QoS Trust Rate Prediction for Web Services Using PSO-Based Neural Network

Mao, Chengying; Lin, Rongru

doi:10.1109/cbd.2016.022

Cited by 4 publications

(1 citation statement)

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“…Computational overhead was not discussed. Mao and Lin [2016] presented a method for evaluating the trust of network services using neural network. They selected Quality of Service (QoS) attributes as trust features.…”

Section: Models With Discrete Numeralmentioning

confidence: 99%

A Survey on Trust Evaluation Based on Machine Learning

et al. 2020

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Trust evaluation is the process of quantifying trust with attributes that influence trust. It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. In order to overcome these problems and intelligently and automatically evaluate trust, machine learning has been applied into trust evaluation. Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic. In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. Then, we justify a number of requirements that a sound trust evaluation method should satisfy, and propose them as evaluation criteria to assess the performance of trust evaluation methods. Furthermore, we systematically organize existing methods according to application scenarios and provide a comprehensive literature review on trust evaluation from the perspective of machine learning's function in trust evaluation and evaluation granularity. Finally, according to the completed review and evaluation, we explore some open research problems and suggest the directions that are worth our research effort in the future.

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Section: Models With Discrete Numeralmentioning

confidence: 99%