Fuzzy Judgment Matrix Research Articles

Evaluation of English Teaching Quality Based on Hierarchical Analysis and Fuzzy Decision Tree Algorithm

Abstract How to improve classroom teaching evaluation so that it can really play the role of guidance, motivation, and promotion of evaluation in the new classroom model is a problem that needs to be solved urgently. This paper first introduces the concepts of fuzzy hierarchical analysis and fuzzy comprehensive evaluation, defines the fuzzy judgment matrix, designs a multi-level fuzzy comprehensive judgment algorithm, and constructs a fuzzy hierarchical analysis-fuzzy comprehensive evaluation model. Then, the generalized fuzzy decision tree model is constructed to achieve a clearer and more effective hierarchical categorization of teaching quality evaluation indexes, and the teaching evaluation index system is established accordingly. Teaching quality evaluation is carried out in the English classrooms of three different teachers in a university, and it is found that the classification effect of this paper’s algorithm is better than that of other comparative algorithms in each evaluation index on the datasets FLAGS and BIRDS. In the university’s teaching quality evaluation validation for three English teachers, X, W, and Y, the comprehensive evaluation scores given by this paper’s method are 94.98, 85.92, and 73.32, respectively, which are in line with the actual teaching situation of the three teachers. This study provides new ideas and methods for reforming classroom teaching evaluation.

Application and Performance Evaluation of Industrial Internet Platform in Power Generation Equipment Industry

The development and application of the industrial Internet platform (IIP) has brought enterprises huge benefits, such as improving the efficiency of resource management and equipment maintenance, achieving the precipitation and reuse of industrial knowledge, and enhancing the development ability of industrial APPs, etc. Therefore, in order to accelerate the application of the IIP, promote the digital transformation of enterprises, and enhance the application effect of the IIP, it is necessary to evaluate the application level and performance of the enterprise IIP. In this paper, taking the IIP in the power generation equipment industry as the research object, the background, architecture, and implementation process of the platform is described, and an evaluation method is proposed. First, a third-level evaluation index system is proposed via reference analysis and the Delphi method from four dimensions—platform basic condition, platform technology capability, platform application service capability, and platform input–output capability—and a total of sixty-five bottom indexes are designed in particular. Then, the fuzzy analytic hierarchy process (FAHP) and particle swarm optimization (PSO) algorithm are used to determine the weights of indexes, where FAHP is used to construct a fuzzy judgment matrix and PSO is adopted to adjust the consistency of the fuzzy judgment matrix in the FAHP. Finally, through the weights of indexes and questionnaire scores, the evaluation results are obtained, and the application level and performance of the IIP in the power generation equipment industry is analyzed. Through analysis, the important indexes that have a significant influence on the evaluation are determined, and the weaknesses that need to be enhanced are also pointed out, which can guide enterprises to make targeted improvements on the IIP in the power generation equipment industry so as to ensure the sustainable development of the power generation equipment industry.

Optimization of Teacher Incentive Mechanism and Policy Supply in Rural China Based on Judgment Matrix

Abstract With the continuous development of artificial intelligence technology, rural education is crucial in Chinese education, and the problem of rural teachers’ incentive mechanisms directly affects the quality of rural education. Firstly, according to the incentive mechanism of rural teachers, quantitative and qualitative research on the incentive mechanism of teachers in rural areas using the fuzzy evaluation method is proposed. In the fuzzy judgment matrix, each level is determined based on the decomposition of the previous level, and the results of the previous level are determined based on the calculation results of the next level. Then, using the relevant motivation theory and questionnaires, the principal and teachers of the school were investigated to understand the current situation and main problems of the construction of teachers’ motivation mechanism in the junior high school of Un Dong Town, Duyun City. Using the fuzzy evaluation method to analyze the difference between the actual feeling and expected demand of teachers’ motivation, the result shows that the difference between salary and promotion is 1.22 and 1.19, which means that the difference between salary and promotion is bigger in the motivation mechanism of rural teachers, and we should focus on reforming the salary scheme and implementing the promotion training mechanism of rural teachers. This study confronts the problem of rural teachers’ incentive mechanism, which has good theoretical and practical guidance for improving education quality and so on.

