Representation Of Uncertainty Research Articles

A review of Fuzzy Logic Analysis in COVID-19 Pandemic and a new technique through extended hexagonal intuitionstic fuzzy number in Analysis of COVID -19

Several intuitionistic fuzzy logic approaches have been used for the diagnosis of COVID-19 patients. We have developed a fuzzy rule base system for the detection of COVID-19 patients. In this study, we have considered six major parameters based symmetric/asymmetric, linear/non-linear hexagonal intuitionistic fuzzy numbers (HIFN) for the input-output factors of the problem. In real-life diagnosis problems, such as assessing COVID-19 symptoms, applying symmetric and asymmetric, linear and non-linear hexagonal intuitionistic fuzzy numbers allows for a more accurate representation of patient conditions. Centre of area method is used for the defuzzied value of the hexagonal intuitionistic fuzzy parameters. HIFN are used because they provide a detailed representation of uncertainty, incorporating both membership and non-membership degrees through six parameters. This flexibility allows for nuanced modelling of real-world scenarios, such as medical diagnoses, where data often includes ambiguity. Then the HIFN approach is used for obtaining the compromising and superlative solution in the diagnostic process of COVID-19 patients. To figure out the adaptability of the proposed HIFN based technique, a comparative study is also introduced. The originality, limitations, future aspects and advantages of using this HIFN based technique is also discussed in this article.

Optimization of autonomous vehicle control system reliability on a commercial scale through LIF dombi methodologies.

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Cyber risk management technology to strengthen the information security of the national economy

Purpose. Developing a technology for managing cyber risks based on their improved classification by the level of impact on the occurrence of an extreme situation. Methodology. To achieve the goal, general scientific and special methods of cognition were used in the study: dialectical and systemic approaches, analysis and synthesis, logical generalization and grouping, structural-logical method, iterative approach, modeling, method of formal representations of uncertainty. Findings. A cyber risk management technology has been developed, consisting of four main stages: analysis of cyber threats (context establishment; security audit; formation of scenario concepts); scenario modeling (threat decomposition; scenario formation; setting criteria; setting probability estimates of concepts (variables); building a network architecture; formation of a private threat model; scenario analysis); risk assessment; object classification. The proposed approach to cybersecurity risk management provides vulnerability detection and risk assessment (risk potential) and simplifies the development of management solutions to prevent events affecting cybersecurity. Originality. The proposed technology differs from the existing ones by focusing on identifying those vulnerabilities and cyber threats that, according to their improved classification by the level of impact on the occurrence of an extreme situation, can lead to serious disruptions in the functioning of critical information infrastructure of the national economy. Practical value. The practical significance of the study lies in the fact that the proposed cyber risk management technology is one of the tools for preventing the realization of risks in cyberspace and the basis for strengthening the information security of economic entities in particular and the national economy as a whole.

Recoverable Robust Shortest Path Problem Under Interval Budgeted Uncertainty Representations

ABSTRACTThis article deals with the recoverable robust shortest path problem under interval uncertainty representations. In this problem, a first‐stage path is computed, which can be modified to some extent after observing changes in the cost structure. The uncertain second‐stage arc costs are modeled by intervals, and the robust min–max criterion is used to compute an optimal solution. The problem is known to be strongly NP‐hard and also hard to approximate in general digraphs. However, until now its complexity for acyclic digraphs was unknown. In this article, it is shown that the problem in acyclic digraphs can be solved in polynomial time for the traditional interval uncertainty and all natural neighborhoods known from the literature. More efficient algorithms for layered and arc series‐parallel digraphs are constructed. Hardness results for general digraphs are also strengthened. Finally, some exact and approximate methods of solving the problem under interval budgeted uncertainty are proposed.

