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In literature, several models which can handle uncertainty in datasets have been introduced. Fuzzy set introduced by Zadeh in 1965, is one of the earliest such models and Atanassov generalised it by introducing the notion of Intuitionistic fuzzy sets(IFS) in 1986. However, these models are handicaped due to their inadequacy as parameterization tools. The notion of Soft sets (SS) was introduced by Molodtsov in 1999 to solve this problem. Almost at the same time, Neutrosophic set (NS) model was introduced by Smarandache, which is a huge generalisation of IFS. As has been the practice, the hybrid model of SS and NS was proposed to frame the notion of Neutrosophic Soft Set (NSS) by Ali and Smaranche in 2015 and studied their properties. Since its inception, one of the major areas of application of Soft Sets has been that of Multi-criterian Decision Making (MCDM). Many problems in MCDM were solved by using hybrid models of SS. Following this trend, in this paper, we develop an algorithm basing upon NSS to handle the problem of MCDM in the selection of faculty through an interview prcess. For this, we had to introduce an improved score function which is used to rank the candidates basing upon several of their characteristics including interview perfromances. This application is better handled by the NSS model as is evident from the results. We illustrated the superiority of our proposed algorithm by providing a comparative analysis with many exieting algorithms in the literature.

‎After the introduction of the concept of fuzzy sets‎ ‎by Zadeh‎, ‎several researches were conducted on‎ ‎the generalizations of the notion of fuzzy sets‎. ‎There are many viewpoints on the notion of metric space in fuzzy topology‎. ‎One of the most important problems in fuzzy topology is obtaining an appropriate concept of fuzzy metric space‎. ‎This problem has been investigated by many authors from different points of view‎. ‎Atanassov gives the concept of the intuitionistic fuzzy set as a generalization of the fuzzy set‎. ‎Park introduced the notion of intuitionistic fuzzy metric space as a natural generalization of fuzzy metric spaces due to George and Veeramani‎. ‎This paper introduces the concept of intuitionistic fuzzy modular space‎. ‎Afterward‎, ‎a Hausdorff topology induced by a $\delta$-homogeneous intuitionistic fuzzy modular is defined and some related topological properties are also examined‎. ‎After giving the fundamental definitions and the necessary examples‎, ‎we introduce the definitions of intuitionistic fuzzy boundedness‎, ‎intuitionistic fuzzy compactness‎, ‎and intuitionistic fuzzy convergence‎, ‎and obtain several preservation properties and some characterizations concerning them‎. ‎Also‎, ‎we investigate the relationship between an intuitionistic fuzzy modular and an intuitionistic fuzzy metric‎. ‎Finally‎, ‎we prove some known results of metric spaces including Baire’s theorem and the Uniform limit theorem for intuitionistic fuzzy modular spaces‎.

The aim of the paper is to extend the ARAS (Additive Ratio ASsessment) strategy to the single-valued pentapartitioned neutrosophic number environment which we call the SVPNN-ARAS strategy. The single-valued pentapartitioned neutrosophic number is the extension of fuzzy number and neutrosophic number. It comprehensively deals with uncertainty as it replaces indeterminacy with three independent entities, namely, contradiction, ignorance, and unknown. To develop a decision-making strategy, the arithmetic averaging operator for pentapartitioned numbers is defined and its basic properties are established. Single valued pentapartitioned number is a suitable mathematical tool to deal with uncertainty comprehensively. The SVPNN-ARAS strategy effectively evaluates and ranks feasible alternatives. In this paper, the ARAS strategy for multi-criteria group decision-making in a pentapartitioned neutrosophic number environment is developed. To demonstrate the applicability of the proposed strategy, a green supplier selection problem is solved and sensitivity analysis is performed to reflect the impacts of weighting of the decision makers and criteria on ranking the alternatives.