Exploration and Practice of College English Reading Teaching Reform in the Context of Deep Learning

Abstract To improve the quality and effectiveness of learners’ English reading learning, this paper proposes a fuzzy hierarchical analysis method incorporating deviance entropy (DE-FAHP), which improves the differentiation quality of the fuzzy judgment matrix and enhances the accuracy of weight calculation of each influencing factor by introducing deviance entropy model. Finally, the comprehensive difficulty quantification model of English reading comprehension objective questions is determined to provide an efficient and accurate method for calculating the parameters of the subsequent English reading comprehension standardized question bank. Finally, this paper proposes a hybrid testing method that combines CAT’s online grouping technology with MST’s staged presentation of test questions to construct the English reading comprehension adaptive testing system ca-MST. Through system validity testing, students in the experimental class improved more significantly, with only question 20 in the experimental class scoring below 4.0, while most of the scores in the control class were below 4.0. The mean reading score of the experimental class was 76.31, with a standard deviation of 11.16, while the mean reading score of the control class was 69, with a standard deviation of 18.76. The comparison showed that the reading scores of the experimental class taught with the reading comprehension adaptive testing system were higher than those of the control class taught with the traditional model, and the differences within the class were smaller.

Application of Multi-Level Fuzzy Comprehensive Evaluation in the Pension Industry of Combination of Medical and Nursing in Heilongjiang Province

The development of pension service industry is the internal requirement of building a harmonious society and promoting the coordinated development of economy and society. Construction of evaluation index system of combination of medical and nursing. There are 6 primary indicators and 21 secondary indicators. By means of questionnaire, this paper investigates the satisfaction of the current medical and nursing industry in Heilongjiang Province, and establishes a fuzzy judgment matrix. The entropy method is used to determine the weight of each index, and the multi-level fuzzy evaluation model of the combination of medical care and pension industry in Heilongjiang Province is established. The comprehensive score was 78.65. It shows that the industry of the combination of medical care and pension in Heilongjiang Province is at the middle and upper level. Although people are very satisfied with the current pension industry, there is still room for improvement. Finally, according to the analysis of the results of the above model, some suggestions are put forward to effectively promote the development of the industry.

Analysis of the environmental resources value and enterprises economic benefits based on combination algorithm

Abstract To solve the problems of fuzziness, nonlinearity and complexity of economic factors and environmental factors in the evaluation of ecological economic benefits of enterprises are studied. Based on the theory of environmental-economic benefits (EEB), this paper defines the value of environmental resources and economic benefits of enterprises, and puts forward the indicators of environmental and economic benefits. The evaluation model of enterprise EEB based on the analytic hierarchy process and fuzzy set algorithm is constructed. By establishing the hierarchical structure model of index system, the intuitive fuzzy judgement matrix is obtained, and the final score of enterprise environmental benefits is calculated by consistency test and weight analysis. After verifying the model, the evaluation method of enterprise EEB proposed in this paper is accurate and reliable, which is helpful to deeply analyse the hidden order and adaptation mechanism of complex adaptation system of enterprise environmental management.

Hydropower Unit State Evaluation Model Based on AHP and Gaussian Threshold Improved Fuzzy Comprehensive Evaluation

Because a single monitoring index cannot fully reflect the overall operating status of the hydropower unit, a comprehensive state evaluation model for hydropower units based on the analytic hierarchy process (AHP) and the Gaussian threshold improved fuzzy evaluation is proposed. First, the unit equipment was divided into a hierarchical system, and a three-tier structure system (target layer-project layer-index layer) of the unit was constructed, and the weight of each component in the system was determined by the comprehensive weighting method. Secondly, according to the characteristics of the normal distribution of the historical health data of the unit, the upper and lower limits of the index were determined based on the Gaussian threshold principle, the real-time monitoring index degradation degree was calculated according to the index limit, and the degradation degree was applied to the fuzzy evaluation model to obtain the fuzzy judgment matrix. The result of assessment was divided into four sections: good, qualified, vigilant, and abnormal. Finally, combined with the unit hierarchical structure system, the weighted calculation of the fuzzy judgment matrix of each indicator, the overall fuzzy judgment matrix of the upper-level indicators of the unit was obtained, and the operating status of the unit was judged according to the matrix. Taking a real power plant unit as an example, the model was verified, and compared with other evaluation methods, the effectiveness and advantages of the proposed method were verified. In addition, the method proposed in this paper effectively solved the problems of index weighting and index limit determination in the existing model of unit condition evaluation.

