scholarly journals A Novel DBSCAN Clustering Algorithm via Edge Computing-Based Deep Neural Network Model for Targeted Poverty Alleviation Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Liu ◽  
Yang Liu ◽  
Zhenquan Qin ◽  
Ran Zhang ◽  
Zheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Poverty Alleviation ◽  
Deep Neural Network ◽  
Clustering Algorithm ◽  
Edge Computing ◽  
Dbscan Clustering ◽  
The Poor ◽  
Poor Households ◽  
Big Data Technology

Big data technology has been developed rapidly in recent years. The performance improvement mechanism of targeted poverty alleviation is studied through the big data technology to further promote the comprehensive application of big data technology in poverty alleviation and development. Using the data mining knowledge to accurately identify the poor population under the framework of big data, compared with the traditional identification method, it is obviously more accurate and persuasive, which is also helpful to find out the real causes of poverty and assist the poor residents in the future. In the current targeted poverty alleviation work, the identification of poor households and the matching of assistance measures are mainly through the visiting of village cadres and the establishment of documents. Traditional methods are time-consuming, laborious, and difficult to manage. It always omits lots of useful family information. Therefore, new technologies need to be introduced to realize intelligent identification of poverty-stricken households and reduce labor costs. In this paper, we introduce a novel DBSCAN clustering algorithm via the edge computing-based deep neural network model for targeted poverty alleviation. First, we deploy an edge computing-based deep neural network model. Then, in this constructed model, we execute data mining for the poverty-stricken family. In this paper, the DBSCAN clustering algorithm is used to excavate the poverty features of the poor households and complete the intelligent identification of the poor households. In view of the current situation of high-dimensional and large-volume poverty alleviation data, the algorithm uses the relative density difference of grid to divide the data space into regions with different densities and adopts the DBSCAN algorithm to cluster the above result, which improves the accuracy of DBSCAN. This avoids the need for DBSCAN to traverse all data when searching for density connections. Finally, the proposed method is utilized for analyzing and mining the poverty alleviation data. The average accuracy is more than 96%. The average F -measure, NMI, and PRE values exceed 90%. The results show that it provides decision support for precise matching and intelligent pairing of village cadres in poverty alleviation work.

scholarly journals A Clustering Algorithm via Density Perception and Hierarchical Aggregation Based on Urban Multimodal Big Data for Identifying and Analyzing Categories of Poverty-Stricken Households in China

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hui Liu ◽  
Yang Liu ◽  
Ran Zhang ◽  
Xia Wu
Keyword(s):  
Big Data ◽  
Poverty Alleviation ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Critical Role ◽  
Land Resource ◽  
Human Society ◽  
The Sustainable Development ◽  
Data Space ◽  
Dbscan Clustering

Nowadays, urban multimodal big data are freely available to the public due to the growing number of cities, which plays a critical role in many fields such as transportation, education, medical treatment, and land resource management. The successful completion of poverty-relief work can greatly improve the quality of people’s life and ensure the sustainable development of the society. Poverty is a severe challenge for human society. It is of great significance to apply machine learning to mine different categories of poverty-stricken households and further provide decision support for poverty alleviation. Traditional poverty alleviation methods need to consume a lot of manpower, material resources, and financial resources. Based on the density-based spatial clustering of applications with noise (DBSCAN), this paper designs the hierarchical DBSCAN clustering algorithm to identify and analyze the categories of poverty-stricken households in China. First, the proposed method adjusts the neighborhood radius dynamically for dividing the data space into several initial clusters with different densities. Then, neighbor clusters are identified by the border and inner distances constantly and aggregated recursively to form new clusters. Based on the idea of division and aggregation, the proposed method can recognize clusters of different forms and deal with noises effectively in the data space with imbalanced density distribution. The experiments indicate that the method has the ideal performance of clustering, which identifies the commonness and difference in characteristics of poverty-stricken households reasonably. In terms of the specific indicator “Accuracy,” the accuracy increases by 2.3% compared with other methods.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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