ABSTRACTThe rapid expansion of extensive brackish water aquaculture (BA) in Indonesia has created an urgent need to develop effective and reliable methods to evaluate and select sites suitable for aquaculture development. The lack of supporting spatial data at appropriate scales has limited the application of GIS-based multi-criteria evaluation methods (MCEs) in Indonesia. This study presents alternative fuzzy-based Geographic Information System (GIS) methods to evaluate and select sites suitable for extensive brackish water aquaculture. This study successfully produced fuzzy set maps from a water availability sub-model, a land conversion sub-model and a green belt buffer zone sub-model. With grades of membership, the fuzzy set maps provide smooth class boundary representation, which creates more options for decision-making than a map classified with crisp logic. Combining these sub-models produced an overall site suitability map at the scale of 1:50,000 for the study region. This final suitability map effectively excluded more than 95% of the unsuitable area for BA. This broad-scale site suitability assessment approach is a helpful planning tool to promote Indonesia’s sustainable development of extensive brackish water aquaculture. It identifies possible conflicts in land uses and considers conservation issues early in the planning process. The output can be used to scope research areas for more detailed investigations in countries where BA is a vital livelihood.

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method.

This paper’s findings are related to geometric function theory (GFT). We employ one of the most recent methods in this area, the fuzzy admissible functions methodology, which is based on fuzzy differential subordination, to produce them. To do this, the relevant fuzzy admissible function classes must first be defined. This work deals with fuzzy differential subordinations, ideas borrowed from fuzzy set theory and applied to complex analysis. This work examines the characteristics of analytic functions and presents a class of operators in the open unit disk Jη,ςκ(a,e,x) for ς>−1,η>0, such that a,e∈R,(e−a)≥0,a>−x. The fuzzy differential subordination results are obtained using (GFT) concepts outside the field of complex analysis because of the operator’s compositional structure, and some relevant classes of admissible functions are studied by utilizing fuzzy differential subordination.

Most populated developing countries having water resources, like Egypt, are interested in aquaculture since it supplies around 30% of the cheap protein consumed by customers. Increasing the production of aquaculture, specifically fish farming, in such countries represents an essential need. One candidate water resource for freshwater fish farming in Egypt is the Nile River (1530 km long). Yet, this represents a challenging task due to the existing variations in its water quality (WQ) parameters, such as dissolved oxygen, acidity, and temperature, at different sites. Climate change and pollution negatively affect many water quality parameters. This work provides a fuzzy-based approach for modeling WQ requirements for a set of fish types and evaluates the suitability of a water site for farming them. Thus, it greatly helps managing and planning fish farming in a set of water sites. It benefits from the flexibility of fuzzy logic to model the farming requirements of each fish type. Consequently, it evaluates and clusters the water sites with respect to their degrees of suitability for farming various fish types. The illustrative case study considers 27 freshwater sites spread along the Nile River and 17 freshwater fish types. The result incorporates a set of suitable clusters and a set of unsuitable ones for farming each fish type. It greatly helps managing and planning fish farming, to maximize the overall productivity and prevent probable catastrophic damage. In addition, it shows how to enhance each unsuitable site. We believe that eliminating the causes of pollution in the polluted freshwater sites along a water source could cause a significant boom in the cultivation of multiple freshwater fish types.

A method of solution of antagonistic games in violation of a common knowledge principle is described, wherein players have incomplete knowledge of possible solutions and appropriate outcomes for the opposite side. As a formal model of for a game situation, it is proposed to use a fuzzy-multiple representation of estimates of the possibilities of using players' strategies and the corresponding consequences. The solution for this problem is based on the transformation of the fuzzy estimates of the results of possible solutions for each situation in the form of an equivalent fuzzy set that has a triangular membership function. The developed method does not impose restrictions on the affiliation functions of the initial fuzzy data. In addition to the selection of the best solution, an estimation of the result and the degree of feasibility is obtained. [ABSTRACT FROM AUTHOR]

We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios. [ABSTRACT FROM AUTHOR]

