Expert Uncertainty Research Articles

Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation

The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes.

A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images with Multi-Relationship Graph Learning.

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

An MCDM Approach for Evaluating Construction-Related Risks using a Combined Fuzzy Grey DEMATEL Method

There is a need for more research into prioritizing project risks based on a sound technique due to the complicated and disorganized character of this stage. The project risk management process typically begins with the identification of critical hazards. This study presents a Grey Fuzzy Decision-Making Trial and Evaluation Laboratory (FGDEMATEL) approach to prioritize potential causes of project risks within Multi-Criteria Decision-Making (MCDM). This framework organizes the numerous risks using the Risk Breakdown Structure (RBS) of the Project Management Institute (PMI). The risk information used in this analysis comes mostly from the views and choices of project experts. Grey theory, which takes language phrases for preference collections and translates them into numerical intervals, is responsible for controlling uncertainty and variance in experts' preferences. As each expert has unique skills and experiences, it evaluates the significance of their opinions using a fuzzy number system that incorporates three dimensions. In the end, the FGDEMATEL approach devised a method to rank various project risks.

Evaluation of sustainable energy systems in smart cities using a Multi-Expert Pythagorean fuzzy BWM & TOPSIS methodology

Smart cities are technological settlements using the collected data to utilize resources and services effectively by combining information and communication technologies with various tools connected to the internet of things network. Sustainable energy systems in smart cities are systems can be evaluated by using multiple criteria decision making methods or methodologies based on several vague/imprecise evaluation criteria. In this paper, sustainable energy systems in smart cities are evaluated by interval-valued Pythagorean fuzzy (IVPF) sets with an integrated optimization based multi-expert fuzzy Best Worst Method (BWM) and TOPSIS methodology that can better handle uncertainty and vagueness in experts’ linguistic assessments than existing methodologies. The considered criteria are weighted by multi-expert IVPF Best Worst Method, which has become a popular weighting method in recent years. Later, the energy alternatives for a real case study are prioritized by multi-expert IVPF TOPSIS method. In the analysis, the most important criterion is found as Environmental sustainability (C1) with the defuzzified weight of 0.218 while the other weights are as initial investment (C2) with 0.196, operating expenses (C3) with 0.163, technical feasibility (C4) with 0.154, social acceptability (C5) with 0.140, and scalability (C6) with 0.129. The obtained results indicate that “Investing in advanced technologies” in a smart city with relative degree of closeness (RDC) value of 0.798, has been determined as the best alternative among the considered five alternatives. It is closely followed by “Developing a transportation system” with the RDC value of 0.681. Sensitivity analysis shows that the ranking results are quite robust and reliable. The comparative analysis with crisp BWM and TOPSIS methodology is applied to check the validity of the proposed methodology.

Attributions of cause of oyster mortality on the British Columbia coast: Oyster growers' and scientists’ perspectives

Unexplained oyster mass mortalities threaten to destabilize the oyster industry on Canada's west coast if left unexplored. A range of potentially causative factors have been identified by the industry and the scientific community but the effects and magnitudes of these factors on oyster mortality risks remain unknown. Through a structured expert elicitation questionnaire, this study presents industry and scientific experts' calibrated judgements of cause of this multifactorial problem. We document low agreement among scientists and oyster growers with regards to important causative groups of factors. There is some agreement that factors like water temperature, seasonality, bacterial pathogens, affect oysters suspended in water and reared in trays, particularly if they are large oysters. However, both scientists and growers harbored differing views about the age and size of oysters susceptible to mass mortalities and the role of food availability and gametogenesis in contributing to mass mortalities. We discuss potential sources of these differing opinions and misconceptions. Our findings also highlight the need for additional research to resolve some of the uncertainty in experts' perceptions of cause. Risk communication efforts in the future should focus on these differing perspectives to work toward mutually viable decisions and solutions.

