Improving the performance of precision poverty alleviation based on big data mining and machine learning

Abstract

Since the reform began in our country, with the rapid economic growth in recent years, the income level has grown extremely unequal, and it is difficult for the low-income poor to benefit from the rapid economic growth. The most important prerequisite for the fight against poverty is the accurate identification of the causes of poverty. To date, our country has not reached the level of maturity required to accurately study the causes of poverty in various households. However, with the rapid development of Internet technology and big data technology in recent years, the application of large-scale data technology and data extraction algorithms to poverty reduction can identify truly poor households faster and more accurately. Compared with traditional machine learning algorithms, there are no machine storage and technical constraints, can use a large amount of data and rely on multiple data samples.

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.…”

Optimization Algorithm of Big Data Mining Based on Machine Learning Model

“…In big data, all our information is stored in it and what we need to do is to analyze and process these massive data and better mine useful and effective information. In the learning process, artificial neural networks often fall into local optimization due to their large training samples, which cannot ensure the accuracy and effectiveness of big data analysis results [17][18].…”