In literature, several models which can handle uncertainty in datasets have been introduced. Fuzzy set introduced by Zadeh in 1965, is one of the earliest such models and Atanassov generalised it by introducing the notion of Intuitionistic fuzzy sets(IFS) in 1986. However, these models are handicaped due to their inadequacy as parameterization tools. The notion of Soft sets (SS) was introduced by Molodtsov in 1999 to solve this problem. Almost at the same time, Neutrosophic set (NS) model was introduced by Smarandache, which is a huge generalisation of IFS. As has been the practice, the hybrid model of SS and NS was proposed to frame the notion of Neutrosophic Soft Set (NSS) by Ali and Smaranche in 2015 and studied their properties. Since its inception, one of the major areas of application of Soft Sets has been that of Multi-criterian Decision Making (MCDM). Many problems in MCDM were solved by using hybrid models of SS. Following this trend, in this paper, we develop an algorithm basing upon NSS to handle the problem of MCDM in the selection of faculty through an interview prcess. For this, we had to introduce an improved score function which is used to rank the candidates basing upon several of their characteristics including interview perfromances. This application is better handled by the NSS model as is evident from the results. We illustrated the superiority of our proposed algorithm by providing a comparative analysis with many exieting algorithms in the literature. [ABSTRACT FROM AUTHOR]

Many problems of practical interest can be modeled and solved by using interval-valued fuzzy graph (IVFG) algorithms. An IVFG is a very useful and effective tool for studying various calculations, fields of intelligence, and computer science, such as networking, imaging, and other fields, such as biological sciences. In different applications, they present an appropriate construction means. There were limitations in the definition of fuzzy graphs (FGs), which prompted us to propose a new definition for IVFGs. Some interesting properties related to the new IVFGs are investigated, and enough conditions under which the level graph on IVFGs is equivalent are obtained. Therefore, in this study, we present the properties of a level graph (LG) of an IVFG, and four operations, the Cartesian product (CP), composition (CO), union, and join, are investigated on it. Today, in a treatment system, one of the issues that can be very valuable and important to the quality of service to patients is finding qualified and efficient people in each department, which is not an easy task. But the interval-valued fuzzy graph, as an important fuzzy graph, can help us by considering the ability of each person in the form of intervals of numbers and the effectiveness of each one on the other (according to the relationships between them) in order to find the most worthy people. So, an application of IVFG to find the most effective person in a hospital information system has been introduced. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce the notion of fuzzy Sβ - compactness. Some of the basic properties and characterization theorems would be investigated of this newly defined compactness in fuzzy setting. We would also introduce and study fuzzy Sβ - closed spaces. [ABSTRACT FROM AUTHOR]

Information leakage has become an urgent problem in multiple Online Social Networks (OSNs). The interactive communication of users has raised several privacy concerns. However, the current related work on privacy measurement only considers the privacy disclosure of user profile settings, ignoring the importance of profile attributes. To solve the efficient measurement problem, we consider the influence of attribute weight on privacy disclosure scores and propose a privacy measurement method by quantifying users' privacy disclosure scores in social networks. Through introducing Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), we propose a Privacy Scores calculation model based on Fuzzy TOPSIS decision method (PSFT), that is more accurate calculate users' privacy disclosure scores and that can improve users' privacy awareness in multiple OSNs. Users can reasonably set the attribute file configuration based on privacy scores and attribute weight. We conduct extensive experiments on synthetic data set and real data set. The results of the experiments demonstrate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

A fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) is one of the family members of fuzzy parameterized structures and a valuable extension of the neutrosophic soft expert set as well as the neutrosophic hypersoft set. This structure involves a multi-argument approximate function that has the ability to change the sub-characteristic pairs in the form of power set of universe. The main function of this structure is the classification of each character into sub-characteristic valued sets. Due to this prominent property, this mathematical structure is useful for uncertainties and also helps make the decision-making process more adaptable and dependable. By using the algebraic and basic ideas of the FpNHse-sets, a useful strategy, especially for medical diagnosis, known as Sanchez's method has been used in this study. To see a reformed process for the medical diagnosis of heart disease, a useful combination of FpNHse-set and a modified Sanchez's method has been made in this context. By using the real data from the Cleveland data set for heart disease, the implementation of the reform process has been made to see its veracity. Finally, a clear comparison of the used study with its existing studies has been made for the purpose of benefits. [ABSTRACT FROM AUTHOR]