AHP and Machine Learning‐Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia

In dynamic situations, multicriteria decision‐making (MCDM) for classification frequently takes place. The selection of suitable land sites and clear criteria is one of the most significant challenges in strategic military site selection around the world and at the national level. The main objectives of the present study are to find out the strategic military site land suitability in Adea District by using AHP, GIS, and machine learning algorithms. To determine the subject weights of criteria, a novel approach for prioritizing the intuitionistic fuzzy judgement matrix is proposed. An optimization model is developed to determine the objective weights that maximize the distance of each alternative to the negative‐ideal solution. Machine learning methods are mainly employed for filtering, interpreting, and predicting information from satellite image data. The random forest analysis method is used for patterning the military strategic site selection in the Adea District. The results of the study were able to create models and submodels of military strategic site land suitability using the weighted linear combination (WLC) mechanism. From the entire research area woreda, 87.02 km2 (9.3%), 439.11 km2 (46.9%), 114.59 km2 (12.2%), 196.48 km2 (20.9%), and 99.19 km2 (10.6%) have not suitable, marginally suitable, moderately suitable, suitable, and highly suitable conditions, respectively, for a strategic military site. Changes in the six classes of land use/land cover were assessed with an overall accuracy of above 97% and an overall kappa statistic of 96%. Finally, the study actually suggests that ML, RS, GIS, and AHP algorithms are beneficial for decision‐makers (DMs) that will enhance the procedure for strategic military site, supported by multicriteria decision‐making and its applications for the study area’s land suitability. Furthermore, security and military authorities will be able to propose a planning protocol and suitable sites for strategic military site selection in the near future based on the study’s basic methodological application and main findings.

Construction of College Aesthetic Education Quality Improvement Model under Big Data Network

In view of the importance and urgency of college aesthetic education, this paper constructs a quality improvement model of college aesthetic education under the big data network. Firstly, it puts forward the characteristics of planning, step-by-step, scientific, and centralized aesthetic education in colleges and universities and then expounds the importance of aesthetic education in colleges and universities, including the importance of aesthetic education in spiritual education and in the construction of socialist spiritual civilization. Integrating the functions of data mining under the big data network, using the analytic hierarchy process to construct the improvement index, and carrying out the consistency test at the same time, we can get the improvement index system of aesthetic education quality in colleges and universities. The fuzzy judgment matrix is constructed to obtain the final improvement result. The experimental results show that the constructed model verifies the importance and urgency of aesthetic education in colleges and universities, has a high degree of freedom, and reduces the complexity of quality improvement.

Risk assessment of large-scale sports events based on fuzzy analytic hierarchy process

Large scale sports events bring all kinds of opportunities, but also contain huge risks. Therefore, risk management measures in the operation of sports events is important, and risk assessment is a significant part of risk management. It is indispensable to select risk factors that have a greater impact on the event risk and analyze their impact on the event risk and the degree of impact. In this paper, the fuzzy analytic hierarchy process is used to study the risk assessment. Through the identification of risk factors, the modeling of analytic hierarchy process, the construction of fuzzy evaluation matrix and the ranking of risk factors, the importance ranking of risk factors in the operation of large-scale sports events is realized. Aiming at the fuzziness of people’s judgment reflected by fuzzy analytic hierarchy process, a research method of risk assessment of large-scale sports events based on fuzzy analytic hierarchy process is proposed. Through a series of steps of risk identification and risk assessment of sports events, the risk assessment of sports events is realized, event risk factors, modeling of AHP structure, building of fuzzy judgment matrix and ranking of event risk factors, importance ranking of risk factors. The experimental results show that the consistency ratio CR = CI/RI = 0.0193/0.58 = 0.033 < 0.1, the judgment matrix has good consistency, which shows the feasibility in the risk assessment of large-scale sports events.