Integrated knowledge management in the supply chain: Assessment of knowledge adoption solutions through a comprehensive CoCoSo method under uncertainty

The natural dispersion of supply chain (SC) elements and the distribution of knowledge within these elements have necessitated an integrated knowledge management (KM) process in the SC. The use of KM poses many challenges for the members of the SC, and the key to the survival of the SC lies in identifying and solving these challenges. For this reason, this paper aims to appraise the challenges of knowledge adoption in the steel SC and proposes solutions to address these challenges. To determine the significance of challenges in the SC and rank the effective knowledge adoption solutions to address these challenges, a new method called Comprehensive CoCoSo (CoCoCoSo) is introduced based on the CoCoSo method. This method consists of three parts: weighting the challenges, weighting the experts, and ranking the solutions, respectively. In the first part, a new process for weighting the challenges based on CoCoSo is presented; in the second part, the CoCoSo method is enhanced by the ideal average concept for weighting the experts, and finally, a new prescription of CoCoSo is introduced in the ranking part of the knowledge adoption solutions. Also, type-2 fuzzy sets (T2FSs) are used to consider the uncertainty in experts' opinions. T2FSs are much stronger compared to classical fuzzy sets because they have fuzzy membership degrees and prepare more degrees of freedom to consider uncertainty. Then, the challenges in the steel SC are determined using experts' opinions, and then the importance of these challenges is assessed. Furthermore, solutions to face these challenges are outlined, and then using the CoCoCoSo method, the efficiency of solutions to solve the challenges is evaluated and ranked according to their efficiency. Among the proposed solutions, affirmative leadership for KM adoption in SC and creating reliable teamwork have been identified as the most effective solutions. Also, sensitivity analysis shows that the proposed method is sensitive to the weight of the challenges. Finally, the validity of the proposed method has been confirmed by comparing it with well-known methods in the literature. Additionally, using the different degree index, the superiority of the proposed method over well-known methods in the literature has been determined.

An integrated GBWM-PROMETHEE-CLOUD & MCGP model for green supplier selection and order allocation (GSSOA) in an oil refinery

With the increase of environmental concerns in recent years and the prevalence of such issues, which originate mainly from large industries and manufacturing plants, green supply chain management (GSCM) issues have been given special attention among researchers. GSCM is a group of management applications integrating supply chain management with criteria related to environment and supplier selection with the purpose of reducing environmental pollution caused by processes involved in a company supply chain. In this research the main contributions is to draw a decision-making structure addressing the applicability of the green supplier selection (GSS) and order allocation (OA) problem in an Iranian Oil refinery company. A combination of Cloud-PROMETHEE along with using GBWM was taken into account for GSS as the first phase and MCGP method for OA as the second phase, in order to solve the GSSOA problem. The proposed technique lets the vagueness and uncertainty of experts' judgments be obtained by assisting uncertain linguistic variable derived from the cloud model. Furthermore, the group best-worst method (GBWM) obtains the optimal criteria weights by forming a nonlinear programing model and the prioritizing of nominated suppliers is established using the PROMETHEE method with the possibility to pick a distinct preference function for each criterion and draw a graph of supplier preferences. At last, MCGP method was applied considering with the possibility of multi-choice aspiration levels (MCAL) for each goal. The suggested model illustrated with actual figures in an oil refinery to provide insight to decision-makers of green supplier selection and order allocation. To do so, initially nine criteria have been derived after vast research on literature and experts’ opinions. Then the suppliers was successfully evaluated with considering uncertainties and randomness in a group of decision maker judgments. The order allocation was also completed by minimizing total cost and delays and maximizing purchase using results of supplier ranking in the first phase. Results have shown that the supplier with better ranking in GSS is preferred in allocating orders. A sensitivity analysis then performed and confirmed that the suggested method is highly stable against different changes in the data. The proposed method was also compared with the basic MCGDM cloud model and the method is approved. Finally, managerial implications and conclusions for additional researches are demonstrated.

