Vague Sets Research Articles

Neutrosophic Logic - A Better Approach to Execute Imprecise Queries Through the Neutrosophic Database Model

Objectives: The neutrosophic set has a more powerful ability to process imprecise information compared to the vague set. The objective of the authors is to process the uncertain query using vague as well as neutrosophic logic and compare the result. Methods: Authors have designed a new architecture to process uncertain queries using vague as well as neutrosophic sets. In the architecture at first, the imprecise query has been taken as input. From the inputted imprecise query uncertain data and fuzzy attributes are identified. Next, using the truth membership algorithm the truth membership value between uncertain data and the fuzzy attribute value for each tuple is calculated. This truth membership value is used to obtain the false and indeterminacy membership value to represent the domain of fuzzy attribute in vague or neutrosophic form. Then, the tolerance membership value of each tuple corresponding to the imprecise query is determined by using the similarity measures formula for vague as well as neutrosophic set. Findings: Using the truth membership algorithm and similarity measures formula, the tolerance membership value for each tuple of the relation corresponding to an imprecise query is obtained. If an imprecise query consists of more than one fuzzy attribute then the fuzzy intersection is used to obtain the membership value of the tuple. After obtaining the tolerance membership value of each tuples, the decision maker will provide a threshold (α-cut) value based on which an SQL is generated which fetched a set of tuples whose membership value meets the threshold (α-cut) value for vague as well as neutrosophic set. Authors have considered a patient database to process uncertain queries using a neutrosophic set and claimed neutrosophic set gives better results compared to vague. Novelty: The novelty of this work is to train the computer using a soft set to obtain desired outcomes while processing imprecise query in the database. Keywords: Fuzzy data, Vague set, Neutrosophic Set, Similarity Measures, α -Equal neutrosophic data

More on the Vague Complex Subhypergroups

The concept of a vague complex set provides a more comprehensive perspective than that of a vague set. This article aims to investigate vague complex and anti-vague complex subhypergroups (Hv−subgroups), supported by various examples with the help of vague complex sets and hyperstructures. Moreover, we explore their properties and relationships with vague and anti-vague subhypergroups.

A novel sequential risk assessment model for analyzing commercial aviation accidents: Soft computing perspective.

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.

Linear Consensus Protocol Based on Vague Sets and Multi-Attribute Decision-Making Methods

This paper proposes a linear consensus protocol QuickBFT based on Vague sets and multi-attribute decision-making methods. QuickBFT simplifies the communication process based on the HotStuff protocol, reduces the four-stage communication to three-stage communication, and reduces the consensus delay. Furthermore, we introduce the Vague set and multi-attribute decision-making theory into the consensus protocol and propose a new leader node selection algorithm, which can prevent Byzantine nodes from becoming leader nodes, thereby improving the protocol performance when the leader node is attacked. Experimental results show that the throughput of QuickBFT is slightly higher than that of the HotStuff protocol without Byzantine nodes, and the consensus delay is reduced by 20%. In the presence of Byzantine nodes, the throughput of QuickBFT is increased by 80% compared with the HotStuff protocol, and the consensus delay is reduced by 60%.

Research on the Level of Synergistic Development of Supply and Demand in China’s Health Industry

This study combines the latest vague set similarity measurement method with the entropy weight TOPSIS method; furthermore, it applies the grey relational analysis method to establish a model for the synergistic development of supply and demand in the health industry, and it empirically analyzes the development status, synergy level, and synergistic development level of China’s health industry supply and demand from 2013 to 2021. The findings reveal that: (1) China’s health industry supply maintains steady growth, while demand grows rapidly. However, the overall level of supply and demand development is generally low, indicating significant potential for future development. Moreover, the development of the supply lags behind that of the demand, leading to an increasing gap year by year. (2) The level of synergy between supply and demand in China’s health industry is relatively high but is decreasing year by year. Although the degree of synergistic development between supply and demand continues to increase, it remains at a low level, indicating that high coordination between supply and demand comes at the cost of a low development level. (3) There exists a significant regional imbalance in the development of China’s health industry, with the overall difference showing a widening trend year by year. Regional disparities are the main source of the overall difference.

