Influence of Internet-based Social Big Data on Personal Credit Reporting

Li, Meng; Zhang, Shuang; Wang, Fuqiang

doi:10.47116/apjcri.2020.07.05

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…To apply big data analytics to village resources research for the Urban Regeneration New Deal Project, it was important to take a value-oriented approach regarding which areas it could actually and specifically benefit. Big data is quite attractive in real estate (Knapp, 2013;WEF, 2012), as it allows a comprehensive analysis to be conducted that can represent society by quickly and easily collecting a variety of data closely related to daily life (Rizk, 2018;Li et al, 2020).…”

Section: Methods Of Big Data Analytics For Village Resources Researchmentioning

confidence: 99%

Discovery of village resources in urban regeneration project based on big data analytics

Kim¹,

Cho²

2023

Int. j. adv. appl. sci.

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In this study, we examined resources at the local level, conducted status research, and explored alternatives based on big data analytics to regenerate a village in a small area in an information communications technology-based urban regeneration project. In particular, we used big data analytics to analyze the current conditions of the local area and provided a case demonstrating how connecting with a local university for expertise could solve current local problems. The main results can be summarized in three dimensions. First, there is job creation in villages using woodworking. At the center of it, it is possible to link with youth entrepreneurship by using the clubs of the local university (Seangnori Research Institute). Quantitatively, it is possible to design offices and offline stores for the sale of developed products, and qualitatively, it can lead to company growth by increasing the business volume of pre-land transportation-type social enterprises and expansion of regional governance through regional exchanges. Second, in terms of providing new information and education, it is possible to provide the Chungnam Appropriate Technology Federation, a non-profit organization in the region, for smooth education and practice. In particular, it is possible to promote the growth of the council through continuous product development. It is possible to strengthen the cooperative system. Third, the effect of the influx of population within the region can be obtained. As publicity and awareness of the visionary workshop project have been expanded, the youth independent talent nurturing process is systematized, and from this, students graduating from local universities can lead to youth jobs in the local area without looking for jobs in other areas, forming a virtuous cycle system. To this end, we divided real estate big data into the categories of system, technology and data, law and policy, structured data of real estate information and unstructured data of social media, web log data, smart device, and real estate policy, development, appraisal, and local analysis to apply to the case area and suggest implications.

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Section: Methods Of Big Data Analytics For Village Resources Researchmentioning

confidence: 99%

Discovery of village resources in urban regeneration project based on big data analytics

Kim¹,

Cho²

2023

Int. j. adv. appl. sci.

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“…Since the widespread application of Internet technology, people are faced with the severe problem of information explosion. The information on the Internet keeps increasing, and its growth momentum is rapid, with geometric magnitude [1][2][3]. The Internet can carry amazing information, and the world is submerged in information.…”

Section: Introductionmentioning

confidence: 99%

Text Classification of Mixed Model Based on Deep Learning

Lee

2023

Teh. glas. (Online)

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At present, deep learning has been widely used many fields, but the research on text classification is still relatively few. This paper makes full use of the good learning characteristics of deep learning, proposes a hybrid model based on deep learning, and designs a text classifier based on the hybrid model. This hybrid model uses two common deep learning models, sparse automatic encoder and deep confidence network, to mix. The hybrid model is mainly composed of three parts, the first two layers are constructed by sparse automatic encoder, the middle layer is a three-layer depth Convolutional Neural Network (CNN), and finally Softmax regression is used as the classification layer. In order to test the classification performance of the classifier based on deep learning hybrid model, relevant experiments were conducted on English data set 20Newsgroup and Chinese data set Fudan University Chinese Corpus. In the English text classification experiment, the classifier based on deep learning hybrid model is used to classify, and a high classification accuracy rate is obtained. In order to further verify the superiority of its performance, a comparative experiment with naive Bayes classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier demonstrates that the classification effect of the classifier based on deep learning hybrid model is better than that of naive Bayes classifier, KNN classifier and support vector machine classifier. In the experiment of Chinese text classification, the Chinese corpus of Fudan University is tested, and a good classification effect is obtained. The influence of different parameter settings on the classification accuracy is discussed.

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Influence of Internet-based Social Big Data on Personal Credit Reporting

Cited by 2 publications

References 6 publications

Discovery of village resources in urban regeneration project based on big data analytics

Discovery of village resources in urban regeneration project based on big data analytics

Text Classification of Mixed Model Based on Deep Learning

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