Work place: VNU University of Engineering and Technology, Ha Noi City, Vietnam
Research Interests: Swarm Intelligence, Machine Learning, Artificial Intelligence, Computer Science & Information Technology
Prof. PhD. Nguyen Thanh Thuy received B.S degree in Mathematics, and Ph.D. degree in Computer Science from Hanoi University of Technology, Vietnam, in 1982 and 1987. He has been the professor of Vietnam since 2010. