Uncertain Information Research Articles

Outlier detection based on multisource information fusion in incomplete mixed data

Multisource incomplete mixed data fusion (MsIMDF) plays a crucial role in outlier detection by utilizing complementary, informative, interpretative, and less noisy single-source data to identify unexpected errors or behaviors. However, existing multisource data fusion approaches only consider homogeneous data and single uncertain information,overlooking the mixed heterogeneous data and diverse uncertainty information. This limitation can negatively impact the performance of outlier detection. To address this issue, we propose MsIMDF-USF, a novel two-stage model that fully leverages the rich multisource knowledge and uncertainty information in incomplete mixed data. During the information fusion stage, the MsIMDF model combines multisource data into new single-source data using the minimum uncertainty strategy based on rough and fuzzy information. Subsequently, in the outlier detection stage, we reconstruct a neighborhood information network under a united-similar-fuzzy (USF) relationship using the new fused data. This reconstruction aims to strengthen the connections between similar objects while weakening relationships among dissimilar ones by considering single-attribute and multi-attribute information. Outlier scores are obtained based on the stationary distribution of the reconstructed networks using a Markov random walk. Experimental results on 16 real datasets demonstrate that the MsIMDF-USF model effectively extracts higher-quality data, exhibiting high applicability and robustness in outlier detection tasks.

A Multi-Criteria Framework for Assessing the Sustainable E-Waste Management Methods with Complex Fuzzy Environment

Due to the substantial economic and environmental repercussions of improper E-waste management and treatment, the issue has gained indispensable attention. In recent years, the research has been done about the selection of proper E-waste management methods like site selection, disposal method selection, etc. with fuzzy environments. But in real situations, the major reasons for improper e-waste management are lack of awareness and improper disposal with sustainability. In finding a sustainable awareness-creating program and disposal method for E-waste, the uncertainty and its sustainability of the information make challenges to decision makers, so the problem becomes a two-dimensional problem with uncertainty. This paper uses complex fuzzy terms to illustrate this two-dimensional uncertain information, in which amplitude term represents the uncertainty about alternative and phase term represents the uncertainty about the sustainability of an alternative. First, we present a new framework MULTIMOORA under complex fuzzy structure. Then, in order to solve two-dimensional uncertain information about E-waste, the proposed complex fuzzy-MULTIMOORA method is applied, in which E-waste awareness-creating program and treatment technology selection are done. The sensitivity and comparative analysis are further delivered to validate the effectiveness of the proposed technique.

Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement

To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.

Large-Scale Satisfaction Rating-Driven Selection of New Energy Vehicles: A Basic Uncertain Linguistic Information Bonferroni Mean-Based MCGDM Approach Considering Criteria Interaction

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers’ willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations.

A systematic survey of fuzzy deep learning for uncertain medical data

Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

Multi-Objective Software Defect Prediction via Multi-Source Uncertain Information Fusion and Multi-Task Multi-View Learning

Multi-Objective Software Defect Prediction via Multi-Source Uncertain Information Fusion and Multi-Task Multi-View Learning

Overlapping community-driven dynamic consensus reaching model of large-scale group decision making in social network

Large-scale group decision-making (LSGDM) serves as a pivotal tool for facilitating consistent decision results through intricate interactions among individuals within the network. However, the impact of complex trust relationships within overlapping communities on consensus is often overlooked in many studies. Moreover, the dynamic interaction between overlapping communities and the consensus reaching process (CRP) is seldom taken into account. This paper aims to build an overlapping community-driven consensus reaching model for addressing LSGDM challenges in social network. Given the advantages of intuitionistic fuzzy numbers (IFNs) in uncertain information representation, IFNs are used to express evaluation information and trust information. Firstly, a novel overlapping community detection method is developed to divide subgroups and detect overlapping communities. Secondly, to determine reliable subgroup weights, this paper constructs a weight determination model that considers multiple factors and their internal correlations. Then, an overlapping community-driven dynamic consensus model is proposed, which provides a new way to resolve the opinion conflicts, considering the dynamic change of CRP. Simultaneously, the reverse effect of opinion adjustment on the social trust network is considered. Finally, the practicality of the proposed model is demonstrated through illustrative cases. Furthermore, through a comparative analysis, the superiority of the proposed model is demonstrated and the efficiency improvement for CRP is verified.