Correction: Endorsers measurement for decarbonised processed food supply chain through newly befitted interval valued neutrosophic vague sets

Correction: Endorsers measurement for decarbonised processed food supply chain through newly befitted interval valued neutrosophic vague sets

OSFS‐Vague: Online streaming feature selection algorithm based on vague set

AbstractOnline streaming feature selection (OSFS), as an online learning manner to handle streaming features, is critical in addressing high‐dimensional data. In real big data‐related applications, the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments. However, existing OSFS methods rely on presented and fixed hyperparameters, which undoubtedly lead to poor selection performance when encountering dynamic features. To make up for the existing shortcomings, the authors propose a novel OSFS algorithm based on vague set, named OSFS‐Vague. Its main idea is to combine uncertainty and three‐way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous method. OSFS‐Vague also improves the calculation method of correlation between features and labels. Moreover, OSFS‐Vague uses the distance correlation coefficient to classify streaming features into relevant features, weakly redundant features, and redundant features. Finally, the relevant features and weakly redundant features are filtered for an optimal feature set. To evaluate the proposed OSFS‐Vague, extensive empirical experiments have been conducted on 11 datasets. The results demonstrate that OSFS‐Vague outperforms six state‐of‐the‐art OSFS algorithms in terms of selection accuracy and computational efficiency.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

An extension of several properties for fuzzy t-norm and vague t-norm

Rosenfeld defined a fuzzy subgroup of a given group as a fuzzy subset with two special conditions and Mustafa Demirci proposed the idea of fuzzifying the operations on a group through a fuzzy equality and a fuzzy equivalence relation. This paper mainly focuses on fuzzy subsets and vague sets of monoids with several extended algebraic properties. Firstly, we generalize some algebraic properties of t-norms to fuzzy t-norms, this allows for a broader analysis and classification of fuzzy t-norms, enabling their wider application. Furthermore, we explore specific research on the properties of vague t-norms. Finally, selected conclusions about fuzzy t-norms are extended to bounded lattices.

Logarithmic Pythagorean neutrosophic vague aggregating operators and their real-life applications

This article examines Pythagorean neurosophic vague set (PyNVS) problems relevant to multiple attribute decision-making (MADM). Pythagorean vague set (PyVS) and neutrosophic set (NS) can be generalized into Pythagorean neutrosophic vague set (PyNVS). We discuss log Pythagorean neutrosophic vague weighted averaging (log PyNVWA), logarithmic Pythagorean neutrosophic vague weighted geometric (log PyNVWG), log generalized Pythagorean neurosophic vague weighted averaging (log GPyNVWA) and log generalized Pythagorean neutrosophic vague weighted geometric (log GPyNVWG). In this article, we define the Euclidean distance (ED), Hamming distance (HD), operator laws, and flowchart using an algorithm. By analyzing log PyNVS through algebraic operations, we discuss its properties. They can identify the best option more quickly and understand the practicalities better. An illustrative example of this is the fusion of computer science and machine tool technology in agriculture. Furthermore, there are autonomous robot tractors and soil sterilization robots that can harvest crops, weed, and take photos of seed planting with seedlings. A random selection of five farmers (alternatives) has been made. Climate, water, soil, disease, and flooding are all criteria to consider when choosing a farmer. Our goal is to narrow down the options by comparing expert judgments with the criteria.

T-Spherical Fuzzy-Valued Neutrosophic Set Theory

From fuzzy to neutrosophy, multiple hybrid models have been innovated, with each introduced model surpassing its predecessor. Due to the inherent indeterminacy in the world, a more precise form of imprecision is required. As a result, more sophisticated variants of the neutrosophic set have been created. Examples of these amalgamations include fuzzy neutrosophic sets, intuitionistic fuzzy neutrosophic sets, Pythagorean fuzzy neutrosophic sets, neutrosophic vague sets, and neutrosophic rough sets. The main objective of this paper is to present another variant of the neutrosophic set called T-spherical fuzzy-valued neutrosophic set (T-SFVNS). Serving as a generalization of the aforementioned combinations, T-SFVNS allows for the representation of indeterminacy and inconsistency in a more nuanced manner. In this paper, we define the T-SFVNS and Tspherical fuzzy-valued neutrosophic numbers (T-SFVNNs). Additionally, we propose several types of score and accuracy functions to compare the T-SFVNNs. We also present the basic operations of T-SFVNSs and the algebraic operations of T-SFVNNs, supported by proofs and illustrative examples..

