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An intelligent insect search system based on observation of the insect's structure

机译:基于昆虫结构观察的智能昆虫搜索系统

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In today's ecological environment, non-experts need insect search systems to identify insect species and to obtain its u-Learning contents. Even though non-experts have been assisted by the naieve web search system based on scientific names, it is very difficult for them to use the system effectively because they are laypersons of insects' scientific names. To assist them more effectively, the ISBC (Insect Search based on Biological Classification) method and ISOBC (Insect Search through Observation based on Biological Classification) method had been proposed. However, the ISBC method requires their time-consuming efforts to search insects because it is due to biological classification based on insects' Order-Family-Species and their scientific names. On the other hand, the ISOBC also gives them troublesomeness to observe insects because it is due to the sequence of biological classification. To overcome such difficulties, we propose a new model, the ISOIS (Insect Search based on Observation of the Insect's Structure) method. It is based on natural observation in the sequence of insect's structure. In addition to that, it is equipped with inference through similarity measure according to the observation attributes by the sequence of biological classification to improve user satisfaction. Finally, we compare it with the ISBC and ISOBC methods. In order to compare the priorities among these three insect search systems, using the AHP method, we derive three evaluation criteria for user satisfaction and three sub-evaluation criteria for each evaluation criterion. In the empirical survey results, we found the order of priorities was ISOIS, ISOBC, and ISBC. This shows that the ISOIS system proposed in this study is comparatively superior to the ISBC and ISOBC systems in usage and quality.
机译:在当今的生态环境中,非专家需要昆虫搜索系统来识别昆虫种类并获取其u-Learning内容。即使基于科学名称的naieve网络搜索系统已经为非专家提供了帮助,但由于他们是昆虫科学名称的外行,因此他们很难有效地使用该系统。为了更有效地帮助他们,提出了ISBC(基于生物分类的昆虫搜索)方法和ISOBC(基于生物分类的观察昆虫搜索)方法。但是,ISBC方法需要花费大量时间来搜索昆虫,因为它是基于基于昆虫的科目种及其科学名称的生物学分类而来的。另一方面,由于生物分类的顺序,ISOBC也使他们难以观察昆虫。为了克服这些困难,我们提出了一种新模型,即ISOIS(基于对昆虫结构观察的昆虫搜索)方法。它是基于对昆虫结构顺序的自然观察。除此之外,它还通过根据生物分类顺序的观察属性,通过相似性度量进行推理,以提高用户满意度。最后,我们将其与ISBC和ISOBC方法进行比较。为了比较这三种昆虫搜索系统之间的优先级,使用AHP方法,我们得出了用户满意度的三个评估标准以及每个评估标准的三个子评估标准。在实证调查结果中,我们发现优先顺序为ISOIS,ISOBC和ISBC。这表明,本研究中提出的ISOIS系统在使用和质量上均优于ISBC和ISOBC系统。

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