Using image-based inspection data to improve response predictions of earthquake-damaged unreinforced masonry buildings

AbstractExplicit representation of uncertainties is essential to improve the reliability of seismic assessments of earthquake-damaged buildings, particularly when dealing with unreinforced masonry buildings. Modern inspection techniques use images for detecting and quantifying the damage to a structure. Based on the principle of falsification, this paper evaluates how the use of information of damage that is obtained from images taken on earthquake-damaged buildings reduces the uncertainty when predicting the seismic response under a future earthquake. New model falsification criteria use information on the residual state of a building, such as shear cracks, residual roof displacements, and observation of out-of-plane failure. To demonstrate the effectiveness of these criteria in reducing the uncertainty in response predictions, results from a four-story unreinforced masonry building stiffened with reinforced concrete walls, which was experimentally tested under a sequence of ground motions, are assessed. Three commonly used modeling approaches (single degree of freedom (DOF) systems, multi DOF systems with four DOFs, and equivalent frame models) are used, where uncertainties in model parameters and model bias are included and propagated through the analysis. Out of the models used, and in the absence of any additional source of information, the proposed falsification criteria are most effective in connection with the equivalent frame model because this model can simulate the response at the element-level, while the simpler models can only represent the global response or the response at the storey-level. The results show that when using only the information on the presence of shear cracks, which might be the first and only source of information after an earthquake, the effectiveness of model falsification is increased, thus reducing the uncertainty in model parameter values and seismic response predictions through the use of image-based inspection.

A hybrid-fuzzy-decision-making framework for digital technologies selection

In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.

Extended Certainty Factors utilizing Neutrosophic Logic

Addressing uncertainty is a significant challenge in the field of artificial intelligence (AI). AI systems often encounter uncertainty due to incomplete data, ambiguous information, or inherent unpredictability in specific situations. To tackle this uncertainty, various solutions have been developed. These solutions range from probabilistic approaches like Bayesian networks and Monte Carlo methods to fuzzy logic and neural networks. These strategies enable AI systems to model and make judgments amid ambiguity by assigning probabilities, dealing with imprecise data, or utilizing learning processes that adapt to changing and uncertain contexts. However, probabilistic methods have limitations when it comes to handling uncertainty. These limitations include assumptions of independence, computational complexity, difficulty in capturing subjectivity, and interpretability. To address these limitations, other methods have been proposed, such as the model of certainty factors. This model offers a framework for reasoning under uncertainty by assigning a numerical value to statements or propositions. Additionally, neutrosophic logic extends classical logic by incorporating the concept of indeterminacy through truth/indeterminacy/falsity-membership functions. In this paper, we propose a method for investigating how certainty factors adapt to a neutrosophic environment. This method contributes to the development of more robust and adaptable decision support systems capable of dealing with diverse uncertainty. Our method, introduced for the first time in related literature, addresses issues such as the limited handling of indeterminacy, the inability to address contradictions, and the limited binary representation of uncertainty that characterize the model of certainty factors. We will provide a clear illustration of these implications through an example that demonstrates the superiority of our suggested method over the traditional technique of certainty factors.

Unveiling Uncertainty: Exploring the Potential of Neutrosophic Statistics for Artificial Intelligence

Artificial intelligence (AI) has revolutionized various fields, but its effectiveness can hampered by limitations in handling uncertainty inherent in real-world data. Traditional statistical methods often rely on binary logic (true/false) and may not adequately capture the nuances of uncertainty present in real-world data. This paper explores neutrosophic statistics (NS) as a novel approach to address uncertainty in AI applications. NS, introduced by Smarandache (1998), generalizes classical set theory by incorporating three truth-values: truth (T), indeterminacy (I), and falsity (F). This allows for a more nuanced representation of uncertainty compared to traditional binary methods. We discuss the limitations of classical statistics and how NS can overcome them. The paper explores the potential of NS for AI in areas like machine learning, data fusion, and Explainable AI (XAI). We propose research questions and methodology to investigate the effectiveness of NS integration with AI frameworks. The expected outcome is to contribute to the development of more robust and reliable AI systems by demonstrating the effectiveness of NS in handling uncertainty. Finally, we discuss future research directions to further explore the potential of NS for AI.

Fuzzy Sets and the Holographic Principle: Uncertainty in Black Hole Physics

Black hole physics, with its profound implications for gravity, spacetime, and the universe'sfundamental structure, remains a challenging field riddled with uncertainties. This study explores theintegration of fuzzy set theory and the holographic principle to address the inherent imprecision in blackhole parameter measurements and advance our understanding of these enigmatic cosmic entities.Hypothetical data sets, characterized by fuzzy set representations of uncertainties, are subjected tofuzzy set-based analysis. Fuzzy entropy calculations reveal the degree of imprecision, while fuzzyclustering suggests the potential existence of distinct black hole classes. This interdisciplinary approachprovides new theoretical insights, challenges existing models, and emphasizes the need for furtherresearch to validate and refine these concepts.

