System Design of Cloud Search Engine Based on Rich Text Content

Chan, Hao-peng; Xu, L. Y.; Liu, Huihui; Zhang, Run-tian; Sangaiah, Arun Kumar

doi:10.1007/s11036-020-01676-3

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Enhanced Learning Methods In Real Application1

Introduction1

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2020

2022

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Article2

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Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Then, the second section of this issue includes the other five papers, which focuses on the enhanced learning methods using in real application, such as forecast, localization, as well as various kinds of applilcations [11][12][13][14][15].…”

Section: Enhanced Learning Methods In Real Applicationmentioning

confidence: 99%

An Introduction to Multimedia Technology and Enhanced Learning

Xia

Liu

2020

Mobile Netw Appl

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Section: Enhanced Learning Methods In Real Applicationmentioning

confidence: 99%

An Introduction to Multimedia Technology and Enhanced Learning

Xia

Liu

2020

Mobile Netw Appl

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“…Each type involves a variety of approaches to discover the information patterns [15]. Accordingly, data mining techniques can be exploited to enhance the ability of search engines to discover specific information in a huge amount of data [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Document classification using term frequency-inverse document frequency and K-means clustering

Al-Obaydy

Hashim

Najm

et al. 2022

IJEECS

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Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include several topics. The word tokens were separately extracted from topics related to a single group. The repeated appearance of word tokens in a document has an impact on the document's weight, which is computed using the term frequency-inverse document frequency (TF-IDF) numerical statistic. To perform the categorization process, the proposed approach employs the paper's title, abstract, and keywords, as well as the categories' topics. We exploited the K-means clustering algorithm for classifying and clustering the documents into primary categories. The K-means algorithm uses category weights to initialize the cluster centers (or centroids). Experimental results have shown that the suggested technique outperforms the k-nearest neighbors algorithm in terms of accuracy in retrieving information.

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