Abstract Cardiovascular disease (CVD) is the one of the most fatal diseases in the world we have seen in last two decades. For heart disease detection, imprecision in clinical parameters may occur due to error in taking readings or in measuring devices or environmental conditions etc. Hence, introducing fuzzy set theory in feature engineering may give better results as it deals with uncertainty. But in fuzzy set theory, only one uncertainty is considered, which is membership degree or degree of belongingness. Intuitionistic fuzzy set (IFS) considers two uncertainties - membership degree and non-membership degree and so IFS may provide efficient results. To reduce the risk of heart disease, an advanced deep learning algorithm will play a significant role in heart disease prediction that will help physicians to diagnose early. In this paper, we have established a transformation of patient features using i) intuitionistic fuzzy parameters, where Sugeno-type fuzzy complement is used and ii) fuzzy parameters, where gamma membership function is used. These transformed attributes are applied on Deep Learning prediction algorithm as Multi-layer Perceptron (MLP). The novelty of the paper lies from feature transformation to deep learning. It is observed that intuitionistic fuzzy transformation approach, keeping model parameters intact, significantly outperforms non-fuzzy method and gammy fuzzy Transformation, which is reflected in evaluation mechanisms. [ABSTRACT FROM AUTHOR]

In this study, we investigate a possible relationship between fuzzy differential subordination and the theory of geometric functions. First, using the Al-Oboudi differential operator and the Babalola convolution operator, we establish the new operator BS α , λ m , t : A n → A n in the open unit disc U. The second step is to develop fuzzy differential subordination for the operator BS α , λ m , t . By considering linear transformations of the operator BS α , λ m , t , we define a new fuzzy class of analytic functions in U which we denote by T ϝ λ , t (m , α , δ) . Several innovative results are found using the concept of fuzzy differential subordination and the operator BS α , λ m , t for the function f in the class T ϝ λ , t (m , α , δ) . In addition, we explore a number of examples and corollaries to illustrate the implications of our key findings. Finally, we highlight several established results to demonstrate the connections between our work and existing studies. [ABSTRACT FROM AUTHOR]

Given a uniform space (X, U), we introduce, from the uniformity U, some uniformities on the set F(X) of all normal, upper semicontinuous with compact support fuzzy sets on X: the Skorokhod uniformity, the level-wise uniformity, the endograph uniformity and the sendograph uniformity. For metric spaces we prove that these uniformities coincide with the uniformities induced by the Skorokhod metric (the level-wise metric, the endograph metric and the sendograph metric, respectively). We study completeness of this class of uniform spaces. [ABSTRACT FROM AUTHOR]

In this research work, we have introduced a new notion of weighted hesitant bipolar-valued fuzzy soft set (WHBFSS) as a generalization of hesitant bipolar-valued fuzzy soft set (HBFSS), and we examine some of its fundamental properties in detail. We've also defined some novel notions of the root mean square difference operator (RMSDO), root mean square difference score matrix (RMSDSM), and weighted score, and using these novel notations, we have proposed an advanced and adjustable decision-making method (DMM) for solving real-life decision-making problems (DMPs) based on both HBFSS and WHBFSS. A real-life example is provided to demonstrate the validity of our suggested method. Finally, a comparative analysis of our approach with an existing method is provided. [ABSTRACT FROM AUTHOR]

The author proposes an approach to finding numerical estimates of the credibility of fuzzy knowledge in fuzzy inference systems. An "input–output" system is modeled by means of fuzzy logic. According to Mamdani's rule, the system's output is found in the form of fuzzy knowledge with subsequent calculation of its credibility. Elements of the probability theory of fuzzy events are used to calculate the credibility of fuzzy knowledge. An example of calculating the credibility of fuzzy knowledge is given. [ABSTRACT FROM AUTHOR]