A Combined Scheme based on the Multiscale Stochastic Perturbed Parameterization Tendencies and Perturbed Boundary Layer Parameterization for a Global Ensemble Prediction System

Abstract Stochastic representations of model uncertainties are of great importance for the performance of ensemble prediction systems (EPSs). The stochastically perturbed parameterization tendencies (SPPT) scheme with a single-scale random pattern has been used in the operational global EPS of China Meteorological Administration (CMA-GEPS) since 2018. To deal with deficiencies in this operational single-scale SPPT scheme, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is developed. In the combined scheme, the mSPPT component aims to expand model uncertainties characterized by SPPT at mesoscale, synoptic scale, and planetary scale. The SPP-PBL component with six vital parameters is used to capture uncertainties in PBL processes, which is underrepresented by SPPT for the tapering treatment within PBL. Comparisons between the operational SPPT scheme and the mSPPT scheme reveal that the mSPPT scheme can generate more improvements in both ensemble reliability and forecast skills mainly in tropics. Besides, additional benefits from SPP-PBL on top of mSPPT are shown to be primarily distributed in tropics at the lower layers below 850 hPa and surface. Furthermore, the combined scheme of mSPPT and SPP-PBL is suggested to yield better spread–error relationships and forecast skills than the operational SPPT scheme in terms of objective verification scores for standard upper-air variables and surface parameters. A case study for the extreme precipitation event on 20 July 2021 in Henan Province of China also demonstrates the better ability of the combined scheme in forecasting the precipitation intensity and location. Significance Statement A comprehensive and reasonable representation of model uncertainties helps to improve the performance of ensemble prediction systems (EPSs). Despite the popular usage in simulating model uncertainties, the stochastically perturbed parameterization tendencies (SPPT) scheme possesses several shortcomings. To overcome this, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is proposed. Based on the global EPS of China Meteorological Administration (CMA-GEPS), additional benefits from the independent usage of mSPPT and SPP-PBL are disclosed. And the combined scheme can inherit the merits of mSPPT and SPP-PBL and generate more improvements on ensemble performance than the original single-scale SPPT scheme. This research provides a guideline for future upgradation of CMA-GEPS.

Suffering without Remedy: The Medically Unexplained Symptoms of Fibromyalgia Syndrome and Long COVID

The term “Medically Unexplained Symptoms” (MUS) describes chronic symptoms for which medical investigations fail to reveal a specific pathology or biomarker. Even as MUS are among the most prevalent chronic health problems in the global north, patients who experience them reside in a nebulous space. Such nebulousness is heightened for women patients. Moreover, women report MUS at higher rates than men. In this review essay, we analyze the medicalization and feminization processes vis-à-vis MUS by focusing on two particular syndromes: Fibromyalgia (FMS) and Long COVID (LC). FMS and LC present clear parallels that allow us to trace an unhappy marriage of women and MUS. We demonstrate how the medical constructions of these two syndromes as knowledge categories are representations of medical uncertainty vis-a-vis women patients. We then scrutinize the resulting gendered consequences of these categories for the illness experience. We conclude our review by calling for a cultural reorientation in our thinking about MUS that centers a recognition that the origins and manifestations of a great deal of human suffering reside outside of medicine’s ways of knowing. In so doing, we connect to foundational claims in medical anthropology and sociology; namely, that illness is more than disease, and health cannot be achieved primarily via biomedical means.

Fixed point results in intuitionistic fuzzy pentagonal controlled metric spaces with applications to dynamic market equilibrium and satellite web coupling.

This manuscript contains several new spaces as the generalizations of fuzzy triple controlled metric space, fuzzy controlled hexagonal metric space, fuzzy pentagonal controlled metric space and intuitionistic fuzzy double controlled metric space. We prove the Banach fixed point theorem in the context of intuitionistic fuzzy pentagonal controlled metric space, which generalizes the previous ones in the existing literature. Further, we provide several non-trivial examples to support the main results. The capacity of intuitionistic fuzzy pentagonal controlled metric spaces to model hesitation, capture dual information, handle imperfect information, and provide a more nuanced representation of uncertainty makes them important in dynamic market equilibrium. In the context of changing market dynamics, these aspects contribute to a more realistic and flexible modelling approach. We present an application to dynamic market equilibrium and solve a boundary value problem for a satellite web coupling.

Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

This work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

A Study on AG-Groups in Spherical Fuzzy Environment

An AG-group is an algebraic structure that satisfies the left invertive law and includes its left identity and inverse elements. This framework is naturally suited for representing and manipulating various real-life applications in civil engineering, information engineering, artificial intelligence, and more. In this paper, we present the structural application of AG-groups in data encryption and in the rotational transformation of water molecules. Additionally, numerous authors have explored fuzzy set theory over AG-group systems, yielding many valuable results. Spherical fuzzy set theory, an extension of fuzzy set theory, allows for the representation of uncertainty in decision-making, where there may be multiple possible choices or uncertainty about the degree of membership of an element in a set. However, no prior work has combined spherical fuzzy set theory with AG-groups, which motivates our study. In this paper, we apply spherical fuzzy set theory to AG-groups and introduce the concept of spherical fuzzy AG-subgroupoids. We provide the characterization of spherical fuzzy AG-subgroupoids over AG-groupoids and examine their related properties. As an application of spherical fuzzy sets over AG-groups, we develop the concept of normal spherical fuzzy groups over AG-groups and investigate their various properties.

Entropy measures of multigranular unbalanced hesitant fuzzy linguistic term sets for multiple criteria decision making

The hesitant fuzzy linguistic term set (HFLTS) is an efficient tool for modeling linguistic information in multi-criteria decision making (MCDM), and the entropy measure of HFLTS, as a substantial representation of uncertainty, merits additional investigation. This article aims to exploit a general framework to facilitate the construction of entropy measure for multigranular unbalanced HFLTS. An axiomatic definition of the entropy for HFLTSs that considers both types of uncertainty (fuzziness and hesitation) is presented, with the entropy measure subsequently derived from distance-based mapping. From this definition, several deduced results have been developed for the mapping that depicts the entropy expression in order to get such functions with ease. Whereafter, a MCDM weight-determining model for multigranular unbalanced linguistic information without preset weights is devised, and an empirical application of the suggested model in MCDM is illustrated. Ultimately, comparisons and analyses with existing studies are conducted to demonstrate the advantages of the proposed work.

A visual scoping review of how knowledge graphs and search engine results page designs represent uncertainty and disagreement

PurposeInformational conflict and uncertainty are common features across a range of sources, topics and tasks. Search engines and their presentation of results via search engine results pages (SERPs) often underpinned by knowledge graphs (KGs) are commonly used across tasks. Yet, it is not clear how search does, or could, represent the informational conflict that exists across and within returned results. The purpose of this paper is to review KG and SERP designs for representation of uncertainty or disagreement.Design/methodology/approachThe authors address the aim through a systematic analysis of material regarding uncertainty and disagreement in KG and SERP contexts. Specifically, the authors focus on the material representation – user interface design features – that have been developed in the context of uncertainty and disagreement representation for KGs and SERPs.FindingsSearches identified n = 136 items as relevant, with n = 4 sets of visual materials identified from these for analysis of their design features. Design elements were extracted against sets of design principles, highlighting tensions in the design of such features.Originality/valueThe authors conclude by highlighting two key challenges for interface design and recommending six design principles in representing uncertainty and conflict in SERPs. Given the important role technologies play in mediating information access and learning, addressing the representation of uncertainty and disagreement in the representation of information is crucial.

A comparative analysis of metaheuristic algorithms in interval-valued sustainable economic production quantity inventory models using center-radius optimization

The real-world production inventory systems involve uncertainties surrounding demand, production, defectiveness, and costs, which pose significant challenges. Various methodologies, including interval, fuzzy, stochastic, and fuzzy-stochastic approaches, have been developed to address these challenges. Among these, the interval approach offers a realistic representation of uncertainty. This study develops a green production model within an interval-based framework, incorporating interval-valued representations of defective rates and demand, which is also stochastic in nature. Differential equations governing inventory levels are formulated in an interval format and solved using advanced parametric techniques. The study extends to profit optimization within this interval-based framework, with the profit maximization problem transformed into a crisp form using interval order relations and center-radius optimization. The optimized solution is obtained through various metaheuristic algorithms. A numerical example validates the proposed model, and sensitivity analyses explore variations in different algorithms and inventory parameters. Additionally, statistical analysis using ANOVA tests is performed. This research contributes to production inventory management by providing a robust framework for handling uncertainty and optimizing performance in real-world scenarios.