New algebraic approach towards interval-valued neutrosophic cubic vague set based on subbisemiring over bisemiring

We introduces the concept of a interval-valued neutrosophic cubic vague subbisemiring (IVNCVSBS), level sets of IVNCVSBS of a bisemiring. IVNCVSBSs are the new extension of neutrosophic subbisemirings and SBS over bisemirings. Let be a neutrosophic vague subset in $X$, we show that is a IVNCVSBS of X if and only if all non empty level set is a SBS of X. Let be a IVNCVSBS of a bisemiring X and strongest cubic neutrosophic vague relation of X, we prove that is a IVNCVSBS of X × X. Let be any IVNCVSBS of X, prove that pseudo cubic neutrosophic vague coset is a IVNCVSBS of X. Let 1, 2,...,n be the family of IVNCVSBS of X1, X2,..., Xn respectively.The homomorphic image of every IVNCVSBS is a IVNCVSBS. The homomorphic pre-image of every IVNCVSBS is a IVNCVSBS. Examples are provided to strengthen our results.

Effective vague soft environment-based decision-making

<abstract><p>The soft idea has a crucial impact on facing uncertainty, but extending soft, fuzzy soft, and vague soft settings is more comprehensive in expressing the problem ambiguity parameters than the soft set. Despite this advancement, the vague soft set falls short in addressing certain decision-making issues. This occurs when we have some external factors that can impact our final decision. These external factors can be represented in the effective parameter set. So, the objective of the current article is to incorporate the new concept of effectiveness into the concept of vague sets. This approach leads the researchers to create a novel and expanded framework for effective decision-making, surpassing any previously introduced methods in applicability. The article also provides an exploration of the types, concepts, and operations of effective vague soft sets, each illustrated with examples. Furthermore, the study delves into the examination of properties like De Morgan's laws, distributive, commutative, absorption, and associative properties for those new sets. Moreover, the framework of effective vague soft sets is used to develop a decision-making methodology. This technique simplifies the process of determining whether a student meets the requirements for a particular level of education or if a patient has a specific disease, among other applications. Additionally, to clarify the proposed algorithm, a detailed case study representing how to classify students toward specializations is examined in detail. Using matrix processes in this example, in addition to $ Wolfram \; Mathematica^{\circledR} $, not only renders computations simpler and quicker but also results in more precise optimum effective decisions. In the end, a detailed comparison with existing techniques is performed and summed up in a chart to illustrate the difference between them and the present one.</p></abstract>

Selection process based on new building construction work using square root vague sets and their aggregated operators

This study discusses novel approaches for solving multi-attribute decision-making problems using logarithmic square root vague sets. Logarithmic square root vague sets can be considered as an extension of vague sets and square root fuzzy sets provides a more profitable method to express the uncertainties in the data to deal with decision making problems. We developed some novel logarithmic square root vague sets, which can overcome the weaknesses of algebraic operations and capture the relationship between various square root vague sets and vague sets. The purpose of this study is to examine several aggregation operators using logarithmic square root vague sets. The weighted logarithmic averaging distance and weighted logarithmic geometric distance operators are distance measure that is based on aggregating model. We discuss the concepts of logarithmic square root vague weighted averaging, logarithmic square root vague weighted geometric, generalized logarithmic square root vague weighted averaging and generalized logarithmic square root vague weighted geometric operators. We communicate that algebraic structures such as associative, distributive, idempotent, bounded, commutativity and monotonicity properties are satisfied by logarithmic square root vague numbers. We discusses some mathematical properties of logarithmic square root vague sets, as well as the Hamming distance and Euclidean distance measures. It measures the similarity of two strings of information by measuring the Hamming distance. An analysis of the proposed operators is presented, along with alternative formulations and families, as well as their main properties. The weighting vector is characterized by multiple classical measures and we propose alternative solutions for dealing with the logarithmic properties of the operators. As a result of studying the weighting vectors of the operators, we present generalizations of them. We examine vague sets with important properties using algebraic operations in more detail. Furthermore, we develop an algorithm to solve multi-attribute decision-making problems using these operators. We provide real-life examples to illustrate how enhanced Euclidean distances, Hamming distances and score values can be applied in real-world situations. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method, and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. Based on the expert assessments, we compared the criteria with the most appropriate options.

A Vague Rough Set Approach for Failure Mode and Effect Analysis in Medical Accident

Identifying the risk factor is a key to success for the Failure model. This paper presents a new FMEA (Failure Mode and Effect Analysis) in medical accident for the hospital management by using vague rough matrix information captured through vague rough set. Using β-product and β-complement product into fuzzy relation matrix and vague set to find the new lower and upper approximation of vague rough set. This method will provide an easy methodology to find the weight of risk factor. Using an example we validate our proposed approach.