OP141 Expert Knowledge Elicitation in Health Technology Assessment: Our Experience Using the Sheffield Elicitation Framework

IntroductionExpert judgement has an important role in health technology assessment (HTA), including as a source of evidence to inform economic modeling when published data are lacking. Quantitative information may be elicited from experts to inform model inputs and associated uncertainty using one of many expert elicitation methodologies. Here, the feasibility and potential benefits of one expert elicitation method, the Sheffield Elicitation Framework (SHELF), to the HTA process is examined.MethodsThe SHELF method seeks to express the knowledge of multiple experts in the form of a subjective probability distribution. Eliciting a subjective probability distribution allows the uncertainty of experts to be included in probabilistic sensitivity analysis, which is becoming an increasingly prominent feature of HTAs. The individual knowledge of participating experts is combined through behavioral aggregation, where experts participate in a discussion before being asked to provide judgments from the perspective of a rational impartial observer. The whole process is led by a facilitator who ensures all participants contribute and confirm that the final distribution is a product of consensus, not compromise.ResultsWe recently conducted two SHELF elicitations as part of an ongoing project aiming to streamline the assessment of positron emission tomography (PET) in Australia. These elicitations provided insight into the usefulness of SHELF within the HTA setting. Given the constraints imposed by the COVID-19 pandemic, the elicitation sessions were conducted online rather than in the ideal face-to-face manner. In collaboration with one of the developers, we successfully adapted the method by making use of video conferencing technology to provide an online environment that mimicked the face-to-face setup as much as possible.ConclusionsSHELF provides a rigorous and scientific method by which to elicit the knowledge of multiple experts in the form of a probability distribution. However, the method is resource intensive and may be best reserved for when data on key drivers are lacking.

Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach

Water is a limited and invaluable resource that is essential for human survival. Negligence and unregulated water use have brought about a global water crisis. Proper management with a relevant decision and information integration approach can aid water to continue as a renewable resource. The water and wastewater industry must shift from outmoded, inefficient techniques to more sustainable, data-driven solutions to address water concerns and improve public health. The Internet of Things (IoT) has emerged as an innovative strategy for decision and information integration to drive an open-loop Water Value Chain (WVC) efficiently. The IoT-driven network allows objects to connect and communicate, gather data in real-time, analyze data and develop reasonable decision – making insights instantaneously. This study aims to find the enablers of IoT for an open-loop WVC. It examines 25 factors for IoT implementation in the open-loop WVC. The 25 factors are clustered into seven enablers using Principal Component Analysis (PCA). These principal components are analyzed by employing a Multi-Criteria Decision Making (MCDM) approach, i.e., the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which helps to find the cause-effect relationship to prioritize the enablers. The fuzzy set theory is used to address the uncertainty and vagueness in experts' opinions and data deficiency problems. The study reveals that the Ecosystem of an IoT network, IoT network configuration and adaptation and data mobility in an IoT network are the most prominent enablers to consider for the implementation of IoT in an open loop WVC. The study may be helpful for regulatory agencies and enterprises in water distribution and processing for identifying and prioritizing the potential enablers of IoT in an open-loop WVC.

Introduction of a new index to assess pavement functional condition

AbstractFor most countries, the construction of the road network is completed to a great extent. As a result, the road operators have focused on the management of the current road constructions. The pavement management systems have been the determinative answer to the above need finding the optimal and more efficient solution which minimizes the maintenance cost. In this context, the present paper aims at the development of an index that assesses sections of road networks based both on the pavement essential demands and surface characteristics. This project presents a model for the rating of highway sections taking into consideration criteria and sub-criteria related to the pavement function. The developed model has been formed based on the opinion of a group of experts. The technique of the fuzzy analytic hierarchy process (FAHP) has been used to mathematically analyze the above opinions and manipulate uncertainty and subjectivity. This method ends up by calculating the weights of significance which are the factors of the index equation. The index is composed in combination with the variables which have come up from the regular measurements of pavement characteristics assessing the pavement functional condition. The results are compared to those of the regular AHP highlighting the degree of influence by the uncertainty of experts’ judgments in some factors of the index. Furthermore, the suitability and ease in the fitting of the FAHP method are verified in pavement management problems. In this project data related to the checks on surface characteristics in the National Highway Network are displayed.

