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Table 1. Machine learning techniques with FRM applications. References



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Table 1.
Machine learning techniques with FRM applications.
References 
[1] P. O. J. Kelliher, D. Wilmot, J. Vij, and P. J. M. Klumpes, "A common risk classification 
system for the actuarial profession'' 
[3] W.-Y. Lin, Y.-H. Hu, and C.-F. Tsai, "Machine learning in financial crisis prediction: A 
survey,'' IEEE Trans. Syst., Man, Cybern. 
[4] J. Garrido, C. Genest, and J. Schulz, "Generalized linear models for dependent frequency 
and severity of insurance claims'' 
RNN BASED SPELL CORRECTION MODEL FOR
AZERBAIJANI LANGUAGE 
Sona Mehdizada 
Baku Higher Oil School 
Baku, Azerbaijan 
sona.mehtizada.std@bhos.edu.az 
Supervisor: Ramil Shukurov 
Keywords:
Natural Language Processing, Recurrent Neural Networks, Spell Correction, 
Attention Mechanism
 
Abstract 
The spell correction system carries utmost importance in the creation of documents in 
any language. It is software that detects orthographic mistakes and suggests the correct 
spelling of the word. Due to the issue of not having a proper spelling system for Azerbaijan 
language, books, documents, or emails should be proofread and checked for typing errors by 
other people which is also not as reliable by virtue of human factor and inefficiency in terms of 
time. Compared to the other languages, Azerbaijan language has more complex morphological 
Machine Learning
based learning methods
Supervised
Regression
Volatility Forecasting, 
Claims Modelling,
Classification
Fraud Detection,
Credit Scoring & Bankruptcy 
prediction
Unsupervised
Clustering
Credit Scoring & Bankruptcy 
prediction
Anomaly Detection
Fraud Detection
[2] T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity,'' J. 
Econometrics, 1986. 



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