Classical Fuzzy Retrial Queue with Working Vacation using Hexagonal Fuzzy Numbers

Using fuzzy techniques “Classical Fuzzy Retrial Queue with Working Vacation(WV) using Hexagonal Fuzzy Numbers” is discussed in this paper. We acquire model in fuzzy environment as the average orbit length, Probability(Pr) that the server busy, and Pr(the server is in a WV period), the sojourn time of a customer in the queue. Finally numerical results are presented.

Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different.

Exploration of employability perceptions with blended multi-criteria decision-making methods

PurposeThe purpose of this paper is to explore employment perception of students as a relevant indicator of higher education quality, using blended multi-criteria decision-making methods.Design/methodology/approachThe differential impact of these variables was analyzed in this paper taking a sample of 641 students and six higher education lecturers identified as experts on young vocational careers. The traditional study of student behavior and perceptions of employability does not incorporate the uncertainty associated with multi-criteria decision processes and is therefore less adapted to the human reasoning process. This research applies traditional techniques together with fuzzy techniques capable of managing more effectively the uncertainty associated with student actions and behaviors.FindingsThis research shows that it is important to consider previous work experience, academic achievement and soft skills developed during education experiences. In this way, this research shows the lecturers how to adapt their pedagogical practices according to students' perceptions of employability and assess their students' perceptions of employability. In addition, lecturers will be able to incorporate the uncertainty associated with decision-making processes to optimize employability perception.Originality/valueHigher education-related research on uncertainty environments as multi-criteria decision problems is still in early stages. The incorporation of the uncertainty associated with decision-making processes to this field allows to optimize employability perception thanks to its adaptation to real human behavior in the adoption of decisions.

Forest Fire Forecasting Using Fuzzy Logic Models

In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature.