Fuzzy Set Values Research Articles

Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data

Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables Vehicular Ad Hoc Network (VANET)

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes’ infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles’ requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.

Lattice Valued Fuzzy Sets in Hilbert Algebras

Lattice Valued Fuzzy Sets in Hilbert Algebras

Three-way decisions based on bipolar-valued fuzzy sets over three-way decision spaces

The concept of bipolar-valued fuzzy set was introduced by Zhang. Can we discuss three-way decisions based on bipolar-valued fuzzy sets over three-way decision spaces? In response to this problem, firstly, the paper discusses partially orders on truth values of bipolar-valued fuzzy sets and three-way decision spaces on this basis. Secondly, this paper presents construction method of decision evaluation functions based on bipolar-valued fuzzy sets over three-way decision spaces. Thirdly, this paper makes three-way decisions based on bipolar-valued fuzzy sets over three-way decision spaces and uses an example on an evaluation problem of credit card applicants to illustrate some notions of the (semi-, quasi-)three-way decision spaces and benefits of constructing methods from semi-(quasi-)decision evaluation functions to decision evaluation functions. Finally, the conclusions are summed up.

A Probabilistic Hesitant Fuzzy MCDM Approach to Selecting Treatment Policy For COVID-19

The global significant rise in the number of sick individuals and fatalities has made the ongoing struggle against the severe and lethal COVID-19 pandemic a global effort. There are several ongoing therapies for COVID-19, and more are being developed. However, selecting the best therapy option for COVID-19 patients is still needed. Patients may easily choose from the available COVID-19 therapies using the multi-criteria decision-making method. As a result, the present study provides an MCDM method that is created to determine COVID-19 therapies. Probabilistic Hesitant Fuzzy Set numbers, values, and ambiguity are introduced. Theorems and characteristics of PHFS numbers are also investigated in depth. The Complex Proportional Assessment technique is used, based on the PHFS, for dealing with ambiguity issues. This study uses ten criteria and three treatment methods: antibacterial medication and plasma treatment, vaccinations, as well as quarantine and in-house isolation. The study results reveal that quarantine and isolation at home mark the most effective treatment, followed by vaccinations with antibiotics and plasma therapy.

An Analysis of Obesity in School Children during the Pandemic COVID-19 Using Plithogenic Single Valued Fuzzy Sets

The objective of this research is to examine the perception that school children with obesity, when excluded from organized academic performance and constrained to their residences during the coronavirus epidemic 2019, will reveal negative consequences in health behaviors. To meet the objective, the concept of Plithogenic Single valued fuzzy sets (PSFS) and their aggregation operators were introduced. Based on the proposed theory, an analysis is presented with the case study to highlight its practicality and preciseness.

Uncertainty Measures in Fuzzy Set-Valued Information Systems Based on Fuzzy β-Neighborhood Similarity Relations

Uncertainty measures are instrumental in describing the classification abilities in information systems, and uncertain information has been measured and processed with granular computing theory. While the fuzzy set-valued information system is a generalization of fuzzy information systems, the relationship between the information granulation and the uncertainty in fuzzy set-valued information systems remains to be studied. This paper probes into uncertainty measures in fuzzy set-valued information systems based on the fuzzy [Formula: see text]-neighborhood and the idea of granulation. Specifically, the fuzzy [Formula: see text]-neighborhood similarity relation that reflects the similarity between two objects is defined in terms of the nearness degree. We propose the concepts of information granules and granular structures induced by fuzzy [Formula: see text]-neighborhood similarity relations, based on which we introduce the granularity measures and rough approximation measures of granular structures in fuzzy set-valued information systems. Given the situation of decision information systems, we propose the granularity-based rough approximation measures by combining granularity measures with rough approximation measures. Experiment results and effectiveness analysis show that the measures we proposed are reasonable and feasible.

Lattice Valued Fuzzy Sets in UP (BCC)-Algebras

The aim of this paper is to apply the concept of L-fuzzy sets (LFSs) to UP (BCC)-algebras and introduce five types of LFSs in UP (BCC)-algebras: L-fuzzy UP (BCC)-subalgebras, L-fuzzy near UP (BCC)-filters, L-fuzzy UP (BCC)-filters, L-fuzzy UP (BCC)-ideals, and L-fuzzy strong UP (BCC)-ideals. Also, we study the characteristic LFSs, t-level subsets, and the Cartesian product of LFSs in UP (BCC)-algebras.

Конвергентная модель управления проектами

The paper analyzes various project management systems. It is shown that in conditions of rapid changes in the external environment for both complex and very complex projects, the choice of one or even two complementary project management systems may not be effective. The paper analyzes three complementary pairs of project management methods. The advantages and disadvantages of each project management method are shown.
 At the end of the analysis, a "Convergence Model" of project management methods is presented, in which the values of systems theory, game theory and fuzzy sets are separately noted, allowing the use of project analytics, which relieves companies from excessive formalization of project activities.

