The main purpose of this study is to present a systematic methodology based on fuzzy Multi-Criteria Decision-Making (FMCDM) models to help users evaluate computer algebra systems (CAS). CAS is a software package for the manipulation of mathematical formulas. The suggested methodology is user-centred which involves users' subjective evaluation judgments. User judgments are represented by means of fuzzy linguistic modelling techniques. An evaluation criteria framework based on the concept of the usefulness of CAS is developed. Next, two FMCDM models – fuzzy Analytical Hierarchy Process (FAHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) are proposed for the evaluation procedure. The FAHP is applied to determine the relative importance weights of qualitative evaluation criteria; the FTOPSIS is applied to rank the CAS alternatives. The illustrated case study demonstrates the applicability and effectiveness of the proposed methodology.
This paper focuses on input variable selection-feature selection-methods with the artificial neural network for the streamflow forecasting of large basins that have a variety of numerous stations. The feature selection methods in the current hydrology research community are not able to handle the problem in such basins. The paper proposes a novel feature selection algorithm-Bubble Selection-based on the idea of utilizing geographic distance as a metric. Evaluation of the performance of the algorithm is carried out by applying the Bubble Selection, to the case study of modeling Austria's water resources of 540 stations in a single run mode. The aim is to select features for each station among 2412 stations, streamflow, precipitation, snow, snow depth, and water level measurements are available. The proposed algorithm allows considerably reducing the dimension of features. The Bubble Selection algorithm is further combined with the Sequential Forward Selection algorithm. Performance of the hybrid model is compared with the performance of Feature Ranking method in terms of the coefficient of determination, Nash-Sutcliffe Efficiency, and percent bias. The results show the superiority of the proposed hybrid algorithm over the Feature Ranking. The paper introduces a methodology to model a large basin and it reveals some skills that a feature selection algorithm should have.
The purpose of this chapter is to explore fuzzy logic based methodology for computing an adaptive interface in an environment of imperfect, vague, multimodal, complex nonlinear hyper information space. To this end, based on fuzzy linguistic modelling and fuzzy multi level granulation an adaptation strategy to cognitive/learning styles is presented. The granulated fuzzy if-then rules are utilized to adaptively map cognitive/learning styles of users to their information navigation and presentation preferences through natural language expressions. The important implications of this approach are that, first, uncertain and vague information is handled; second, a mechanism for approximate adaptation at a variety of granulation levels is provided; third, a qualitative linguistic model of adaptation is presented. The proposed approach is close to human reasoning and thereby lowers the cost of solution, and facilitates the design of human computer interaction systems with high level intelligence capability.
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