reducing space dimensionality.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Economics
Xiang Li, Xianghui Yuan, Jin Yuan, Hailun Xu
Summary: Introducing FM-RLS and RW-OLS methods for predicting CSI 300 intraday index return in the Chinese stock market showed better performance than the same sign method. The additional profit mainly comes from two conflicting signals, with one amplitude far greater than the other.
APPLIED ECONOMICS LETTERS
(2021)
Article
Engineering, Mechanical
Xinghao Du, Jinhao Meng, Kailong Liu, Yingmin Zhang, Shunli Wang, Jichang Peng, Tianqi Liu
Summary: This paper proposes a co-estimation framework utilizing the advantages of both recursive least squares (RLS) and recursive total least squares (RTLS) for a higher parameter identification performance of the battery equivalent circuit model (ECM). RLS quickly converges by updating the parameters along the gradient of the cost function, while RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results show that the proposed method has good accuracy, a fast convergence rate, and robustness against noise corruption.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2023)
Article
Energy & Fuels
Sangmin Lee, Trine Krogh Boomsma
Summary: This paper discusses the optimal operation of a fleet of plug-in hybrid electric vehicles in a market setting. By formulating a Markov decision process, the study seeks to minimize costs by determining the best policy for engine utilization and battery charging/discharging, considering uncertainties in electricity prices and driving demands. The methodology of approximate dynamic programming, using simulation and value function approximation, is employed to address computational challenges. The results show that discharging the battery, rather than using the engine, is generally preferred, unless the battery capacity is insufficient. Charging the battery should be timed according to power prices. The proposed policy demonstrates superiority over the simple policy of immediate charging, with a cost difference of 2%-4% for small and medium-sized fleets.
Article
Environmental Sciences
Nasir Abbas Khan, Zaiwu Gong, Ashfaq Ahmad Shah
Summary: Climate variability in Pakistan poses risks to rice crops, impacting the livelihoods and food security of millions of rural households. This study shows that farmers have high perception of risks and have implemented various adaptation measures, which have positive impacts on rice yield and returns.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Shuli Yan, Qi Su, Zaiwu Gong, Xiangyan Zeng
Summary: This paper aims to predict the trend of short-term online public opinion by establishing a fractional order multivariable time-delayed discrete grey model, considering the rapid fermentation characteristics of online public opinion. The time-delay parameters are introduced and the lag time is determined by the maximum grey correlation theory to take into account the dynamic time-varying delays of each driving factor. The model is further applied to forecast a real event on microblog, showing higher prediction accuracy compared to related grey models.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xia Liu, Yejun Xu, Zaiwu Gong, Francisco Herrera
Summary: Democratic consensus aims to achieve a soft consensus among a group while ensuring individual satisfaction; MpMcLSDM involves a large number of decision makers, and effectively managing individuals to promote democratic consensus is a current research challenge, this study proposes a DCRP approach to address this issue.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Yanxin Xu, Zaiwu Gong, Guo Wei, Weiwei Guo, Enrique Herrera-Viedma
Summary: This paper explores dimensionality reduction and subgroup optimization in large-scale group decision-making (LSGDM) by utilizing uncertainty theory and linear uncertain variables in social networks. A clustering method is proposed to decompose the large group into subgroups with higher consilience degrees and preference similarities, lowering the information dimension for LSGDM.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Geosciences, Multidisciplinary
Ruiling Sun, Zaiwu Gong, Weiwei Guo, Ashfaq Ahmad Shah, Jie Wu, Haiying Xu
Summary: An effective risk assessment and response to flood disasters is crucial for the sustainable development of a region. The Choquet integral method provides a useful supplement to traditional indicator integration methods as it can effectively address the issues of index interactions and information overlap. A flood disaster risk assessment model based on the Choquet integral was constructed for the Yangtze River Delta region, taking into account various factors from 2006 to 2017. The assessment results can provide valuable insights for flood disaster risk management in the region.