(1) Background--At present, the distribution of forestry resources in China is uneven, and the forestry development in some regions has problems such as large resource input, backward production technology and low production efficiency. How to find a way to promote the improvement of green total factor productivity of regional forestry has become an urgent issue for the forestry authorities of local governments. (2) Methods--Based on the technology-organization-environment (TOE) theoretical framework, six antecedents affecting green total factor productivity of regional forestry were selected. Based on the data of 29 provinces (autonomous regions and municipalities) in China from 2016 to 2020 (excluding Tianjin, Xizang, Hong Kong, Macao and Taiwan), this paper used fuzzy set qualitative comparative analysis to study the configuration paths of green total factor productivity improvement of regional forestry. (3) Results--The consistency index values of all the conditional variables are less than 0. 9, which indicates that a single factor cannot constitute the necessary condition for achieving high regional forestry green total factor productivity. Six antecedents constitute three configuration types to improve regional forestry green total factor productivity. The overall consistency of these three configuration types is 0. 872 and the total coverage is 0.729. Among them, the technology-driven path mostly exists in the eastern region, the configuration-driven path of "average forestland size + forestry carbon sinks" mostly exists in the central region, and the configuration-driven path of "financial support + forestry carbon sinks" mostly exists in the western region. If the average forestland size of operators and the forestry carbon sinks development as the core conditions exist, then the government financial support and the forestry utilization of foreign capital can replace each other in the process of achieving a high level of regional forestry green total factor productivity. The condition of "low average forestland size" exists in both of the two configuration types leading to low forestry green total factor productivity, and the two configurations can cover 73. 2% of the samples, indicating that the low average forestland size of operators is an important factor restricting the improvement of forestry green total factor productivity in most regions. (4) Conclusions and Discussions--Based on the above analysis, the following suggestions are put forward: First, select different configuration paths of forestry green total factor productivity improvement according to the forest resource endowments in different regions. For example, the regions belonging to the technology-driven configuration path can promote the application of digital technology in forestry production scenarios and integrated innovation by increasing digital infrastructure construction and forestry green technology innovation incentives, so as to promote the improvement of regional forestry green total factor productivity through digital development. Because the development of forestry carbon sinks is the core condition for improving forestry green total factor productivity in most central and western regions. Therefore, the local forestry authorities should simplify the forestry carbon sinks trading procedures, reduce the transaction costs of forestry operators, and stimulate their active participation in carbon sinks trading. Second, optimize the structure of foreign investment in forestry, change the previous single mode of driving regional forestry development mainly relying on financial capital investment, and give full play to the substitute role of forestry foreign investment in the improvement of regional green total factor productivity. Third, speed up the reform of regional forest property rights transfer mechanism, and improve the scale and efficiency of average forestland of forestry operators. [ABSTRACT FROM AUTHOR]

In the present scenario, the main aim of each and every organization is to be competitive, eco-friendly, and sustainable in order to run effectively and efficiently. Therefore, to meet and fulfill the above objectives, each organization must identify and choose the appropriate and best green and sustainable supplier, as a green supplier considers green technology, green products, green packaging, etc., while a sustainable supplier considers all three characteristics of sustainability, namely social, economic, and environmental. The problem of the evaluation and selection of green and sustainable suppliers is an example of a multi-criteria decision-making (MCDM) problem. In this research paper, to handle the uncertainty hidden in the green supply chain, the extended version of a MCDM technique named fuzzy additive ratio assessment (F-ARAS) is implemented for the evaluation of the relative weights of the green and sustainable supplier selection criteria, namely social, economic, and environmental, and also to determine the best green and sustainable supplier through fuzzy theory by using linguistic terms, which includes the application of interval-valued triangular fuzzy numbers (IVTFNs) in an organization. Finally, the technique, F-ARAS, has performed well and enables the organization's managers to attain their required solution and final decision. [ABSTRACT FROM AUTHOR]