Interval value fuzzy hyper AT-algebras

The aim of this work is to a connection between two concepts which are an interval value fuzzy set and a hyper AT-algebra. Also, some properties of these concepts are found. The notions of IVF hyper AT-subalgebras, IVF hyper ideals and IVF hyper AT-ideals are defined. Then IVF (weak, strong) hyper ideals and IVF (weak, strong) hyper AT-ideals are discussed. After that, some relations among these ideals are presented and some interesting theorems are proved.

A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes

In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent task management and execution of control commands for actuators. This paper proposes a predictive-learning framework based on contextual feature selection and optimal actuator control mechanism for minimizing energy consumption in smart buildings. We aim to assess multiple parameters and select the most relevant contextual features that would optimize energy consumption. We have implemented an artificial neural network-based particle swarm optimization (ANN-PSO) algorithm for predictive learning to train the framework on feature importance. Based on the relevance of attributes, our model was also capable of re-adding features. The extracted features are then applied as input parameters for the training of long short-term memory (LSTM) and optimal control module. We have proposed an objective function using a velocity boost-particle swarm optimization (VB-PSO) algorithm that reduces energy cost for optimal control. We then generated and defined the control tasks based on the fuzzy rule set and optimal values obtained from VB-PSO. We compared our model’s performance with and without feature selection using the root mean square error (RMSE) metric in the evaluation section. This paper also presents how optimal control can reduce energy cost and improve performance resulting from lesser learning cycles and decreased error rates.

An approach to multiple attribute decision making based on linguistic value soft rough set and VIKOR method

Linguistic variable is an effective method of representation the preferences of a decision-maker for inaccuracy available information in decision making under uncertainty. This article investigates a multiple attribute ranking decision making problem with linguistic preference by using linguistic value soft rough set. Firstly, we present the definition of linguistic value fuzzy set by introducing the concept of linguistic variable into the original Zadeh’s fuzzy set. We then define the concept of linguistic value soft set and the pseudo linguistic value soft set over the alternative set and parameter set of discourse. Moreover, we investigate the basic operators and the mathematical properties of the linguistic value soft set. Subsequently, we establish the rough approximation of an uncertainty concept with linguistic value over the object set and parameter set, i.e., the linguistic value soft rough set model. Meanwhile, we discuss several deformations of the linguistic value soft rough lower and upper approximations as well as some fundamental properties of the linguistic value soft approximation operators. With reference on the exploring of the fundamental of linguistic value soft rough set, we construct a new method for handling with the multiple attribute ranking decision making problems with linguistic information by combining the proposed soft rough set and the VIKOR method. Then, we give the detailed decision procedure and steps for the established decision approach. At last, an extensive numerical example is further conducted to illustrate the process of the decision making principle and the results are satisfactory. The main contribution of this paper is twofold. One is to provide a new model of granular computing by infusion the soft set and rough set theory with linguistic valued information. Another is to try making a new way to handle multiple attribute decision making problems based on linguistic value soft rough set and the VIKOR method.

State Evaluation Model of High voltage Switchgear Based on Fuzzy Set-valued Statistical Method and Kernel Vector Space Model

The traditional high voltage switchgear (HVS) state evaluation model mostly adopts electrical test, live detection and historical data, neglecting the influence of real-time operation data of HVS composition equipment on the state evaluation results. This paper proposes a HVS operation state evaluation model based on fuzzy set-valued statistics method and kernel vector space model based on electrical test data and on-line monitoring data. First of all, according to the components of high voltage switchgear, the operation state of HVS is described and the evaluation index system is established. Secondly, the fuzzy set-valued statistics method is used to construct the mathematical model of evaluation index weight. Then, the kernel vector space model is introduced, and the Gaussian kernel function is used to map the sample to the features of the high-dimensional feature space. The indicator vector of the sample data and the ideal indicator vector of the high-voltage switchgear operation status level standard are defined in the high-dimensional feature space, and the angle-weighted cosine between the two vectors is calculated as the closeness of the sample to the standard status level, and then the high-voltage switchgear operation status level is obtained. Finally, the real data of a power supply company in western China are simulated. The results show that the greater the closeness degree is, the closer the HVS corresponding to the sample is to the normal state, on the contrary, the smaller the closeness degree is, the closer the HVS is to the fault state.

Algebraic structure of fuzzy signatures

Fuzzy signatures have been used for various applications, including medical diagnosis, communication of robots based on intention guessing, and residential building evaluation. However, there exist many questions about this research topic which have not been addressed in the literature. One of the most important aims is to formally define a family of fuzzy signatures from which an algebraic structure can be obtained allowing to make computations among fuzzy signatures. This paper studies this family and defines suitable meet and join operators satisfying the properties of a lattice as an algebraic structure. A partial ordering relation, the least and greatest elements are also defined on the family of fuzzy signatures. As a consequence, fuzzy signatures can be used as truth values of fuzzy sets, which provides a great level of representativity, completely different from the interval-valued and type-2 fuzzy sets, among others.

