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

Abstract

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.

Contributions of the 5G Network with Respect to Poverty (SDG1), Systematic Literature Review

“…These technologies can drive the development of new products and services, generate employment opportunities in emerging sectors, and foster the creation of new businesses, which in turn helps to reduce poverty. [119,[150][151][152][153][154][155][156][157][158][159][160][161][162][163][164][165][166][167][168][169] Agriculture and food security agriculture Agriculture is a crucial sector for combating poverty, especially in rural areas. The 5G network can improve agricultural efficiency and productivity by enabling the use of advanced technologies such as connected sensors, smart irrigation systems, and remote crop monitoring.…”

“…Big data [159,161,164] Access to information and education E-learning Image processing Mobile application [130,131,133] [170] [132] RQ3. What are the applications technologies based on 5G networks with the greatest contribution to poverty reduction?…”

Utilities of Artificial Intelligence in Poverty Prediction: A Review

“…The aim of the paper is a development of a new model called the DBSCAN Clustering Algorithm via Edge Computing-Based Deep Neural Network for poor households identification. This method can conduct identification via determining the poverty features of poor households [109].…”

Optimization of Cold Chain Distribution Route with Mixed Time Window considering Customer Priority

“…e DBSCAN clustering algorithm is a set of neighborhoods that describe the tightness of the sample set, and parameter (ε, MinPts) is used to describe the tightness of the sample distribution in the neighborhood [20], where ε refers to the neighborhood distance threshold of a certain sample and MinPts refers to the threshold of the number of samples in the neighborhood of a certain sample with a distance of ε. If the sample set is assumed to be D � (x 1 , x 2 , ..., x m ), then the density description of the DBSCAN clustering algorithm is defined as follows:…”

“…Equation (19) denotes that the refrigerated vehicle starts from the distribution center to complete the distribution task and then returns to the distribution center. Equation (20) signifies that any customer is only allowed to set out once for refrigerated vehicles. Equation (21) describes that any customer only allows the refrigerated vehicle to arrive once.…”