Egyptian Social Insurance Big Data Mining Using Supervised Learning Algorithms

Senousy, Youssef; Hanna, Wael K.; Shehab, Abdulaziz; Riad, A. M.; El-Bakry, Hazem M.; Elkhamisy, Nashaat

doi:10.18280/ria.330504

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Machine-learning models have contribute to SIS. The author in [19] proposed the use of three algorithms, namely the decision tree, native Bayes, and CN2 rule induction, to predict classification based on some of the social insurance features for people. The three algorithms obtained high accuracy results when classifying.…”

Section: Related Workmentioning

confidence: 99%

Using Fuzzy Time Series Models to Estimate the Cost of Benefits of Egyptian Social Insurance System

Raouf

Elsaieed

2022

IJFIS

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In recent years, many methods have been proposed to forecast data in different fields based on successful fuzzy time series models (FTS). Egyptian social insurance systems (SISs) need support to optimally define and estimate yearly total benefits (pensions), which helps the actuaries who are responsible for the system make optimal decisions. Given that the total benefits have not been forecasted before by prediction methods, this paper proposes FTS models by Chen, Cheng, Yu, and Song to forecast Egyptian social insurance benefits, proposes Huarng for appropriate partition lengths, and constructs the interval length using the difference in the transformation data method, given that the data has not been stationary in recent years and has increased significantly. The proposed approach is based on experiments implemented using four models with interval length partitions of 5, 10, 50, 100, and Huarng partitions of 465. The results show great progress in the performance of yearly benefit forecasting, especially in the Chen model with a Huarng 465 partition, which has high accuracy prediction with low error when training and testing data.

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Section: Related Workmentioning

confidence: 99%

Using Fuzzy Time Series Models to Estimate the Cost of Benefits of Egyptian Social Insurance System

Raouf

Elsaieed

2022

IJFIS

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“…Traditionally, data mining generally deals with the data from a single source. In reality, the original data may come from multiple sources in different fields [13]. The conventional multi-source data fusion is comparable to the integration of multiple datasets, which are consistent, accurate, and meaningful.…”

Section: Data Processing and Multi-source Fusionmentioning

confidence: 99%

Prediction of English Scores of College Students Based on Multi-source Data Fusion and Social Behavior Analysis

Zhao¹,

Ren²,

Zheng³

2020

RIA

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Multi-source data fusion is the premise of applying big data technology in specific fields. Inspired by the theory on multi-source data fusion, this paper fuses various data on college students, including motion trajectories, consumptions, and social behaviors, and adopts support vector machine (SVM), a machine learning (ML) classifier to predict the English scores of college students. The behavior trajectories were taken into account, because this type of data represents the social similarity between students. Specifically, the behavioral features of college students were extracted, and subject to principal component analysis (PCA). Based on these features, the correlation between student score and social relation was analyzed, and used to predict the English scores of college students. Experimental results show that our method can accurately reflect the relationship between the social behaviors and course scores of college students.

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“…Big data and its diverse applications have changed people's life styles and work methods, also brought new opportunities for the innovation in education [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The Chinese government has attached great importance to education big data; therefore, a series of policies have been issued to promote the construction of education big data, and give guidance to the management and utilization of education big data.…”

Section: Introductionmentioning

confidence: 99%

Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data

Cao

2021

Int. J. Emerg. Technol. Learn.

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With the advent of the big data era, significant changes have taken place in every aspect of education. To effectively evaluate the academic performance of college students, this paper firstly establishes a scientific evaluation index system for student portrait. Taking the course Object-Oriented Programming as an example, the authors collected various data on the academic performance of college students. The collected data were normalized, and the weight of each evaluation index was determined through analytic hierarchy process (AHP). Next, a fuzzy evaluation model was constructed based on big data, and used to assess each dimension of college students’ academic performance. The evaluation reveals the problems of college students in learning and practice, and helps to generate the portrait of each student. The research results promote the realization of personalized education.

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Egyptian Social Insurance Big Data Mining Using Supervised Learning Algorithms

Cited by 8 publications

References 7 publications

Using Fuzzy Time Series Models to Estimate the Cost of Benefits of Egyptian Social Insurance System

Using Fuzzy Time Series Models to Estimate the Cost of Benefits of Egyptian Social Insurance System

Prediction of English Scores of College Students Based on Multi-source Data Fusion and Social Behavior Analysis

Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data

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