A reflection on Springer Nature’s current editorial policies related to “predatory” journals and references

Despite its prominent position as one of the leading for-profit scholarly publishers of subscription and open access (OA) journals, and its membership of the Committee on Publication Ethics (COPE), Directory of Open Access Journals (DOAJ), and Open Access Scholarly Publishers Association (OASPA), Springer Nature can be criticized for taking a vague approach in its editorial policies pertaining to “predatory” publishing. While cautioning authors and/or editors about the citation of papers that are published in “predatory” journals, the advice presented in the policies itself is flawed due its limitation to OA journals – thus apparently excluding the possibility that subscription journals might also be “predatory” and failing to specify precisely which journals authors and editors should be careful of, i.e., no source for the “predatory” definition of OA journals is indicated. Moreover, this vague set of policies does not have a publication date, nor is any authorship specified. The opacity of these aspects of these editorial policies limits their usefulness and weakens their objectives, namely in ostensibly offering scholarly advice to protect editors and authors. I argue that the Springer Nature policies pertaining to “predatory” publishing as they currently stand are not useful, and if left unchanged, can be a source of confusion or error for authors and editors of Springer Nature journals. In this connection, the identified risk is that if the poor advice indicated in those policies is heeded, it may encourage abuse, insults and/or indiscriminate profiling of academics, including those associated with Springer Nature journals. While assuming Springer Nature’s genuine intention to alert editors and authors against citing papers published in suspect scholarly venues, I argue that this editorial set of policies should either be rewritten to rely on clearer parameters, including a definitive list of journals for which “predatory” criteria have been defined, or abandoned as they are overly broad. The criticism is aimed at stimulating a discussion among other editors and publishers, especially COPE members.

Similarity Measure of Plithogenic Cubic Vague Sets: Examples and Possibilities

The crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets are the extension of the plithogenic set, in which elements are characterized by the number of attributes and each attribute can assume many values. To achieve more accuracy and precise exclusion, a contradiction or dissimilarity degree is specified between each attribute and the values of the dominating attribute. A plithogenic cubic vague set is a combination of a plithogenic cubic set and a vague set. The key tool for resolving problems with pattern recognition and clustering analysis is the similarity measure. In this research, we characterize and investigate the similarities between two Plithogenic Cubic Vague sets (PCVSs) for (z≡F), (z≡IF) and (z≡N). Also, examples are given to examine similarities in the pattern recognition application problems.

Analysis of the quality, source identification and apportionment of the groundwater in a typical arid and semi-arid region

A detailed and thorough study of the quality and its distribution, source identification and apportionment of groundwater, as the major or even sole selection of clean water in Songyuan City (a typical arid and semi-arid district in China), is needed for local sustainable development. The groundwater quality was evaluated using a vague set (VS)-technique for order of preference by similarity to ideal scheme (TOPSIS) model to determine its contamination status. Additionally, to explore the source and quantitative analysis of the driving factors of the groundwater chemistry in Songyuan City, a hydrochemical graphical method and a Principle Component Analysis (PCA) and Positive Matrix Factorization (PMF) model were employed. Regarding the quality of the groundwater in Songyuan, 74.5% of the samples were categorized into Grade I–III (Excellent, Good, and Moderate) water, indicating that the majority of the groundwater can be used directly. Moreover, the confined water tended to be cleaner and more appropriate for use in comparison with the phreatic water in Songyuan City. The PCA and PMF model and hydrochemical graphs demonstrated water–rock interactions and the evaporation process acted as the main factor, which was followed by anthropogenic activities as they contributed 50.1% and 20.4% of the total, respectively. Besides, the fluoride and iron contamination mainly caused by geogenic processes cannot be ignored as they were identified as the two driving factors that controlled the water quality, accounting for 20.2% and 9.3%, respectively. The mutual influence of the natural geological processes and human interventions contribute to the groundwater deterioration in Low Plain. Furthermore, the inappropriate well-forming techniques (without water-stop processes) in Qianan County was the main reason for the low-quality of deep Neogene confined water. This research provides useful information and directions for the local government to carry out corresponding and effective groundwater remediation strategies.

Linear-combined rough vague sets and their three-way decision modeling and uncertainty measurement optimization

Linear-combined rough vague sets and their three-way decision modeling and uncertainty measurement optimization

Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource Scheduling Optimization.

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.