College graduates employment prediction based on Principal Component Analysis and the combined adaptive boosting and the back propagation neural network algorithm

Li, Xiangyang; Bao, Juan; Jiang, Yan; Ke, Zhunping; Wang, Changbo

doi:10.1109/icnc.2015.7378151

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…In machine learning, a probabilistic classifier based on baye's [20] theorem is the naïve bayes classifiers. The provisional and minimal probabilities of two arbitrary events are related using bayes theorem.…”

Section: Naïve Baye's Classifiermentioning

confidence: 99%

Down syndrome detection using modified adaboost algorithm

Rajesh

2021

IJECE

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In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for down syndrome prevention. In this work, recognition of Down syndrome utilizes a set of facial expression images. Solid geometric descriptor is employed for extracting the facial features from the image set. An AdaBoost method is practiced to gather the required data sets and for the categorization. The extracted information is then assigned and used to instruct the Neural Network using Backpropagation algorithm. This work recorded that the presented model meets the requirement with 98.67% accuracy.

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Section: Naïve Baye's Classifiermentioning

confidence: 99%

Down syndrome detection using modified adaboost algorithm

Rajesh

2021

IJECE

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“…In Ref. [3], college graduate employment prediction was studied based on principal component analysis and neural networks. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Combining Fourier Fractional GM(1,1|sin) Model with Rat Swarm Optimizer for Employment Rate Prediction

Cai,

Lei,

et al. 2024

IEEJ Transactions Elec Engng

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Accurate prediction of employment rate of graduated students can greatly help education authorities to make informed decisions as well as for universities to adjust their teaching plans. Unfortunately, prediction of the employment rate of graduated students is still a difficult problem because the historical employment rate data exhibits fluctuations. In this paper, a new method is proposed for employment rate prediction using fractional gray GM(1,1sin) model, which aims to alleviate the effect of data fluctuation on prediction accuracy and increase the contribution of new data in the prediction procedure. To further decrease prediction error, a Fourier series is adopted to model the residual series. The proposed model, called Fourier Fractional GM(1,1sin) Model (FFGMsin), is used to predict the employment rate of graduated students of Yanshan University from 2010 to 2019. Results show that the proposed method can obtain more accurate prediction results than GM(1,1) model and its variants. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Geometric Average Weakening BufferGM(1,1) and its Application in Prediction of Employment Rate of College Graduates

Guo

2022

IEEJ Transactions Elec Engng

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Prediction of the employment rate of college graduates is very important not only for education department to make suitable decision but also for university to adjust its teaching plan. However, it is a difficult task to accurately predict the employment rate of college graduates because it is affected by many factors, making the original data fluctuant. In this paper, a simple and effective prediction approach is proposed to predict employment rate of college graduates using GM (1,1). To attenuate effect of data fluctuation and improve prediction accuracy of GM (1,1) model, the geometric average weakening buffer operator (GAWBO) is first applied to original employment rate data, and then GM (1,1) model is used to predict employment rate. The prediction experiment of Yanshan university employment rate from 2010 to 2019 is performed using the proposed method. Results show that the proposed method can obtain more accurate prediction result than GM (1,1). © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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College graduates employment prediction based on Principal Component Analysis and the combined adaptive boosting and the back propagation neural network algorithm

Cited by 4 publications

References 1 publication

Down syndrome detection using modified adaboost algorithm

Down syndrome detection using modified adaboost algorithm

Combining Fourier Fractional GM(1,1|sin) Model with Rat Swarm Optimizer for Employment Rate Prediction

Geometric Average Weakening BufferGM(1,1) and its Application in Prediction of Employment Rate of College Graduates

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