Xiao-kang Wang’s research while affiliated with Shenzhen University and other places

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Publications (55)


A decision support system for comments-adjusted ranking of hotels
  • Article

December 2024

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20 Reads

Journal of the Operational Research Society

Xiao-kang Wang

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Ya-nan Wang

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Probabilistic Linguistic Group Decision-Making Based on Evidential Reasoning Considering Correlations Between Linguistic Terms

June 2023

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41 Reads

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2 Citations

International Journal of Fuzzy Systems

In recent years, evidential reasoning (ER) has provided an effective means to deal with uncertain information depicted by probabilistic linguistic terms sets (PLTSs) during fusion. However, it is worth noting that due to the differences in decision makers’ preferences and understanding of linguistic terms, there is a significant difference between the level of ER and the term in PLTSs, which hinders further development of ER in PLTSs. To fill this gap, this study modifies the existing ER algorithm with linguistic correlation and introduces it to multiple attribute group decision-making (MAGDM) problems within PLTSs. First, the correlations between different linguistic terms are defined based on the expressive preferences of decision-makers. Second, the correlation between different linguistic terms is integrated into the original ER to reduce the contradiction caused by expressive preferences. Moreover, linguistic correlation is also involved in the calculation of reliability to adjust the distance measure, which can reduce the unreliability caused by the preferences expressed by decision-makers. Then, nonlinear programming models are conducted to drive expert reliability. Thereafter, the modified ER algorithm is employed to integrate expert opinions into a comprehensive evaluation of alternatives. Finally, an illustrative example of an industry evaluation problem is conducted to verify the robustness and validity of the proposed method.



Figure 2. Multi-rule inference algorithm.
Changes in different numbers of fuzzy clusters.
Experiment 1 results.
Experiment 2.
Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction
  • Article
  • Full-text available

May 2023

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50 Reads

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3 Citations

Mathematics

Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods.

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Linguistic Single-Valued Neutrosophic Multi-Criteria Group Decision Making Based on Personalized Individual Semantics and Consensus

May 2023

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116 Reads

Informatica

In practical linguistic multi-criteria group decision-making (MCGDM) problems, words may indicate different meanings for various decision makers (DMs), and a high level of group consensus indicates that most of the group members are satisfied with the final solution. This study aims at developing a novel framework that considers the personalized individual semantics (PISs) and group consensus of DMs to tackle linguistic single-valued neutrosophic MCGDM problems. First, a novel discrimination measure for linguistic single-valued neutrosophic numbers (LSVNNs) is proposed, based on which a discrimination-based optimization model is built to assign personalized numerical scales (PNSs). Second, an extended consensus-based optimization model is constructed to identify the weights of DMs considering the group consensus. Then, the overall evaluations of all the alternatives are obtained based on the LSVNN aggregation operator to identify the ranking of alternatives. Finally, the results of the illustrative example, sensitivity and comparative analysis are presented to verify the feasibility and effectiveness of the proposed method.


Citations (48)


... To represent the linguistic decision information accurately, Zhang et al. [18] proposed the linguistic distribution assessment (LDA) method, in which symbolic proportions are assigned to the linguistic terms in a linguistic term set. The LDA method is a generalization of the two-tuple linguistic method and probabilistic linguistic term sets, and defined as a set of binary relationships between linguistic variables and their symbolic probabilities [19,20]. Compared with other linguistic computing methods, the LDA method can efficiently represent uncertain information and reflect the real experience of decision makers [21,22]. ...

Reference:

New Approach for Quality Function Deployment Based on Linguistic Distribution Assessments and CRITIC Method
An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes
  • Citing Article
  • April 2024

Information Fusion

... This setting is appropriate for certain use cases, but often, the cognitive demands in document processing emerge with longer text. In applied settings, the aim is to ease timeconsuming tasks, e.g., in health care to reduce documentation time (Hou et al. 2024), and in legal and administrative sectors to categorize documents and extract relevant information (Valvoda and Cotterell 2024). So far, the applicability of feature attribution methods and their evaluation has not been sufficiently studied for long documents. ...