FAHP-Based Reliability Evaluation of Distributed IoT Devices in a Distribution Power Grid

Reliable operation of distributed internet of things (IoT) devices is essential for supporting two-way interaction between energy flow and information flow in a distribution power grid. Traditional reliability evaluation methods suffer from several challenges including complex evaluation indicator selection and inadaptability of fixed weights with actual reliability requirements. In this paper, we first establish a multilevel reliability evaluation indicator system, where the indicators are selected through joint consideration of comprehensiveness and effectiveness. Then, we propose a fuzzy analytic hierarchy process- (FAHP-) based reliability evaluation method, where a three-layer reliability evaluation architecture is established. Specifically, a 0.1-0.9 scaling method is adopted to establish the fuzzy judgment matrix and scale the important relationship among state-layer elements, which can be dynamically adjusted to generate the weights adapting with actual operation state. The scores of distributed IoT devices and corresponding reliability levels can be obtained through the weighted summation of the scores of each layer. Simulation results verify the effectiveness and accuracy of the proposed method through comparing with actual reliability value and two existing evaluation methods.

An FGM decomposition-based fuzzy MCDM method for selecting smart technology applications to support mobile health care during and after the COVID-19 pandemic

In a fuzzy multicriteria decision-making (MCDM) problem, a decision maker may have differing viewpoints on the relative priorities of criteria. However, traditional methods merge these viewpoints into a single one, which leads to an unrepresentative decision-making result. Several recent methods identify the multiple viewpoints of a decision maker by decomposing the decision maker’s fuzzy judgment matrix into several symmetric fuzzy subjudgment matrices, which is an inflexible strategy. To enhance flexibility, this study proposed a fuzzy geometric mean (FGM) decomposition-based fuzzy MCDM method in which FGM is applied to decompose a fuzzy judgment matrix into several fuzzy subjudgment matrices that can be asymmetric. These fuzzy subjudgment matrices are diverse and more consistent than the original fuzzy judgment matrix. The proposed methodology was applied to select the best choice from a group of smart technology applications for supporting mobile health care during and after the COVID-19 pandemic. According to the experimental results, the proposed methodology provided a novel approach to decomposing fuzzy judgment matrices and produced more diverse fuzzy subjudgment matrices.

Contingency response decision of network public opinion emergencies based on intuitionistic fuzzy entropy and preference information of decision makers

A multi-attribute group decision-making (MAGDM) method based on intuitionistic fuzzy preference information is proposed for the multi-attribute intuitionistic fuzzy group decision-making problem where the decision-makers weight and attribute weight are completely unknown and the decision-maker has preference information for the scheme. Firstly, an intuitionistic fuzzy interval judgment matrix is established to describe the original data of the key decision indicators for multiple network public opinion emergencies that erupt simultaneously. Secondly, the attribute weights are determined based on the improved intuitionistic fuzzy entropy construction method, and the expert weights are determined by using objective decision information, taking into account the intuitionistic fuzzy entropy of decision matrix. MAGDM can not only synthesize experts' professional experience in different aspects, but also avoid experts' decision-making errors caused by insufficient domain knowledge. It is necessary to continuously adjust the expert weight based on decision-making information to make the comprehensive decision-making information more accurate. Thirdly, a scheme preference model and an attribute weight optimization model are established to determine the ranking method of intuitionistic fuzzy interval values. Then, a modified distance measure of intuitionistic fuzzy sets (IFSs) is introduced to make the evaluation result more accurate and reasonable when it comes to solving the deviation between the evaluation value and ideal solution of each scheme. Finally, the effectiveness and practicability of the proposed decision-making method are verified by an example of emergency crisis severity, It assists decision makers in selecting and implementing the optimal emergency response plan in a timely and efficient manner, which improves the emergency treatment efficiency of network public opinion crisis, helps emergency departments to better deal with the network public opinion crisis, improves the ability of public opinion guidance and control, and provides a new method and idea for multi-attribute intuitionistic fuzzy group decision-making problem.

Invested Costs and Risk Control Model of Social Governance Based on Fuzzy Algorithm

In view of the fact that the current methods cannot effectively and correctly control the invested costs and risk of social governance, resulting in a higher risk rate in the invested costs, a fuzzy algorithm-based invested costs and risk control model for social governance is proposed. This paper analyzes the classification and causes of risk, expounds the methods of risk identification and risk assessment, constructs and studies the invested costs and risk control model of social governance, establishes the fuzzy judgment matrix of risk control, calculates the single-layer ranking weight vector of risk fuzzy judgment matrix, and determines the fuzzy judgment matrix. The membership degree of risk influencing factors to the risk level is input into the social governance invested cost and risk control model to obtain the corresponding risk assessment results and predict the invested cost. According to the comparison of experimental results, through the test of risk degree and risk rate, it is verified that the maximum risk level value of the model is 20, which can minimize the risk degree; the risk control coefficient of the model is between 0.6 and 1.0, which can effectively reduce the probability of risk and achieve the purpose of design.

