Rural Financial Information Service Platform Under Smart Financial Environment

Dai, Wensheng

doi:10.1109/access.2020.3033279

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Economic environment factors are measured by regional economic development, the proportion of agriculture, agricultural labor productivity and convenience of commodity market transactions. Social environment factors include per capita education level, population density(T. Liu et al, 2020), and information technology (Dai, 2020). The descriptive statistics of influencing factors are shown in Table 2.…”

Section: Variables Of Influencing Factorsmentioning

confidence: 99%

Spatial-Temporal Evolution and Influencing Factors of Rural Financial Resource Allocation Efficiency in China

Zhang

Hua

2023

Preprint

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China is faced with challenge of improving efficiency in rural financial resource allocation in rural revitalization. This research examines the spatial-temporal evolutions of the static and dynamic allocation efficiency (SAE and DAE) of financial resources in rural China and their influencing factors. The combination of a super-slack-based measure (SBM) model with the Malmquist productivity index (MPI) is used to evaluate SAE and DAE from 2000 to 2019, respectively. Also, the spatial econometric models are applied to estimate spatial effects and effects of influencing factors on SAE or DAE. The result shows that SAE and DAE has been fluctuant de-creasing from a temporal perspective, while the differences in SAE or DAE among provinces from a spatial perspective are gradually decreasing. In particular, SAE shows a spatial distribution pattern of "high in the south and low in the northwest" and spatial siphon effect, while DAE gradually evolved into three high-efficiency poles in Xinjiang, Inner Mongolia and Sichuan. Further analysis shows that the development of economy significantly limited SAE and DAE; the ratio of deposits to loans significantly increased SAE, while the proportion of agriculture and the convenience of commodity market transactions adversely affected SAE. In addition, the regional education level significantly increased DAE. This research may help to formulate a reasonable allocation of limited rural financial resources. Classification code JEL G20; D14; J24

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Section: Variables Of Influencing Factorsmentioning

confidence: 99%

Spatial-Temporal Evolution and Influencing Factors of Rural Financial Resource Allocation Efficiency in China

Zhang

Hua

2023

Preprint

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“…Since the 1980s, the theory of China's rural financial system has been fully developed, which relies entirely on market mechanisms and emphasizes that interest rate easing could boost financial intermediaries in rural areas. (Wensheng, 2020). After the 1990s, scholars found that social and non-market factors were essential for establishing efficient financial markets.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

Can China’s digital inclusive finance help rural revitalization? A perspective based on rural economic development and income disparity

Xiong

Fan

et al. 2022

Front. Environ. Sci.

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The development of digital inclusive finance in China can help alleviate the problems of the “three rural areas” and contribute to rural revitalization. The impact of digital inclusive finance on rural revitalization is examined by multiple linear regression, mediated effect model, and threshold effect model in a sample of 30 provinces in China from 2011 to 2020. It is found that (1) the digital inclusive finance index and its sub-indicators - the breadth of digital financial coverage and depth of digital financial use - can promote rural revitalization in China, but the digitalization degree of its sub-indicators does not have a significant linear effect on rural revitalization, but a “U” shaped relationship of first inhibiting and then promoting, with 2017 as the boundary. (2) The regional heterogeneity test reveals that the promotion effect of the digital financial inclusion index and its sub-indicators of digital financial coverage breadth and digital financial usage depth on rural revitalization is more significant in the eastern coastal and central inland provinces, and insignificant in the western remote areas. (3) The mediating effect test finds that digital inclusive finance in China can contribute to rural revitalization by promoting economic growth in rural areas and narrowing the income gap between urban and rural areas. (4) The threshold effect test reveals that there is a single threshold effect of digital inclusive finance and its three sub-indicators on rural revitalization in China. When the digital inclusive finance index and the three sub-dimensions are below the threshold value, they have a facilitating effect on rural revitalization, and after crossing the threshold value, their facilitating effect on rural revitalization becomes more significant. Based on the empirical analysis results, this paper puts forward suggestions for improving the development mechanism of digital inclusive finance, formulating differentiated financial support policies and strengthening financial supervision according to regional differences, and establishing a long-term mechanism for rural revitalization, to promote China’s rural revitalization.

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“…In terms of the Self Organizing Map (SOM) for extracting features, Zhang et al suggested another unmonitored approach (Wensheng, 2020). This methodology included two essential developments, the retrieval of sub‐local images, as seen by their objectives of suspense and use of them from various angles, colouring, surface, and form.…”

Section: Background Ovarian Cancer Area Using Internet Of Medical Thingsmentioning

confidence: 99%

Feature optimization and identification of ovarian cancer using internet of medical things

Ghazal

Taleb

2022

Expert Systems

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Ovarian cancer (OC) is one kind of tumour that impacts women's ovaries and is hard to diagnose in the initial phase as a primary cause of cancer death. The ovarian cancer information generated by the Clinical Network has been used, and the Self Organizing Map (SOM) and Optimized Neural Networks have suggested a new method for the distinction between ovarian cancer and remaining cancer. Feature optimization and identification of the ovarian cancer (FOI-OV) framework are proposed in this research. The SOM algorithm has also been used separately to improve the functional subset, with understandable and intriguing information from participants' health information steps. The SOM-based collection appears to be tolerable in guided learning strategies due to the lack of different classifiers, which would direct the quest for knowledge specific to the classification algorithm. The classification technique will classify data from ovarian cancer as benign/malignant. By optimizing Neural Network configuration, Advanced Harmony Searching Optimization (AHSO) can enhance the ovarian cancer detection method compared with other methods. This research's suggested model can also diagnose cancer with high precision, and low root means square error (RMSE) early. With 94% precision and 0.029%, RMSE, SOM, and NN techniques have shown identification and precision in ovarian cancer. Optimization (AHSO) has provided an efficient classification approach with a better failure rate.

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Rural Financial Information Service Platform Under Smart Financial Environment

Cited by 9 publications

References 25 publications

Spatial-Temporal Evolution and Influencing Factors of Rural Financial Resource Allocation Efficiency in China

Spatial-Temporal Evolution and Influencing Factors of Rural Financial Resource Allocation Efficiency in China

Can China’s digital inclusive finance help rural revitalization? A perspective based on rural economic development and income disparity

Feature optimization and identification of ovarian cancer using internet of medical things

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