An extended WASPAS approach for teaching quality evaluation based on pythagorean fuzzy reducible weighted Maclaurin symmetric mean

Teaching quality evaluation (TQE) can not only improve teachers’ teaching skills, but also provide an important reference for school teaching management departments to formulate teaching reform measures and strengthen teaching management. TQE is a process of grading and ranking a given teachers based on the comprehensive consideration of multiple evaluation criteria by expert. The Maclaurin symmetric mean (MSM), as a powerful aggregation function, can capture the correlation among multiple input data more efficient. Although multitude weighted MSM operators have been developed to handle the Pythagorean fuzzy decision issues, these above operators do not possess the idempotency and reducibility during the procedure of information fusion. To conquer these defects, we present the Pythagorean fuzzy reducible weighted MSM (PFRWMSM) operator and Pythagorean fuzzy reducible weighted geometric MSM (PFRWGMSM) operator to fuse Pythagorean fuzzy assessment information. Meanwhile, several worthwhile properties and especial cases of the developed operators are explored at length. Afterwards, we develop a novel Pythagorean fuzzy entropy based upon knowledge measure to ascertain the weights of attribute. Furthermore, an extended weighted aggregated sum product assessment (WASPAS) method is developed by combining the PFRWMSM operator, PFRWGMSM operator and entropy to settle the decision problems of unknown weight information. The efficiency of the proffered method is demonstrated by a teaching quality evaluation issue, as well as the discussion of sensitivity analysis for decision outcomes. Consequently, a comparative study of the presented method with the extant Pythagorean fuzzy approaches is conducted to display the superiority of the propounded approach.

Analysis and review on fuzzy evaluation of the performance

In modern conditions a comprehensive analysis of development trends and their effectiveness in various areas of human activity increasingly requires the analysis of data accumulated in numerous documents stored on the global network. Such an analysis is based on annual results in many areas of research, time trends and keywords. This analysis has certain peculiarities: it requires an analysis of not always accurate numerical information, comparison of qualitative indicators, obtaining both qualitative and quantitative characteristics, as well as the use of reference information for researchers and decision makers in related fields. In this regard, in recent years, a fuzzy assessment based on fuzzy mathematics is increasingly used in all types of assessing the effectiveness of various activities. The paper deals with conducting a bibliometric study based on the Extended Science Citation Index (SCI-E) and fuzzy performance measures to understand research trends and areas of focus. This paper takes the relevant scientific papers in Web of Science database as the research object, and analyzes the research trends with the help of bibliometrics. The results show that the number of papers published in the world is on the rise. The number of papers published in China and Iran is higher than that in other countries and regions. However, the number of papers cited in the United States and Turkey is higher than that in other countries or regions. Islamic Azad University is the largest. The research topics focus on fuzzy sets, fuzzy logic, genetic algorithms and performance evaluation. The research hotspots before 2011 included expert systems, neuro-fuzzy systems, and pattern recognition. After 2011, the research hotspots became neural networks, fuzzy sets, and machine learning.

Use of Intuitionistic Fuzzy Numbers in Survey Sampling Analysis with Application in Electronic Data Interchange

The validity of survey sampling depends on how precise the data is. The validity reduces when questionnaire contains linguistic variables and respondent has to answer in zero or one. The ambiguity in the respondent’s mind can be minimized by getting a response in an intuitionistic fuzzy sense. In this study, we proposed intuitionistic fuzzy aggregative investment benefit rate to select the best manufacturing facility location, where respondents expressed both membership function and nonmembership function for linguistic variables, with some hesitancy level, and give significant improvement over the fuzzy assessment method. The proposed methodology is also verified at different hesitancy levels and recommended to use a small hesitancy level. Real-life example of the use of electronic data interchange (EDI) in Pakistan’s different fields, such as business, commerce, transport, airlines, warehousing, and logistic and delivery industry, is used to support the proposed methodology. Thus, an intuitionistic fuzzy rate reduces the cost, time, and effort of survey sampling instead of taking and analyzing responses from large samples.

A Multicriteria Selection Framework for Wireless Communication Infrastructure with Interval-Valued Pythagorean Fuzzy Assessment

In recent years, interval-valued Pythagorean fuzzy number is playing a more and more important role in decision management. It is a more effective and powerful tool to handle fuzzy information in decision problems. The multicriteria decision-making theory has been widely used in solving practical problems, such as the risk assessment of financial investment, engineering and construction, medical and health care, and information security. The main purpose of this paper is to apply a new interval-valued Pythagorean fuzzy decision-making method to practice and to analyze and solve the problem of wireless communication infrastructure. In this paper, a new interval-valued Pythagorean fuzzy ranking method, extending scope of application of the VIKOR method to interval-valued Pythagorean fuzzy set, is proposed. In order to adapt to actual needs, subjective and objective weights are combined to solve decision-making problems to enhance its practicality, validity, and effectiveness. An example of wireless communication infrastructure problem is provided to illustrate the rationality of this method and verify its advantages.

Model of Evaluation and Selection of Expert Group Members for Smart Cities, Green Transportation and Mobility: From Safe Times to Pandemic Times

This paper presents the development of technologies to support the decision-making of local government executives and smart city concept managers in selecting and evaluating the competencies of new members for advisory groups for solving problems that are implemented in safe times in individual areas or in crises, such as pandemics. The reason for developing effective urban transformation strategies and for the transparent selection of independent experts (non-politicians) for policymaking, decision-making, and implementation teams is not only the heterogeneity of smart city dimensions together with the necessary complexity and systems approach, but also the nature of the capacities and tools needed for smart city concepts. The innovative hybrid competency assessment model is based on fuzzy logic and a network for neuro-fuzzy assessment. It is a technological model for evaluating the competencies of specialists, taking into account the influence of human factors on the processes of personnel selection and system management. An innovative web platform named “Smart City Concept Personnel Selection” has been designed, which can be adapted to various users of municipalities or regional institutions for the transparent selection of qualified personnel for effective decision-making and the use of public funds during safe times or emergencies, such as the COVID-19 pandemic.

An Internet of Things Evaluation Algorithm for Quality Assessment of Computer-Based Teaching

Internet of Things has the ability to revolutionize computer-based teaching and assess the quality of teaching at the same time. The assessment of teaching efficiency is hampered by two issues: the evaluation index method (EIS) is insufficient, and the assessment framework is incapable of dealing with complicated fuzzy indexes. To address these issues, the theory of fuzzy stratification from IoT perspective is presented for the first time in this paper. This theory is based on the related theory of fuzzy assessment by measuring the teaching quality evaluation index. Initially, theoretical analysis and model measurement were merged to build a better multiangle EIS for teaching quality. To manage fuzzy indexes, a teaching quality assessment model was developed using both quantitative and qualitative studies. The suggested EIS and fuzzy assessment model can effectively evaluate the standard of teaching in schools, colleges, universities, and institutes, according to implementation results. This qualitative assessment approach is empirical and rational, and it strongly promotes the quality enhancement of educational effectiveness, based on our experimental and simulation results.