Most organizations use performance appraisal system to evaluate the effectiveness and efficiency of their employees. In evaluating staff performance, performance appraisal usually involves awarding numerical values or linguistic labels to employees performance. These values and labels are used to represent each staff achievement by reasoning incorporated in the arithmetical or statistical methods. However, the staff performance appraisal may involve judgments which are based on imprecise data especially when a person (the superior) tries to interpret another person's (his/her subordinate) performance. Thus, the scores awarded by the appraiser are only approximations. From fuzzy logic perspective, the performance of the appraisee involves the measurement of his/her ability, competence and skills, which are actually fuzzy concepts that can be captured in fuzzy terms. Accordingly, fuzzy approach can be used to handle these imprecision and uncertainty information. Therefore, the performance appraisal system can be examined using Fuzzy Logic Approach, which is carried out in the study. The study utilized a Cascaded fuzzy inference system to generate the performance qualities of some University non-teaching staff that are based on specific performance appraisal criteria.
Performance of a teacher is vital both for students and institution and must be measured and evaluated for positive reinforcement to teaching. This paper presents a mathematical model to evaluate faculty's teaching performance using fuzzy logic In this proposed evaluation the degree of satisfaction is defined in advance by experts with respect to levels of performance. Evaluator awards fuzzy marks into the fuzzy performance sheet according to each level of performance. From this, the degree of satisfaction of a subject topic is calculated and the result is calculated based on all the topics in the subject. The obtained results from the proposed approach are compared with the conventional non-fuzzy approach and the comparative results are presented.
Abstract-Now a day Cricket is one of the most popular sports around the world. Twenty-20 cricket is the most popular entertaining game in last eight to ten years among different formats of cricket. Indian Premier League (IPL) plays vital role to upturn the status of Twenty-20 cricket. This paper aims to analysis the team performances during first six sessions of IPL in the field of sports Data Mining. The proposed work deals with five different multi-criteria techniques and two group decision analysis in fuzzy environment to handle the imprecise and ambiguous data. The result shows that proposed model yields more realistic way to judge the team's performance and every time it produces the accurate performance appraisal.
Plant diseases are a normal part of the natural world, and they are one of the many ecological processes that work together to keep the vast number of living organisms in the world in a state of equilibrium with one another. Each plant cell has its own set of signalling pathways that help the plant fight off viruses, animals, and insects. Concerns have been raised about whether or not it is possible to use machine learning to make crop predictions based mostly on weather data. The goal of the research is to help users choose the right crop to grow so that they can maximise their yield and, as a result, the money they make from the project. In a rural area where almost half the people work in agriculture, one of the most important problems is when farmers can't use traditional or other non-scientific methods to choose a crop that will grow well in their soil. Researchers can't make use of case studies as well as they could because there isn't enough correct and up-to-date information available. With the resources at our disposal, we have proposed a model that makes use of random forests and the genetic algorithm. This model has the potential to solve this problem by providing predictive insights on the long-term viability of crops and recommendations based on machine-reading models that have been trained to take important environmental parameters into consideration..
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