A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant Transformation.

In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker’s fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.

Application of Data Mining in Effect Evaluation of Lean Management

Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

Seven-stage complex horizontal well 3D borehole track design

In this paper, a three-dimensional borehole track design is carried out for complex horizontal wells. Based on the two-dimensional borehole track model, a seven-segment ideal three-dimensional borehole track model is established by considering the parameter range, frictional torque and resistance, and borehole track positioning hit rate. After that, Monte Carlo simulation is applied to find the optimal design of the borehole track. Based on fuzzy comprehensive evaluation, horizontal well drilling completion acceptance criteria are established. Three evaluation factors of cost, risk, and difficulty of construction are determined to constitute the factor set; five rubrics of excellent, good, moderate, qualified and unqualified are used to constitute the evaluation set. After deriving the affiliation matrix, the final evaluation criteria are determined by using the fuzzy judgment matrix to calculate the superiority and inferiority ratings of the mechanical probability model compared with the developed criteria.

Big data-driven fuzzy large-scale group decision making (LSGDM) in circular economy environment

With the continuous development of big data techniques, more and more group decision-making problems involve multiple decision-makers. In the era of big data, massive data can improve decision-making ability and provide good support for decision-making. Large-Scale Group Decision Making (LSGDM) has been developed, and it discusses many cases where decision-makers are related to the decision-making process. However, the current LSGDM model has some shortcomings. To end this issue, this paper proposed a novel LSGDM method to study the role of the big data-driven decision-making issue. In this regard, existing literature and expert interviews were used to collect research standards. Then, a fuzzy LSGDM judgement matrix decision-making method based on group analysis is proposed. Finally, the method is applied to evaluate the takeout service platform. The results show that seven respondents need to adjust the acceptable consistency of the fuzzy judgement matrix, and they can quickly reach a satisfactory consensus. The results can further enrich and improve the group decision-making method based on a fuzzy judgement matrix.

Fuzzy hierarchy analytic method of enterprise supply chain financial risk

Supply chain finance refers to one or more companies upstream and downstream of the industrial supply chain. According to the core company of each node, based on actual transactions, use customized and standardized financing transaction methods to control capital flows or control assets. The right to provide comprehensive financial products and services between supply chain nodes. This article only needs to introduce the financial risk analysis of the enterprise supply chain based on the fuzzy analytic hierarchy process. This paper proposes a fuzzy analytic hierarchy process, which uses a combination of qualitative and quantitative risk assessment methods to assess financial risks, and designs a financial risk assessment system by constructing a fuzzy judgment matrix. It also proposes a comprehensive judgment of the financial risk assessment method. The impact of various risk factors on financial services provides a basis for risk prevention. The experimental results of this paper show that the fuzzy analytic hierarchy process evaluation method is relatively objective and can effectively evaluate the financial risks of the enterprise supply chain. From the weight analysis, it can be concluded that the technical risk weight value is 0.47, which accounts for the largest proportion and is the most important risk.

Diverse three-dimensional printing capacity planning for manufacturers

Three-dimensional (3D) printers are widely used in rapid prototyping and mass customization. However, capacity planning for 3D printers is challenging because 3D printers are usually not dedicated, and estimating the demand for their capacity is not easy. To overcome this problem, in this study, a consistent-decomposition (CD) fuzzy-analytic-hierarchy-process (FAHP) approach is proposed. This approach extracts the various viewpoints of a capacity planner by decomposing his or her fuzzy judgment matrix into several fuzzy subjudgment matrices. On the basis of each fuzzy subjudgment matrix, the performances of various 3D printers are assessed and compared. The best-performing 3D printer from each viewpoint is chosen. The proposed methodology has been applied to a real-world case in which a diverse set of 3D printers was acquired by a manufacturer. The proposed methodology returned a diverse set of optimal 3D printers, whereas a competing set chosen using any existing method lacked diversity. In addition, a decision maker who applied the proposed methodology did not need to specify different requirements for various types of 3D printers.