New Modification of Ranked Set Sampling for Estimating Population Mean: Neutrosophic Median Ranked Set Sampling with an Application to Demographic Data

The study addressed the limitations of classical statistical methods when dealing with ambiguous data, emphasizing the importance of adopting neutrosophic statistics as a more effective alternative. Classical methods falter in managing uncertainty inherent in such data, necessitating a shift towards methodologies like neutrosophic statistics. To address this gap, the research introduced a novel sampling approach called “neutrosophic median ranked set sampling” and incorporated neutrosophic estimators tailored for estimating the population mean in the presence of ambiguity. This modification aims to address the inherent challenges associated with estimating the population mean when dealing with neutrosophic data. The methods employed involved modifying traditional ranked set sampling techniques to accommodate neutrosophic data characteristics. Additionally, neutrosophic estimators were developed to leverage auxiliary information within the framework of median-ranked set sampling, enhancing the accuracy of population mean estimation under uncertain conditions. The methods employed involved modifying traditional ranked set sampling techniques to accommodate neutrosophic data characteristics. Bias and mean squared error equations for the suggested estimators were provided, offering insights into their theoretical underpinnings. To illustrate the effectiveness and practical applications of the proposed methodology and estimators, a numerical demonstration and simulation study have been conducted using the R programming language. The key results highlighted the superior performance of the proposed estimators compared to existing alternatives, as demonstrated through comprehensive evaluations based on mean squared error and percentage relative efficiency criteria. The conclusions drawn underscored the effectiveness of the neutrosophic median ranked set sampling approach and suggested estimators in estimating the population mean under conditions of uncertainty, particularly when utilizing neutrosophic auxiliary information and validated real-life applicability. The methodology and estimators presented in the study were shown to yield interval-based results, providing a more realistic representation of uncertainty associated with population parameters. This interval estimation, coupled with minimum mean squared error considerations, enhanced the efficacy of the estimators in determining population mean values. The novelty of the work lies in its introduction of a tailored sampling approach and estimators designed specifically for neutrosophic data, filling a significant gap in the literature. By extending classical statistics to accommodate ambiguity, the study offers a substantial advancement in statistical methodology, particularly in domains where precise data is scarce and uncertainty is prevalent. Furthermore, the empirical validation through numerical demonstrations and simulation studies using the R programming language adds robustness to the proposed methodology and contributes to its practical applicability.

Updating local and global probabilities during maze navigation.

We examined the human ability to encode and utilize local and global uncertainty information during a navigational task. Participants were tasked with navigating a virtual maze in which wall locations were obscured. Local cues and a global direction provided guidance. The validities of the global and local cues were separately and jointly varied across the two experiments. The results demonstrated that participants effectively utilized both global and local cues for navigation with a stronger reliance on local cues and a heightened precision in estimating their reliability. Our findings suggest that the representation of uncertainty for proximate events can be dissociated from that of distal events. Furthermore, humans effectively integrate both forms of information when making decisions during navigation tasks. This research advances our understanding of uncertainty processing and its implications for decision making in complex environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Robust optimization approaches in inventory management: Part B - the comparative study

This comparative study, constituting Part B of our extensive investigation into Robust Optimization (RO) in inventory management, builds on the foundational insights from Part A’s survey. It conducts a thorough analysis of various RO formulations and algorithms, emphasizing their practical application, efficacy, and computational considerations in inventory management contexts. This study is meticulously structured to address the pivotal inquiries identified in Part A, encompassing the effective representation of uncertainty, the selection of optimal decision criteria, the influence of decision rules on inventory management performance, computational challenges, and the adaptability of these methods to evolving technological and market conditions. This research juxtaposes theoretical findings with empirical data, offering a comprehensive evaluation of the strengths, limitations, and practical implications of each robust inventory model discussed in the literature. This investigation not only complements the survey in Part A but also serves as a standalone, in-depth contribution to robust inventory management.