Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 224-230.doi: 10.11896/jsjkx.200600016

• Artificial Intelligence • Previous Articles     Next Articles

Method for Prediction of Red Blood Cells Supply Based on Improved Grasshopper Optimization Algorithm

LIU Qi1, CHEN Hong-mei2, LUO Chuan3   

  1. 1 Graduate School of Tangshan,Southwest Jiaotong University,Tangshan,Hebei 063000,China
    2 School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
    3 College of Computer Science,Sichuan University,Chengdu 610065,China
  • Received:2020-06-01 Revised:2020-08-26 Online:2021-02-15 Published:2021-02-04
  • About author:LIU Qi,born in 1996,postgraduate.His main research interests include machine learning and data mining.
    CHEN Hong-mei,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include granular calculation,rough sets and intelligent information processing.
  • Supported by:
    The National Natural Science Foundation of China(61976182,61572406,6207022096),Key Program for International S & T Cooperation of Sichuan Province(2019YFH0097) and Applied Basic Research Programs of Science and Technology Department of Sichuan Province(2019YJ0084).

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Abstract: At present,the problem of blood supply shortage is quite serious.There exists the phenomenon that short supply happen between blood stations and institutions that use blood.Aiming at such a problem,the LSTM prediction method based on the improved grasshopper optimization algorithm(GOA) is proposed in order to predict red blood cells supply in the future and provide effective guidance for workers in making blood collection plan and preparation plan.By using LSTM to capture the under-lying patterns between the historical data,the effect of predicting the future can be achieved.There are two parts of work.Firstly,aiming at the problem that the conventional GOA is easy to fall into local optimum and has a slower convergence speed,the model of refracting opposite-based learning and chaotic mapping are introduced to GOA so as to improve the global exploration capability.Secondly,in order to improve the accuracy of LSTM,it is combined with the improved GOA and evaluate the perfor-mance of the improved LSTM model by using the real data of red blood cells supply in a certain area.Comparing to the conventional LSTM,the MAE,MAPE,RMSE are reduced by 39.827 8,1.10%,55.819 1,respectively.The experimental results show that the proposed method has higher reliability.

Key words: Chaotic mapping, Grasshopper optimization algorithm, Long short-term memory, Opposite-based learning, Prediction of red blood cells supply

CLC Number: 

  • TP389
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