The probabilistic rough set (PRS) model, through the incorporation of error levels, represents a quantitative extension of the classical rough set model. It serves as a fundamental expansion that enables robust fault tolerance capabilities by employing relative quantitative description. However, when confronted with interval-valued fuzzy data, the PRS model is rendered ineffective. The primary reason for this lies in the absence of a unique equivalence relation in interval-valued decision systems. This paper presents a novel approach to address this limitation. In this paper, we first propose a fuzzy similarity relation based on diversity function, which establishes a viable foundation for constricting models of probabilistic rough fuzzy set and multi-granularity probabilistic rough set models for interval-valued fuzzy decision systems. Then the decision rules are derived from the presented three kinds of multi-granularity probabilistic rough fuzzy sets, respectively. In order to elucidate the concepts of interval-valued probabilistic rough fuzzy sets and multi-granularity probabilistic rough fuzzy sets, a case study is considered, which is helpful for applying these theories to deal with practical issues. [ABSTRACT FROM AUTHOR]

The author proposes an approach to finding numerical estimates of the credibility of fuzzy knowledge in fuzzy logical inference systems. The “input–output” type system is modeled by means of fuzzy logic. According to Mamdani’s rule, the output of the system is found in the form of fuzzy knowledge with subsequent calculation of its credibility. Elements of the probability theory of fuzzy events are used to calculate the credibility of fuzzy knowledge. An example of calculating the credibility of fuzzy knowledge is given. [ABSTRACT FROM AUTHOR]

In this research, a novel approach is applied to obtain the solution for the non-homogeneous system of fuzzy fractional differential equations. The fuzzy fractional initial value problem is split into sets of classical fractional initial value problems. Such a fractional initial value problem is constructed by taking the forcing term derived from each of fuzzy set and the initial value obtained from each of fuzzy intervals. The solution to the fuzzy fractional initial value problem is defined as the collection of all solutions of the classical fractional initial value problems, and its degree of membership is the smallest value among the degrees of membership obtained from forcing terms and initial values in the associated fuzzy sets. Specifically, a solution is constructed as a fuzzy collection of real vector functions, instead of a vector of fuzzy-valued functions. The numerical example has been used to illustrate the proposed approach in detail. The benefits of the proposed methodology over the existing methodology, as well as its applicability to the real world, are clearly demonstrated. [ABSTRACT FROM AUTHOR]

The study of fuzzy geometry and its different components has grown in recent years, establishing the formal foundations for its development. This paper is devoted to addressing some metric relations in the fuzzy right triangle; in particular, a version of the Pythagorean theorem and geometric mean theorem are provided in analytical fuzzy geometry. The main results show, under certain conditions of the fuzzy vertices, a subset relation between the fuzzy distances associated with the fuzzy right triangle, which is very similar to the classical statements of the Pythagorean theorem and the geometric mean in Euclidean geometry. [ABSTRACT FROM AUTHOR]

