A dynamic fuzzification approach for interval type-2 membership function development: case study for QoS planning

Kalibatiene, Diana; Miliauskaitė, Jolanta

doi:10.1007/s00500-021-05899-8

Cited by 2 publications

(1 citation statement)

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“…In order to improve the performance of the proposed system, MF parameters and fuzzy rules are optimized, using a particular optimization method such as genetic algorithm, Particle Swarm Optimization, pattern search, adaptive neuro-fuzzy inference, etc. [22]. For better performance and feasibility, ANFIS, which is a combination of a fuzzy inference system (FIS) and an adaptive neural network [20], is used for optimization.…”

Section: Data-driven Optimization (Anfis)mentioning

confidence: 99%

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

Miliauskaitė

Kalibatiene

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Today, various stakeholders provide a large number of functionally similarWeb Services (WS) to meet increasingly complex business needs. Therefore, to distinguish similar WS, some researchers have proposed using the non-functional characteristics, named Quality of Service (QoS), and user’s needs, expressed through Quality of Experience (QoE). Thus, all those QoS and QoE attributes should be taken into account in predicting WS quality jointly, called WS QoSE (Quality of Service and Experience). However, these attributes are different in nature, i.e., QoS is data-driven and numerical, while QoE is expert-based and linguistic. Consequently, to predict WS QoSE, in this paper, we propose a hybrid fuzzy inference approach, composing both quantitative and qualitative data inputs into WS QoSE output by applying the adaptive neuro-fuzzy inference system (ANFIS). The developed prototype allows us to implement the proposed approach, investigate its performance, and study the effect of QoE attributes on WS QoSE. The results of the two experiments show good performance and suitability of the proposed hybrid fuzzy inference approach for predicting WS QoSE based on combining QoS and QoE attributes. We expect that those results inspire researchers and practitioners to understand the WS QoSE better and develop user needs matching WS.

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Section: Data-driven Optimization (Anfis)mentioning

confidence: 99%