Modelling long medical documents and code associations for explainable automatic ICD coding
  • Citing Article
  • February 2024

Expert Systems with Applications

... Considering the objectivity and subjectivity of the TSM decision process, a combined weight calculation method based on the BWM [45] and maximum deviation method [46] can be developed. The solution algorithm is as follows: ...

Two-way referral cooperative hospital selection with uncertain information: A two-sided matching decision-making approach
  • Citing Article
  • July 2023

Computers & Industrial Engineering

... Commonly used machine learning algorithms for DHS prediction problems include, but are not limited to: Support Vector Regression (SVR) [9], Simple Recurrent Neural Network (Simple RNN) [10], Long Short-Term Memory Network (LSTM) [11], Sequence-to-Sequence (Seq2Seq) [12], and the Informer neural network [13]. ...

An attention-based Bayesian sequence to sequence model for short-term solar power generation prediction within decomposition-ensemble strategy
  • Citing Article
  • June 2023

Journal of Cleaner Production

... This method helps in the development of automated systems for processing fiction texts. Shulzhuk (2019) and Wang et al. (2023) indicate the importance of the study of small genres in the study of literary features. The approach covered in this study complements this by showing the applications of the frame theory to the analysis of larger fiction texts and the discovery of deep linguistic connections within them. ...

Probabilistic Linguistic Group Decision-Making Based on Evidential Reasoning Considering Correlations Between Linguistic Terms
  • Citing Article
  • June 2023

International Journal of Fuzzy Systems

... Several studies allow for the prediction of emergency risk depending on the road infrastructure [18 − 20], the occurrence of traffic jams in highway sections [21], and the use of a multi-criteria model [22]. Models designed due to fuzzy logic are used for the same approaches [23,24]. ...

Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction

Mathematics

... The continuous increase in the number of motor vehicles has brought many problems to society, such as traffic congestion, a waste of resources, economic losses, excessive commuting times, and frequent traffic accidents. In addition, the pollution caused by the large number of cars may threaten human health [1]. Since traffic flow can reflect the number of vehicles that pass a point in a certain period of time [2], accurate traffic flow forecasting is of great significance to management departments and individuals, which can optimize the design and operation of transportation systems to proposed a method to predict the spatio-temporal characteristics of short-term traffic flow by combing the k-nearest neighbor algorithm and bidirectional long-short-term memory network model. ...

An interpretable diagnostic approach for lung cancer: Combining maximal clique and improved BERT

Expert Systems

... This includes advancements in motor design, materials, control strategies, and power electronics. Additionally, efforts will be made to develop innovative motor technologies that address the specific requirements of EV applications, such as high power-to-weight ratio, compact size, and improved thermal management [17]. ...

Data-driven multi-criteria decision support method for electric vehicle selection
  • Citing Article
  • February 2023

Computers & Industrial Engineering

... However, in the real world, knowledge is constantly evolving with the passage of time, new facts, events, and relations continuously emerge, leading to complex temporal interactions among dynamic facts [1].Thus, temporal information is included in the knowledge graph to better simulate and understand the dynamics and complexity of the real world. This is crucial for a variety of downstream applications that are time-sensitive, including recommendation systems [2], financial analysis [3], medical fields [4], [5], and more. ...

A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction
  • Citing Article
  • October 2022

Computers in Biology and Medicine

... Fuzzy neural networks can be classified into type-1 and type-2 FNNs based on the type of fuzzy sets they utilize. While type-1 FNNs offer a more compact model structure and lower training costs, their performance is less effective compared to type-2 FNNs when dealing with complex uncertainty problems due to the crisp membership grades [6][7][8][9]. The introduction of the interval type-2 FNN [10] sparked a surge in applications of this type [11][12][13]. ...

A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction
  • Citing Article
  • August 2022

Knowledge-Based Systems