Multi-Autonomous Underwater Vehicle Full-Coverage Path-Planning Algorithm Based on Intuitive Fuzzy Decision-Making

Aiming at the difficulty of realizing full-coverage path planning in a multi-AUV collaborative search, a multi-AUV full-coverage path-planning algorithm based on intuitionistic fuzzy decision-making is proposed. First, the state space model of the search environment was constructed using the raster method to provide accurate environment change data for the AUV. Second, the full-coverage path-planning algorithm for the multi-AUV collaborative search was constructed using intuition-based fuzzy decision-making, and more uncertain underwater information was modeled using the intuition-based fuzzy decision algorithm. A priority strategy was used to avoid obstacles in the search area. Finally, the simulation experiment verified the proposed algorithm. The results demonstrate that the proposed algorithm can effectively realize full-coverage path planning of the search area, and the priority strategy can effectively reduce the generation of repeated paths.

Cooperative Path Planning for Multiplayer Reach‐Avoid Games under Imperfect Observation Information

This article investigates a reach‐avoid game and proposes a cooperative path planning algorithm for a target–pursuers (TP) coalition to capture an evader. In the game, the target aims to bait and escape from the evader, and the pursuer aims to capture the evader. Due to imperfect observations, the TP coalition has uncertain information of the evader's state, while the evader is assumed to have perfect observation. The game model is constructed by formulating the optimization problems for each player in a receding horizon fashion. Then, to counter the evader effectively, the TP coalition constructs a virtual evader using the belief information from a Kalman filter. And a chance constraint optimization problem is constructed to predict the virtual evader's trajectory under uncertainties. The TP coalition can capture the actual evader by generating a robust counter‐strategy against the virtual evader with a chance constraint feasible set. Next, to compute the Nash equilibrium of the TP coalition's subjective game, an iterative algorithm is designed that combines the iterative best response and the distributed alternating direction method of multiplier algorithms. Finally, the effectiveness of the algorithm is validated through simulations and experiments.

An Improved Dempster–Shafer Evidence Theory with Symmetric Compression and Application in Ship Probability

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources.

Adaptive graph active learning with mutual information via policy learning

Graph neural networks entail massive labeled samples for training, and manual labeling generally requires unaffordable costs. Active learning has emerged as a promising approach to selecting a smaller set of informative labeled samples to improve model performance. However, few active learning techniques for graph data account for the cluster structure and redundancy of samples. To address these issues, we propose an approach that employs uncertain information as an observation for a reinforcement learning agent to adaptively learn a node selection policy. We construct states using node information obtained via mutual information, which considers both the graph structure and the node attributes. The proposed method accurately quantifies node information by leveraging the receptive field of the graph convolutional network and capturing the clustering structure of the data, taking into account the low redundancy and diversity of the labeled samples. Experiments conducted on real-world datasets demonstrate the superiority of the proposed approach over several state-of-the-art methods.

Dynamic evolutionary analysis of opinion leaders’ and netizens’ uncertain information dissemination behavior considering random interference

Dynamic evolutionary analysis of opinion leaders’ and netizens’ uncertain information dissemination behavior considering random interference

Prioritization of sustainable development plannings for supply chain finance using a novel Triangular neutrosophic-based TOPSIS-CPT-VIKOR model

The development of sustainable supply chain finance (SSCF) promotes the improvement of the long-term production and operational performance of enterprises. It is significant to explore both environmentally friendly and cost-effective solutions, especially in the presence of uncertain information. The triangular neutrosophic number (TrNN) can enable decision-makers (DMs) to express their preferences in high uncertainty. This paper aims to provide a systematic multiple-attribute group decision-making (MAGDM) framework to support DMs in prioritizing sustainable development plannings by combining the best-worst method (BWM), optimization model of maximizing deviation method, and uniformity entropy theory. Moreover, a TrNN-based extended TOPSIS method is developed to measure the differences in DMs’ importance. We incorporate the cumulative prospect theory (CPT) method to account for DMs’ psychological behaviors in risk scenarios. The CPT-VIKOR methodology is employed to rank alternatives, enhancing the precision of decision-making. Finally, the proposed approach is successfully applied to the LNG industry to illustrate its applicability.