The major contribution of present study is to revisit how consumer obtain high adoption intention of artificial intelligence (AI)'s production/service based on symmetric and asymmetric thinking in data analysis. In recent years, technology involving AI has become key technology for success worldwide and has received prominence among academics and practitioners. Accordingly, it is necessary to identify the relationships among relationship marketing, AI perceived usefulness, perceived ease of use, perceived risk, and adoption intention. The main contribution of the symmetric approach (i.e., SEM, structure equation modeling) is to test the research hypothesis to determine the net effect between the variables, and asymmetric approach (i.e., fsQCA, fuzzy set qualitative comparative analysis) contributes to the identification of sufficient conditions leading to high level adoption intention in the concept of fuzzy sets. With symmetric approach, results of path analysis of SEM indicate that impacts of trust are greater than that of commitment. Similarly, perceived usefulness of AI has a greater impact on the adoption intention. Furthermore, AI perceived risk is negative associate with adoption intention. With asymmetric approach, intermediate solutions from fsQCA show that there are three sufficient conditions for high adoption intention of AI. For instance, one of configurations or sufficient conditions is trust, commitment, and AI perceived usefulness present but perceived ease of use absent. Plain Language Summary: Revisiting AI adopt intention The major contribution of present study is to revisit how consumer obtain high adoption intention of artificial intelligence (AI)'s production/service based on symmetric and asymmetric thinking in data analysis. The main contribution of the symmetric approach (i.e., SEM, structure equation modeling) is to test the research hypothesis to determine the net effect between the variables, and asymmetric approach (i.e., fsQCA, fuzzy set qualitative comparative analysis) contributes to the identification of sufficient conditions leading to high level adoption intention in the concept of fuzzy sets. With symmetric approach, results of path analysis of SEM indicate that impacts of trust are greater than that of commitment. Similarly, perceived usefulness of AI has a greater impact on the adoption intention. Furthermore, AI perceived risk is negative associate with adoption intention. With asymmetric approach, intermediate solutions from fsQCA show that there are three sufficient conditions for high adoption intention of AI. For instance, one of configurations or sufficient conditions is trust, commitment, and AI perceived usefulness present but perceived ease of use absent. [ABSTRACT FROM AUTHOR]

Total ozone time series in a metropolis in India were examined in the current study. The data explored are obtained through Brewer spectrophotometer, which counts photons with a photomultiplier to calculate UV irradiance in the spectrum and measures total ozone when the relative route of photons through the ozone layer (air mass) is 3.5 or less. The total ozone time series' uncertainty was thoroughly examined using the Dempster–Shafer method, and the association was also depicted using three-dimensional graphs. Finally, the Adam Optimisation Algorithm and the Rectified Linear Unit were used to demonstrate the prediction capability of the single layer Long Short-Term Memory model. [ABSTRACT FROM AUTHOR]

Brain magnetic resonance imaging (MRI) data is a hot topic in the domains of biomedical engineering and machine learning. Without locating anomalies, such as tumors and edema, radiologists and other medical experts cannot effectively recommend or administer therapy for patients. Having three different magnetic resonance techniques (T1 weighted, T2 weighted, and T3 weighted), MRI can produce detailed multimodal scans of different human brain tissues with varying contrast, which can help pinpoint the source of any abnormalities. The cerebrospinal fluid (CSF), white matter (WM), and grey matter (GM) are all components of the brain, and their boundaries are sometimes hazy and difficult to nail down. In light of the problems above, this paper makes an effort to tackle issues like: i) the noise that exists in the brain datasets for MRI, ii) the fuzziness, uncertainty, overlap, indiscernibility of complex brain tissue regions, iii) the inability of traditional unsupervised methods to reliably distinguish between various brain tissue locations, and iv) ineffective performance. We propose some robust techniques by utilise spatial contextual data, a rough set, a fuzzy set, and ultimately a fuzzy set to steer the clustering process in a better direction, allowing it to deal with likely noise, outliers, and other artifacts. [ABSTRACT FROM AUTHOR]

The energy of a graph is related to its spectrum, which is equal to the total of the latent values of the pertinent adjacency matrix. In this research work, we proposed some of the features and the energy of the Seidel Laplacian of a fuzzy graph. Also, the lower and upper bounds for the energy of the Seidel Laplacian of a fuzzy graph were studied with suitable illustrative examples.

