Early Suicide Prevention: Depression Level Prediction Using Machine Learning and Deep Learning Techniques for Bangladeshi Facebook Users
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Springer Nature Singapore
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https://link.springer.com/content/pdf/10.1007/978-981-19-2445-3_52
Reference17 articles.
1. World Health Organization. Suicide Prevention. Retrieved from https://www.who.int/mental_health/prevention/suicide/suicideprevent/en
2. Al Mamun, M.A., Griffiths, M.D.: The association between Facebook addiction and depression: a pilot survey study among Bangladeshi students. Psychiatry Res. 271, 628–633 (2019). https://doi.org/10.1016/j.psychres.2018.12.039
3. Arafat, S.M.Y., Al Mamun, M.A.: Repeated suicides in the University of Dhaka (November 2018): Strategies to identify risky individuals. Asian J. Psychiatry 39, 84–85 (2019). https://doi.org/10.1016/j.ajp.2018.12.014
4. Leiva, V., Freire, A.: Towards suicide prevention: early detection of depression on social media. In: LNCS, pp. 428–436. Springer (2017). https://doi.org/10.1007/978-3-319-70284-1_34
5. Uddin, A.H., et al.: Depression analysis from social media data in Bangla language using long short term memory (LSTM) recurrent neural network technique. In: 5th IEEE IC4ME2, Rajshahi, BD (2019). https://doi.org/10.1109/IC4ME247184.2019.9036528
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1. Depression Intensity Identification using Transformer Ensemble Technique for the Resource-constrained Bengali Language;Journal of Engineering Advancements;2024-05-10
2. Detecting Level of Depression from Social Media Posts for the Low-resource Bengali Language;Journal of Engineering Advancements;2023-06-28
3. BanglaSAPM: A Deep Learning Model for Suicidal Attempt Prediction Using Social Media Content in Bangla;2022 25th International Conference on Computer and Information Technology (ICCIT);2022-12-17
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