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Journal of Globe Scientific Reports

Volume 5, 2023 - Issue 2

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Deep Learning and Computational Mathematics in Crisis Detection for International Students

by Haruto Nakamura 1,*
1
Kanazawa University, College of Human and Social Sciences, Japan
*
Author to whom correspondence should be addressed.
JGSR  2023 5(2):88; https://doi.org/10.xxxx/xxxxxx
Received: 31 May 2023 / Accepted: 1 November 2023 / Published Online: 10 December 2023
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Abstract

With the rapid development of education for overseas students in China, psychological problems caused by cross-cultural factors have become increasingly prominent. Based on the psychological questionnaire data, in order to make full use of the relationship information between students, this paper proposes a psychological crisis individual identification model based on bipartite graph convolution network model (B-GCN) to make up for the shortcomings of traditional identification methods. Because the GCN model can not realize inductive learning, this paper improves the GCN model based on the structural characteristics of psychological tests, and proposes a bipartite graph neural network model. The model can classify the students who have never appeared in the graph structure, so as to realize the early warning of psychological crisis. Experiments show that the proposed B-GCN model has a good performance.

Keywords: Graph convolutional neural network; Psychological crisis; International students; Deep learning;
Copyright: © 2023 by Nakamura. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Nakamura, H. Deep Learning and Computational Mathematics in Crisis Detection for International Students. Journal of Globe Scientific Reports, 2023, 5, 88. doi:10.xxxx/xxxxxx
AMA Style
Nakamura H. Deep Learning and Computational Mathematics in Crisis Detection for International Students. Journal of Globe Scientific Reports; 2023, 5(2):88. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Nakamura, Haruto 2023. "Deep Learning and Computational Mathematics in Crisis Detection for International Students" Journal of Globe Scientific Reports 5, no.2:88. doi:10.xxxx/xxxxxx

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  1. JONES R，FINLAY F.Medical students'experiences and perception of support following the death of a patient in the UK，and while overseas during their elective period[J].Postgraduate Medical Journal，2014，90（1060）：69-74.
  2. WANG C C，ANDRE K，GREENWOOD K M.Chinese students studying at australian universities with specific reference to nursing students：A narrative literature review[J].Nurse Education Today，2015，35（4）：609-619.
  3. Lindemann E. Symptomatology and management of acute grief[J]. Pastoral Psychology, 1944, 151(6): 155-160.
  4. Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, et al. Resilient Distributed Datasets: A Fault_Tolerant Abstraction for In-Memory Cluster Computing[C]. Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation. 2012.
  5. Wong T. Alternative prior assumption for improving the performance of naive Bayesian classifiers[J]. Data Mining & Knowledge Discovery, 2009, 18(2): 183-213.
  6. Noh, Younghee. A study on next-generation digital library using context-awareness technology[J]. Library Hi Tech, 2013, 31(2).
  7. Saarela M, Karkkainen T. Analysing Student Performance Using Sparse Data of Core Bachelor Courses[J]. Journal of Educational Data Mining, 2015, 7(6): 393-401.
  8. Ravikumar R, Aljanahi F, Rajan A, et al. Early Alert System for Detection of At-Risk Students[C]. 2018 Fifth HCT Information Technology Trends(ITT). IEEE, 2018. 138-142.
  9. Yang H, Diao J, Zhou T,et al. Research and Implement of Course Early Warning System Based on Teaching Behaviour Data[C]. Journal of Physics: Conference Series. IOP Publishing, 2019. 42-52.
  10. Yang H, Diao J, Zhou T,et al. Research and Implement of Course Early Warning System Based on Teaching Behaviour Data[C]. Journal of Physics: Conference Series. IOP Publishing, 2019. 42-52.
  11. Chattopadhyay S, Ray P, Chen H S, et al. Suicidal Risk Evaluation Using a Similarity-Based Classifier[J]. Advanced Data Mining and Applications, 2008, 51-61.
  12. Eyre H A , Eskin A , Nelson S F , et al. Genomic predictors of remission to antidepressant treatment in geriatric depression using genome-wide expression analyses: a pilot study[J]. International Journal of Geriatric Psychiatry, 2016, 31(5):510-517.
  13. J. Alpert and M. Fava. Handbook of Chronic Depression: Diagnosis and Therapeutic Management.Medical Psychiatry[M]. Marcel Dekker Incorporated,2014.
  14. Otte C . Incomplete remission in depression: Role of psychiatric and somatic comorbidity[J]. Dialogues in Clinical Neuroscience, 2008, 10(4):453-460.
  15. Zhi Nie, Pinghua Gong, Jieping Ye. Predict Risk of Relapse for Patients with Multiple Stages of Treatment of Depression[C]// Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. ACM, 2016.
  16. Schoenhuber R , Gentilini M . Anxiety and depression after mild head injury: a case control study.[J]. Journal of Neurology Neurosurgery & Psychiatry, 1988, 51(5):722-724.
  17. Dabek F , Caban J J . A Neural Network Based Model for Predicting Psychological Conditions[J]. 2015.
  18. Petersen J , Austin D , Kaye J A , et al. Unobtrusive In-Home Detection of Time Spent Out-of-Home With Applications to Loneliness and Physical Activity[J]. IEEE Journal of Biomedical and Health Informatics, 2014, 18(5):1590-1596.
  19. Austin J , Dodge H H , Riley T , et al. A Smart-Home System to Unobtrusively and Continuously Assess Loneliness in Older Adults[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2016, 4:1-11.
  20. Pulekar G , Agu E . Autonomously Sensing Loneliness and Its Interactions With Personality Traits Using Smartphones[C]// 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT). IEEE, 2016.
  21. Sanchez W., Martinez A., Campos W., Estrada H., Pelechano V., Inferring Loneliness Levels in Older Adults From Smartphones[J], Journal of Ambient Intelligence and Smart Environments, vol. 7, no. 1, pp. 85-98, 2015.
  22. Kipf T N , Welling M . Semi-Supervised Classification with Graph Convolutional Networks[J]. 2016.