Probabilistic Interval-Valued Fermatean Hesitant Fuzzy Set and Its Application to Multi-Attribute Decision Making

It is difficult to describe the hesitation and uncertainty of experts by single-valued information, and the differences in the importance of attributes are often ignored during the decision-making process. This paper introduces the probability and interval values into Fermatean hesitant fuzzy set (FHFS) and creatively proposes the probabilistic interval-valued Fermatean hesitant fuzzy set (PIVFHFS) to deal with information loss. This new fuzzy set allows decision makers to use interval-valued information with probability to express their quantitative evaluation, which broadens the range of information expression, effectively reflects the important degree of different membership degrees, and can describe uncertain information more completely and accurately. Under the probabilistic interval-valued Fermatean hesitant fuzzy environment, several new aggregation operators based on Hamacher operation are proposed, including the probabilistic interval-valued Fermatean hesitant fuzzy Hamacher weighted averaging (PIVFHFHWA) operator and geometric (PIVFHFHWG) operator, and their basic properties and particular forms are studied. Then, considering the general correlation between different attributes, this paper defines the probabilistic interval-valued Fermatean hesitant fuzzy Hamacher Choquet integral averaging (PIVFHFHCIA) operator and geometric (PIVFHFHCIG) operator and discusses related properties. Finally, a multi-attribute decision-making (MADM) method is presented and applied to the decision-making problem of reducing carbon emissions of manufacturers in the supply chain. The stability and feasibility of this method are demonstrated by sensitivity analysis and comparative analysis. The proposed new operators can not only consider the correlation between various factors but also express the preference information of decision makers more effectively by using probability, thus avoiding information loss in decision-making progress to some extent.

A hybrid integrated multi-criteria decision-making approach for risk assessment: a study of automotive parts industry

PurposeThis paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.Design/methodology/approachTo address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.FindingsThis work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.Originality/valueThis research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

Evaluating the effects of uncertainty in interval pairwise comparison matrices

In the context of multicriteria decision analysis, Pairwise Comparison Matrices (PCMs) represent a widely-used mathematical structure to capture comparisons about criteria or alternatives. When a confidence (or uncertainty) level is associated to the provided comparison measures, PCMs are naturally extended to the so-called Interval Pairwise Comparison Matrices (IPCM). Classical approaches, based on the eigenvector associated to the largest eigenvalue of the matrix (Analytic Hierarchy Process) or optimization problems (e.g. the Logarithmic Least Squares approach), are frequently adopted in order to estimate absolute utilities from relative judgements in absence of expert's uncertainty. Conversely, in the presence of uncertain measures, computationally intensive approaches based on Monte Carlo analysis have been adopted. In this paper, starting from a given PCM, we provide an approach able to find the smallest multiplicative perturbations of its elements able to perturb the obtained ranking from the ordinal perspective. On the basis of such approach, we provide theoretical results which can be adopted also in the case of uncertain measures (e.g., IPCMs) with the aim to characterize the stability of the obtained ranking on the basis of the given uncertainty. As testified by numerical examples that conclude the paper, the proposed framework can greatly simplify the classical approaches by providing the possibility to make direct assumptions on the final outcome of Monte Carlo analysis and reducing or avoiding its required computational effort.