As the increasing environmental issues, various companies have take initiatives to produce green products or to select green suppliers which maximize the business performance and minimize the environmental pollution. The real numbers data have imbiguity and uncertainty due to described by classical tools. Therefore, we consider a new type of fuzzy set, fuzzy credibility rough sets. In fuzzy credibility rough set has credibility membership of positive membership and they reduced the imbiguity in data information. In this paper we have defined a new set called fuzzy credibility rough set (FCRS), after that we defined Frank operational laws for FCRS information. Using these operational laws, we defined a series of aggregation operators that is fuzzy credibility Frank rough weigthed averaging aggregation operators, fuzzy credibility Frank rough ordered weigthed averaging aggregation operators, fuzzy credibility Frank rough hybrid weigthed averaging aggregation operators and its basic properties like boundedness, monotonicity and idempotency.As there is no work which is based on Frank norms aggregation operators under FCRS information. So, we defined a series of aggregation operators that can help us to collect the data for various green suppliers management.We developed a new set called fuzzy credibility rough set (FCRS). We developed a new Frank norms operational laws under FCRS information. We developed a series of aggregation operators. We developed and extend various steps of GRA, VIKOR and TOPSIS method under FCRS information. We explained the application of our proposed work to a real life decision making problem (green supplier management).All the proposed work is to applied to real life decision making problems (green supplier management) to find the best optimal result. Firstly we can collect the data from the decision makers using the proposed aggregation operators and then we applied all the steps of developed method to find the solution in case of green supplier management.

The integration of industry and education is an effective way to optimize the structure of talent training and promote the transformation of scientific and technological achievements. The improvement of traditional education quality can be promoted by promoting digital reform as a national strategic goal. The teaching model of industry-education integration for sports talents is built on the axis of co-creation and the SECI knowledge creation model in this paper. Finally, with the perspective of ecosystem theory, the factors influencing the integration of the university sports industry and education are screened from the three levels of the main body of industry-teaching integration, environment, and mechanism. The fuzzy set and evaluation laboratory method (DEMATEL) are comprehensively applied to determine the importance degree and hierarchical structure of the factors influencing the integration of university sports production and education so as to propose the strategy of digital teaching to deepen the integration of university production and education. The results of the study show that student benefit acquisition and the motivation of industry-teaching integration are 1.9650 and 2.2157, respectively, and the correlation coefficient of the two is 0.537. That is to say, for every level of university sports industry-teaching integration, the level of university sports professional talent cultivation will increase by 0.537 points. This study provides a basic theoretical basis for the subsequent construction of a collaborative training model for sports talent in the context of university industry-teaching integration and the exploration of collaborative paths.

This article analyzes the impact of the Internet on college students’ reading habits and preferences, emphasizing that short reading, micro-reading, and miscellaneous reading have become trends. The article discusses the intelligent transformation of libraries, proposes strategies such as enhancing resource publicity, strengthening academic resource construction and providing personalized services, and analyzes the effect of brilliant reading promotion assessed by WeChat Communication Index (WCI). The results show that reading speed in the virtual reality environment is significantly faster than in the traditional environment, but deep comprehension takes longer. The time spent by mobile college students on entertaining videos, WeChat public numbers, etc. reached a ratio of 0.6740 and 0.5637. PC students spent more time on professional academic journals, academic MOOC videos, etc. The results of the WCI index for March-September 2022 showed that the WCI before the beginning of the school year was 176.23, and the highest in the middle of the semester reached 387.63, which proved the effectiveness of the intelligent transformation of the Library. The article demonstrates college students’ new trend of reading behavior in the Internet era and the effectiveness of Library coping strategies.

As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.

This paper introduces the concept of dominance degree in ecology and constructs a model of the dominant degree of inbound tourism flow gathering and dispersal in Chinese provinces and cities along the Belt, and Road advocated based on the optimization of the entropy value method. In order to further analyze the model, the obtained tourism flow gathering and dispersal dominance degrees are first integrated into a fuzzy set of tourism flow gathering and dispersal dominance degrees. The natural sequence of fuzzy numbers is obtained by screening the fuzzy set, and the fuzzy indexes are obtained after the distribution fuzzy calculation, and finally, the evaluation model of tourism flow gathering and dispersal advantage degree is constructed based on the index matrix and the combination assignment method. After analysis, it is obtained that the inbound tourism distribution of Chinese provinces and cities along the route from 2005 to 2020, the fourth tier of provinces and cities increased, the proportion by 41.02%, the number of the third tier decreased, the proportion decreased to 29.33%, and the uneven distribution of the advantage of inbound tourism distribution was strengthened.