Eulerian and Hamiltonian Soft Semigraphs

Soft set theory is a mathematical approach to address the challenges of handling vague or uncertain information. It is a more advanced version of classical set theory that deals with imprecise elements and enables the flexible representation of uncertain data. It involves categorizing the elements of the universe based on specific parameters. Semigraph is a generalization of a graph which is different from a hypergraph. A hypergraph extends the concept of a graph by allowing any subset of vertices to form an edge. Semigraphs, on the other hand, distinguish themselves from hypergraphs by imposing a specific order on the vertices within each edge. Soft semigraphs were developed using the principles of soft set theory applied to semigraphs. This study introduces Eulerian and Hamiltonian soft semigraphs. We establish a necessary and sufficient condition for a soft semigraph to be Eulerian, relying on parameters such as [Formula: see text]-part consecutive adjacent degree, [Formula: see text]-part end degree, and the [Formula: see text]-part consecutive adjacency graph. Additionally, we provide the conditions for a soft semigraph to be Hamiltonian. We introduce the concept of maximal non-Hamiltonian [Formula: see text]-part. Finally, we define the closure of a soft semigraph and demonstrate the relationship between a Hamiltonian soft semigraph and its closure.

EDAS method for circular pythagorean fuzzy with improved Dombi power aggregation operators and their application in policy analysis and decision support systems

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values.

Application of energy combined thermal comfort in intelligent building management in complex environments

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

A multiple attribute group decision making model based on 2-tuple linguistic pythagorean fuzzy dombi aggregation operators for optimal selection of potential global suppliers

Multiple-Attribute Group Decision-Making (MAGDM) is a significant area of research in decision-making, and its principles and methodologies are widely implemented. A Pythagorean Fuzzy Set (PFS) is an extension of an Intuitionistic Fuzzy Set (IFS) that is highly valuable for representing uncertain information in real-world scenarios. The 2-Tuple Linguistic Pythagorean Fuzzy Number (2TLPFN) is a specific type of Pythagorean Fuzzy Number (PFN) that can be used to represent uncertainty in real-world decision making through the use of 2-Tuple Linguistic Terms (2TLTs). This paper focuses on the examination of Multiple Attribute Group Decision Making (MAGDM) using 2TLPFNs. Dombi's t-norm and t-conorm operations were commonly referred to as Dombi operations, which might have been greater degree of applicability if offered in a new form of flexibility within the general parameter. In this research, we implement Dombi operations to construct some 2-Tuple Linguistic Pythagorean Fuzzy (2TLPF) Dombi Aggregation operators. These operators include the 2TLPF Dombi Weighted Averaging (2TLPFDWA) operator, 2TLPF Dombi Ordered Weighted Averaging (2TLPFDOWA) operator, 2TLPF Dombi Weighted Geometric (2TLPFDWG) operator, and 2TLPF Dombi Ordered Weighted Geometric (2TLPFDOWA) operator. An analysis is conducted to examine the unique characteristics of these suggested operators. Subsequently, we leveraged the proposed operators to develop a model aimed at tackling the MAGDM problems in the 2TLPF environment. Eventually, a suitable instance has been demonstrated to validate the formation of the model as well as exhibit its implementation and resilience.

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.

Complete ranking for generalized trapezoidal fuzzy numbers and its application in supplier selection using the GTrF-CoCoSo approach

Decision-making is an indispensable tool in many real-life situations. The classical set theory is insufficient to solve problems involving uncertainty and qualitative data. In order to tackle scenarios with qualitative information and imprecise boundaries, fuzzy set theory generalizes crisp sets. Thus, ranking is an essential methodology in decision-making in a fuzzy environment. In literature, generalized trapezoidal fuzzy numbers (GTrFNs) are used to model many real-life situations with imprecise, uncertain, and incomplete information. There are various techniques available to discriminate arbitrary GTrFNs. Several researchers are seeking a standard consensus technique to distinguish between arbitrary GTrFNs. The diagonal distance right position, diagonal distance left chord, diagonal distance center position, diagonal distance, and diagonal distance right chord scores are the five score functions that are used in this work to develop a complete ranking (total ordering) principle on the class of GTrFNs to distinguish arbitrary GTrFNs. To show the potentiality of the proposed method, we compare the proposed approach with the few existing approaches suggesting to rank GTrFNs. Further, we proposed a new GTrF-CoCoSo approach by incorporating the proposed complete ordering principle. Then the efficacy of the proposed ranking principle is examined in solving multi-criteria decision-making (MCDM) using the GTrF-CoCoSo approach to select the best banana supplier for food production company. Furthermore, a sensitivity analysis is presented to evaluate the performance of the suggested ranking principle with the GTrF-CoCoSo approach.

Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.