A hybrid multi-stage decision-making method with probabilistic interval-valued hesitant fuzzy set for 3D printed composite material selection

The 3D printed composite material selection is of great interest due to its extensive application prospect and can be considered as a challenging multiple-criteria decision making (MCDM) issue. The hesitation and uncertainty of experts are difficult to measure, and the high degree of interaction among criteria is often overlooked in the decision process. In addition, composites are required to serve in harsh environments for various mechanical and industrial fields, resulting in the degradation of mechanical properties. In this study, a hybrid multi-stage decision-making method is developed to conduct 3D printed composite material selection in harsh environments. The theory of probabilistic interval-valued hesitant fuzzy set (PIVHFS) is proposed to characterize the decision-making information of experts, which can effectively quantify the assessment in uncertain environments. An integrated method that combines Choquet fuzzy integral and Shapley value is proposed to obtain the weight vector of criteria, which can reflect the mutual influence between criteria and their overall importance. The final decision-making result and the optimal alternative can be calculated by the PIVHFS-based Tomada de Decisão Interativa Multicritério and Technique for order preference by similarity to an ideal solution (TODIM-TOPSIS) method. An empirical application, i.e., 3D printed composites in the background of automotive chassis, is applied to validate the application of the proposed method. Comparative analysis, sensitivity analysis, and managerial implications are also conducted to illustrate the validity of the method. This paper provides a valuable tool for addressing the material selection issue of 3D printed composites from a multi-criteria perspective.

Decision-making model for failure modes and effect analysis based on rough fuzzy integrated clouds

Risk assessment is one of the key topical subjects to identify and eradicate potential failures from system, design and structure at the early stage of production process. As different kinds of uncertainties such as diversity, randomness, vagueness coexist, single uncertainty based decision models are not sufficient to evaluate risk priority of failure modes. However, the existing mathematical approaches usually utilize single models ignoring the coexistence of intrapersonal and interpersonal uncertainties in experts’ judgements. The weights computed from relative importance of risk factors are essential information to capture the relation of various factors in decision system. Most of the current references do not consider the objective and subjective weights simultaneously which effects the rationality of results. In the present study, a novel linguistic assessment model , namely rough fuzzy integrated clouds (RFICs), is developed to handle uncertain information by integrating rough fuzzy numbers with cloud model theory. Then a significant decision support model for the analysis of failure modes is constructed based on RFICs, hybrid weighting method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The positive and negative ideal solutions are determined using statistical deviations and arithmetic operations of clouds to rank the failure modes. The proposed model is implemented on a real-world application to detect the failures of steam valve system in a power generation plant. The simulation and sensitivity of the proposed model is also analyzed for different variations of control parameters. The proposed approach is compared with four existing approaches to show its effectiveness, out-performance and improved accuracy.

Evaluating the Renewal Degree for Expressway Regeneration Projects Based on a Model Integrating the Fuzzy Delphi Method, the Fuzzy AHP Method, and the TOPSIS Method

As the volume and scale of urban expressways continue to increase, renewal remains a concern for urban development. The renewal and decision-making of an urban expressway need to be endowed with new concepts to adapt to the rapid development of cities. Nevertheless, in addition to considering road factors such as facility conditions, driving conditions, and environmental protection, the existing evaluation system lacks comprehensive consideration of factors that improve resilience and adapt to future urban development, and it lacks a quantifiable general update evaluation system. Thus, the establishment of a comprehensive renewal indicator system and a mixed evaluation framework is a challenge. This study proposes an evaluation framework of expressway renewal indicators that integrates the three dimensions of macro, meso, and micro based on the fuzzy Delphi method, the fuzzy AHP method, and the TOPSIS method. A q-rung orthopair fuzzy linguistic set was used to handle expert uncertainty information in the process of conducting fuzzy evaluations. The indicators were refined into general and quantifiable evaluation indicators to improve their versatility. Moreover, the renewal value of expressways was measured and calculated using the TOPSIS method, and four renewal intervals were divided according to the calculation results. As a result, 28 renewal indicators were screened out, and the five factors with the greatest impact on renewal were the demand for transport development, the renewal of facility and service functions, the upgrading of institutional resilience, structural renewal, and economic development. The model was applied to eight expressways in Shanghai to calculate the renewal degree value and divide the renewal status. The model could identify the renewal needs of each road to guide the renewal decision. This study proposes an evaluation model to measure urban expressway renewal studies and provides a reference for urban renewal in the area of sustainable development