Article
Computer Science, Artificial Intelligence
Juan Antonio Morente-Molinera, Yinglin Wang, Zai-Wu Gong, A. Morfeq, Rami Al-Hmouz, Enrique Herrera-Viedma
Summary: This article introduces a new method for multicriteria group decision-making, which reduces the initial set of criterion values using hierarchical clustering methods and utilizes fuzzy ontologies as an aid system. This method allows experts to focus on ranking the reduced set of criterion values and manage a fair amount of information in the decision-making process.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Management
Weiwei Guo, Zaiwu Gong, Wei-Guo Zhang, Yanxin Xu
Summary: Due to the difficulty and cost of achieving hard consensus in group decision making, it is reasonable and necessary for decision makers to reach a soft consensus within a certain level of tolerance and consensus. This paper proposes the concepts and definitions of consensus principle, tolerance level, and consensus level, and introduces the minimum cost consensus models based on these points. A feedback regulation mechanism based on tolerance threshold rewards is added to the models to connect individual weight with tolerance threshold, and a precise method for solving realistic consensus problems is provided.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Huanhuan Song, Zaiwu Gong, Jeffrey Yi-Lin Forrest, Weiwei Guo, Enrique Herrera-Viedma
Summary: In social network group decision-making, interactions between decision makers play a crucial role in achieving sufficient consensus. This study combines traditional cost consensus models with Choquet integral and empathy theory to consider interaction constraints in opinion integration and adjustment evolution. By incorporating social network structures, interaction characteristics, and influence indices, the convergence law of social network cost consensus is investigated. Different cost consensus models, including fuzzy interaction, empathic interaction, and a combination of both, are constructed using piecewise-linear utility functions to capture the heterogeneity and stage characteristics of decision makers' attitudes. A case analysis of emergency material reserve decision-making demonstrates the applicability of the proposed models.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Environmental Sciences
Zhijuan Jiang, Rui Yan, Zaiwu Gong, Gaofeng Guan
Summary: To achieve net zero emissions, emission reduction paths for road freight need to be explored. The study shows that different fuel and electricity mixes, as well as vehicle technology, have different impacts on emissions. Policymakers should develop emission standards based on vehicle characteristics and technology.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Management
Fan-Yong Meng, Zai-Wu Gong, Witold Pedrycz, Jun-Fei Chu
Summary: In group decision making, when individual decision information is discrepant, an adjustment mechanism is activated. This paper addresses the selfishness of decision makers (DMs) and proposes a fair and reasonable consensus adjustment mechanism to counteract their selfish behavior and promote consensus-reaching. A model is built to identify the minimum consensus adjustment of each DM, and the concept of selfish-dilemma consensus adjustment cooperative games is introduced. Payoff indices, namely the Shapley value and the nucleolus, are used for the minimum total consensus adjustment allocation to ensure fairness and reasonableness. A numerical example is provided, and a comparative analysis is conducted, demonstrating the effectiveness of this approach in achieving Pareto-optimal consensus adjustment allocation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Xiaohui Gao, Zaiwu Gong, Qingsheng Li, Guo Wei
Summary: This research proposes a hybrid mathematical model, called the Choquet integral-based model, to reliably predict natural gas consumption. The model combines the grey accumulation generating operator and the grey wolf optimizer to comprehensively consider model performance and handle both seasonal and long-term variability. The results demonstrate that the proposed model exhibits better robustness and prediction performance by considering the interaction between models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shuli Yan, Qi Su, Zaiwu Gong, Xiangyan Zeng, Enrique Herrera-Viedma
Summary: This paper proposes a novel new information variable weight fractional rolling grey model for predicting online public opinion trends, which shows better prediction performance compared with other models.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Yizhao Zhao, Zaiwu Gong
Summary: This study addresses the issues of non-uniqueness of optimal weights, lack of consideration for DMUs' satisfaction, and excessive differences of indicators in the secondary goal approach for cross-efficiency evaluation. To overcome these problems, the study introduces the concept of minimum dissimilarity of weights and constructs an improved method based on DMUs' satisfaction. The method applies the 2-additive Choquet integral to reflect the pairwise interaction between input (or output) indicators and adjust DMUs' satisfaction targets according to ethical principles.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2023)
Article
Automation & Control Systems
Xiaoxia Xu, Zaiwu Gong, Enrique Herrera-Viedma, Gang Kou, Francisco Javier Cabrerizo
Summary: This article extends the research on uncertain minimum cost consensus models (MCCMs) by incorporating linear uncertainty distributions (LUDs) and considering asymmetric costs. Two novel optimization-based consensus models are proposed, one for obtaining a minimum cost consensus and the other for addressing group decision making problems without presetting a specific consensus level (CL) threshold.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zaiwu Gong, Xiujuan Ma, Weiwei Guo, Guo Wei, Enrique Herrera-Viedma
Summary: The study proposes a consensus approach with trust relationships and adjustment cost to address the lack of attention to individual decision costs and similarity in expert decision behaviors in the social network decision process. The method consists of three stages: trust propagation, weight allocation, and consensus reaching. Uncertain theory is employed in trust propagation while comprehensive weight allocation is based on network structure and relationship strength. Consensus is considered at both individual and collective group levels using chance-constrained programming models. Comparative analysis is conducted to evaluate the effectiveness and advancement of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(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)
