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High competition between universities has been increasing over the years, and stimulates higher education institutions to attain higher positions in the ranking list. Ranking is an important performance indicator of university status evaluation, and therefore plays an essential role in students’ university selection. The ranking of universities has been carried out using different techniques. Main goal of decision processes in real-life problems is to deal with the symmetry or asymmetry of different types of information. We consider that multi-criteria decision making (MCDM) is well applicable to symmetric information modelling. Analytic hierarchy process (AHP) is a well-known technique of MCDM discipline, and is based on pairwise comparisons of criteria/alternatives for alternatives’ evaluation. Unfortunately, the classical AHP method is unable to deal with imprecise, vague, and subjective information used for the decision making process in complex problems. So, introducing a more advanced tool for decision making under such circumstances is inevitable. In this paper, fuzzy analytic hierarchy process (FAHP) is applied for the comparison and ranking of performances of five UK universities, according to four criteria. The criteria used for the evaluation of universities’ performances are teaching, research, citations, and international outlook. It is proven that applying FAHP approach makes the system consistent, and by the calculation of coefficient of variation for all alternatives, it becomes possible to rank them in prioritized order.
Rashad Aliyev; Hasan Temizkan; Rafig Aliyev. Fuzzy Analytic Hierarchy Process-Based Multi-Criteria Decision Making for Universities Ranking. Symmetry 2020, 12, 1351 .
AMA StyleRashad Aliyev, Hasan Temizkan, Rafig Aliyev. Fuzzy Analytic Hierarchy Process-Based Multi-Criteria Decision Making for Universities Ranking. Symmetry. 2020; 12 (8):1351.
Chicago/Turabian StyleRashad Aliyev; Hasan Temizkan; Rafig Aliyev. 2020. "Fuzzy Analytic Hierarchy Process-Based Multi-Criteria Decision Making for Universities Ranking." Symmetry 12, no. 8: 1351.
Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.
Rashad Aliyev; Sara Salehi; Rafig Aliyev. Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting. Sustainability 2019, 11, 793 .
AMA StyleRashad Aliyev, Sara Salehi, Rafig Aliyev. Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting. Sustainability. 2019; 11 (3):793.
Chicago/Turabian StyleRashad Aliyev; Sara Salehi; Rafig Aliyev. 2019. "Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting." Sustainability 11, no. 3: 793.
Real-world decision relevant information is often partially reliable. The reasons are partial reliability of the source of information, misperceptions, psychological biases, incompetence, and so forth. Z -numbers based formalization of information ( Z -information) represents a natural language (NL) based value of a variable of interest in line with the related NL based reliability. What is important is that Z -information not only is the most general representation of real-world imperfect information but also has the highest descriptive power from human perception point of view as compared to fuzzy number. In this study, we present an approach to decision making under Z -information based on direct computation over Z -numbers. This approach utilizes expected utility paradigm and is applied to a benchmark decision problem in the field of economics.
Rashad R. Aliev; Derar Atallah Talal Mraiziq; Oleg Huseynov. Expected Utility Based Decision Making underZ-Information and Its Application. Computational Intelligence and Neuroscience 2015, 2015, 1 -11.
AMA StyleRashad R. Aliev, Derar Atallah Talal Mraiziq, Oleg Huseynov. Expected Utility Based Decision Making underZ-Information and Its Application. Computational Intelligence and Neuroscience. 2015; 2015 ():1-11.
Chicago/Turabian StyleRashad R. Aliev; Derar Atallah Talal Mraiziq; Oleg Huseynov. 2015. "Expected Utility Based Decision Making underZ-Information and Its Application." Computational Intelligence and Neuroscience 2015, no. : 1-11.
In many real-world problems involving pattern recognition, system identification and modeling, control, decision making, and forecasting of time-series, available data are quite often of uncertain nature. An interesting alternative is to employ type-2 fuzzy sets, which augment fuzzy models with expressive power to develop models, which efficiently capture the factor of uncertainty. The three-dimensional membership functions of type-2 fuzzy sets offer additional degrees of freedom that make it possible to directly and more effectively account for model’s uncertainties. Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient “If-Then” rules. The type-2 fuzzy neural networks take advantage of capabilities of fuzzy clustering by generating type-2 fuzzy rule base, resulting in a small number of rules and then optimizing membership functions of type-2 fuzzy sets present in the antecedent and consequent parts of the rules. The clustering itself is realized with the aid of differential evolution. Several examples, including a benchmark problem of identification of nonlinear system, are considered. The reported comparative analysis of experimental results is used to quantify the performance of the developed networks.
Rafik A. Aliev; Witold Pedrycz; Babek G. Guirimov; Rashad R. Aliev; Ümit Ilhan; Mustafa Babagil; Sadik Mammadli. Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization. Information Sciences 2011, 181, 1591 -1608.
AMA StyleRafik A. Aliev, Witold Pedrycz, Babek G. Guirimov, Rashad R. Aliev, Ümit Ilhan, Mustafa Babagil, Sadik Mammadli. Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization. Information Sciences. 2011; 181 (9):1591-1608.
Chicago/Turabian StyleRafik A. Aliev; Witold Pedrycz; Babek G. Guirimov; Rashad R. Aliev; Ümit Ilhan; Mustafa Babagil; Sadik Mammadli. 2011. "Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization." Information Sciences 181, no. 9: 1591-1608.