OSVR: an efficient support vector regression model based host overload detection and secure virtual machine migration

Parthasarathy, S.

doi:10.1007/s12652-022-04439-y

Cited by 3 publications

(1 citation statement)

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“…In addition to neural network models and deep learning models, there are several other models used for color prediction. For example, it has also been suggested that support vector machine models can perform classification and regression analysis of data to achieve color prediction [5][6]. In this paper, the color design and Kansei engineering of products are studied, and the prediction model of machine vision is explored experimentally.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

Han

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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Color is a visual element of people, it can bring people different levels, different feelings, and can make people have associations and emotional resonance. Under the background of Kansei engineering, product color design is an important research field. This paper aims to discuss the predictive model of Kansei engineering in product color design, in order to improve the quality of products and meet the needs of consumers. Through empirical research and user survey, this paper verifies the accuracy and effectiveness of product color perceptual image generated by machine vision prediction model for the elderly products. The survey and experimental results show that support vector machines perform best, with an accuracy of 91% and a recall rate 4% higher than BP neural networks.

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Section: Introductionmentioning

confidence: 99%