Design and evaluation of a web classification system for educational resources

This chapter opens the storeroom door. If the classification system is considered the brain of the museum, the storeroom is its unconscious mind where secrets of collection, acquisition, classification, object restoration, and display are hidden. Visible storage has been deployed since the 1970s as a planned technique for demonstrating the potential of museums and highlighting the authenticity of their displays. By melding front and back regions, visible storerooms, like others touristic sites and spectacles, lure in clientele. In bringing to light the content of storerooms, both scandalous secrets and pleasant surprises may be revealed. Storerooms are the locus of key objects in repatriation claims, as well as educational resources for museums. Storeroom innovation through recategorizing and restocking objects may become the impetus for museological change.

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Classification of Tourism Web with Modified Naïve Bayes Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.931-932.1360 ◽

2014 ◽

Vol 931-932 ◽

pp. 1360-1364 ◽

Cited By ~ 1

Author(s):

Naruepon Panawong ◽

Chakkrit Snae Namahoot ◽

Michael Brückner

Keyword(s):

Classification System ◽

Web Pages ◽

Light Weight ◽

Product Markets ◽

Web Contents ◽

Local Product ◽

Bayes Algorithm ◽

Web Classification ◽

F Measure

In this paper we report results of a research aimed at classification Web contents on tourism with a modified Naïve Bayes algorithm. We used Web pages relating touristic information about Thailand. An appropriate light-weight tourism ontology with related terms was used to improve the results, which were categorized into six categories (attractions, accommodation, dining, local product markets, One Tambon One Product (OTOP) shops, and events). The Naïve Bayes algorithm generates results for each category, but Web pages can contain diverse information about tourism spanning over groups. The initial Web classification system could not categorize 130 sites (27.40%) out of 475 tested pages, because those Web pages contain words from more than one category. Therefore, we modified the Naïve Bayes algorithm to improve the efficiency of Web classification, which was then tested with the help of F-Measure: the results show 100% for precision, 97.39% for recall, and 98.58% for F-measure.

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International Research and Variation in Views of Ethnicity

Perspectives on Global Issues in Communication Sciences and Related Disorders ◽

10.1044/gics2.2.66 ◽

2012 ◽

Vol 2 (2) ◽

pp. 66-72

Author(s):

Ella Inglebret ◽

Amy Skinder-Meredith ◽

Shana Bailey ◽

Carla Jones ◽

Ashley France

Keyword(s):

United States ◽

Classification System ◽

International Research ◽

The United States ◽

Research Articles ◽

Journal Articles ◽

Research Participants ◽

The Past ◽

International Scale ◽

Publication Patterns

The authors in this article first identify the extent to which research articles published in three American Speech-Language-Hearing Association (ASHA) journals included participants, age birth to 18 years, from international backgrounds (i.e., residence outside of the United States), and go on to describe associated publication patterns over the past 12 years. These patterns then provide a context for examining variation in the conceptualization of ethnicity on an international scale. Further, the authors examine terminology and categories used by 11 countries where research participants resided. Each country uses a unique classification system. Thus, it can be expected that descriptions of the ethnic characteristics of international participants involved in research published in ASHA journal articles will widely vary.

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