Sustainable E-scooter parking operation in urban areas using fuzzy Dombi based RAFSI model

Over the last five years, e-scooters have gradually become commonplace in most urban areas. However, shortcomings in infrastructure provision, especially with parking, make it awkward to use these vehicles. This study presents a novel hybrid fuzzy multi-criteria decision-making model for determining optimal e-scooter parking locations by combining the Logarithmic Methodology of Additive Weights (LMAW) and the RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) methods. In the first stage, the fuzzy Aczel-Alsina function based LMAW approach is used to address the uncertainty of experts’ opinions in the decision process and to calculate the weights of the twelve criteria. Afterward, fuzzy Dombi based RAFSI is applied to obtain the ranking of e-scooter parking locations. A case study is presented to propose a solution for the operation of e-scooter parking by taking into consideration three different alternatives based on four different aspects and twelve criteria. According to the findings, the third option, which is a hybrid operation with geo-fencing hubs in primary catchment areas of public transportation, is the most practical choice for a sustainable operation of e-scooter parking. This option also has the potential to be the most environmentally friendly.

Expansion of fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator

Quantification in collective behavior and decision-making in fuzzy conditions is crucial to ensure the health and safety of the population. The task of modeling and predicting behavior in fuzzy conditions, as is known, has increased complexity due to a large number of factors from which an NP-complete multi-criteria problem is formed. There is a difficulty in quantifying the impact of fuzzy factors using a mathematical model. In this regard, the paper proposes a stochastic model of human decision- making to account for doubts and uncertainty of experts based on a modified aggregation operator. The developed fuzzy model combines fuzzy logic into a conventional model of social behavior. Unlike existing models and applications, this approach uses fuzzy sets and membership functions to describe the evacuation process in an emergency situation. The purpose of this work is to expand fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator and analysis of existing solutions. The problem statement in this paper is as follows: to form a set of fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator. The scientific novelty lies in the formation of a set of factors that form fuzzy rules for making dynamic decisions. The practical value of the work lies in the creation of fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator [1-3]. To implement the proposed model, the process of social behavior during evacuation, independent variables are determined. These variables include measurements related to social factors, in other words, the behavior of individual subjects and individual small groups, which are fundamental at an early stage of evacuation.

EFNC-Exp: An evolving fuzzy neural classifier integrating expert rules and uncertainty

Data stream classification processes with neuro-fuzzy approaches may involve situations where uncertainties arise, which may directly interfere with the quality of the results of the evolving models. Another factor that can help improve the performance of neuro-fuzzy evolving models is using a priori knowledge about a topic and incorporating it into the model's training procedure. The definition of fuzzy rules with a high degree of representativeness for certain classes can help models increase the significance of the representation of these labels and thus boost their predictive performance for these classes. This article proposes the integration of uncertainty in experts' feedback on the class labels and the integration of expert rules into the classifier architecture and the evolving, adaptive learning engine. This uncertainty integration occurs by combining it in defining neurons' weights in the first layer of the model and incorporating these weight values in the Gaussian neurons in the model's first layer; furthermore, uncertainty is integrated into an incremental feature weighting concept (inducing a weighted version of it) for the curse of dimensionality reduction. The proof of the new concepts will be carried out through tests on binary pattern classification problems in real-world data streams and a comparison between our approach and several related state-of-the-art works in evolving (neuro-) fuzzy modeling. The results obtained by the model showed that, by explicitly respecting the uncertainty of the class labels in the process of updating the evolving neuro-fuzzy classifier, the accuracy trend lines showed a robust behavior as the degree of distortion existing in the class labels of the samples due to uncertainty could be partially compensated.

Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults

BackgroundAchieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions.MethodsTo overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed.ResultsThe proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society.ConclusionsFrom a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.