An Mai

Orcid: 0000-0003-3743-0701

According to our database1, An Mai authored at least 12 papers between 2019 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2022
Approach portfolio selection for Vietnamese stock market based on nonlinear shrinkage method.
Proceedings of the 14th International Conference on Knowledge and Systems Engineering, 2022

Auto Machine Learning-Based Approach for Source Printer Identification.
Proceedings of the Recent Challenges in Intelligent Information and Database Systems, 2022

2021
Improved sparse PCA method for face and image recognition.
CoRR, 2021

Text classification problems via BERT embedding method and graph convolutional neural network.
CoRR, 2021

Microscopic printing analysis and application for classification of source printer.
Comput. Secur., 2021

Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning.
Proceedings of the Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, 2021

2020
Voting shrinkage algorithm for Covariance Matrix Estimation and its application to portfolio selection.
Proceedings of the 2020 RIVF International Conference on Computing and Communication Technologies, 2020

Rotat3D: A Knowledge Graph Embedding using Relational Rotation in 3D Vector Space.
Proceedings of the 12th International Conference on Knowledge and Systems Engineering, 2020

2019
Solve fraud detection problem by using graph based learning methods.
CoRR, 2019

An Adaptive Hash-Based Text Deduplication for ADS-B Data-Dependent Trajectory Clustering Problem.
Proceedings of the 2019 IEEE-RIVF International Conference on Computing and Communication Technologies, 2019

A Simplified Framework for Air Route Clustering Based on ADS-B Data.
Proceedings of the 2019 IEEE-RIVF International Conference on Computing and Communication Technologies, 2019

K-Segments Under Bagging approach: An experimental Study on Extremely Imbalanced Data Classification.
Proceedings of the 19th International Symposium on Communications and Information Technologies, 2019


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