APPLICATION OF INTERVAL VALUED FUZZY SET AND SOFT SET

Molodtsov was a father of soft set approach. We can’t easily settle the membership degree in some practical application. So it must be much better to describe interval-valued data instead of explaining membership degree. In this paper, we introduce the latest approach of the interval-valued fuzzy soft set by combining the interval-valued fuzzy set and soft set models. This approach successfully follows distributive, associative and DeMorgan’s laws as well. In the end, a decision problem is solved by this approach.

AN AGGREGATION OF SURVEY QUESTIONNAIRES IN A FUZZY ENVIRONMENT AND ITS APPLICATION

In the response process of survey questionnaires, respondents generally express their opinions (assessment) in a fuzzy environment, i.e., assessment with a degree of membership, and in many cases, an aggregation of survey questionnaires is generally computed mathematically. In this paper, we propose a new aggregation method using the FEV (Fuzzy Expected Value) in summarizing survey questionnaires. As an application of proposed aggregation method, to quantify accurately the current state of opinions and the reasons for national IT investment increasing the rate of economic growth, we decided to circulate a survey questionnaire on national IT investment to interested parties throughout the research institute, government, IT industry. Generally, as we have already known and experienced in most of survey questionnaires, there was a little response. Moreover, according to interested parties, the result was biased views on the government IT investment. So, we consider big data analysis on the selection of the proper IT items for national investment. Proposed aggregation method is consisted of 2-phases. In phase 1, keyword selection for the proper IT items based on big data such as Facebook, Twitter, blog, Google, etc. is achieved. This phase is not mandatory (optional and dependent on the characteristic of survey). In phase 2, aggregation by using the FEV is obtained. Proposed aggregation method on survey questionnaires is particularly useful to find current focal issues such as trend, what’s new, etc., on big data such as Facebook, Twitter, blog, Google, etc. Moreover, in many cases, aggregation using the FEV is a better representative value than the arithmetic mean. Generally, the FEV is more suitable than the value of averaging computation in searching for the representative value of fuzzy set.

Some Algebraic Properties of Generalized Fuzzy Rough Approximations Derived by Fuzzy Set-Valued Homomorphism of LA-G-Semigroups

In this paper we define the concept of fuzzy set valued homomorphism of LA- G-semigroups and mention some features of them. We also investigate the approximations of a generalized fuzzy approximation space constructed on LA-G-semigroups and derived by fuzzy set valued homomorphisms of LA-G-semigroups. Especially, we focus on some algebraic properties of fuzzy subsets in terms of protection of some properties under these approximations.

A note on the lattice structure for subalgebras of the algebra of truth values of type-2 fuzzy sets

The algebra of truth values of type-2 fuzzy sets is not a lattice, but some of its subalgebras may be lattices. In this paper, we give a necessary and sufficient condition under which subalgebras of the truth value algebra of type-2 fuzzy sets form lattices. Further, we prove that if a certain subalgebra is a lattice, then it is isomorphic to an appropriate subalgebra whose all elements are convex functions with the same height. Based on these results, if a certain subalgebra is a lattice, the equivalent characterizations of the partial order in it are given.

A fuzzy rough set based fitting approach for fuzzy set-valued information system

Information systems comprising of multiple attribute values are known as set-valued information systems (SIS). This paper proposes a novel fuzzy set-valued information system (FSIS) as a generalized model of SIS. SIS is known as FSIS if some of its set-values are fuzzy set-values. Existing fuzzy rough set (FRS) model for SIS does not fit a dataset well and can lead to samples misclassification since sometimes there exist a large overlap between samples, so a fitting FRS model is introduced for handling FSIS. Fuzzy similarity relations obtained from each fuzzy set-valued attribute are parameterized using two parameter values to characterize the fuzzy information granules and then based on it, fuzzy lower and upper approximations are reconstructed. This approach can fit FSIS well and prevent samples misclassification. Later, the relative reduct of FSIS is computed using greedy forward algorithm. The effectiveness of proposed FSIS using fitting FRS model approach is proved by conducting a comparative study of proposed FSIS with existing information systems.

Fuzzy Set‐Valued Information Systems and the Algorithm of Filling Missing Values for Incomplete Information Systems

How to effectively deal with missing values in incomplete information systems (IISs) according to the research target is still a key issue for investigating IISs. If the missing values in IISs are not handled properly, they will destroy the internal connection of data and reduce the efficiency of data usage. In this paper, in order to establish effective methods for filling missing values, we propose a new information system, namely, a fuzzy set‐valued information system (FSvIS). By means of the similarity measures of fuzzy sets, we obtain several binary relations in FSvISs, and we investigate the relationship among them. This is a foundation for the researches on FSvISs in terms of rough set approach. Then, we provide an algorithm to fill the missing values in IISs with fuzzy set values. In fact, this algorithm can transform an IIS into an FSvIS. Furthermore, we also construct an algorithm to fill the missing values in IISs with set values (or real values). The effectiveness of these algorithms is analyzed. The results showed that the proposed algorithms achieve higher correct rate than traditional algorithms, and they have good stability. Finally, we discuss the importance of these algorithms for investigating IISs from the viewpoint of rough set theory.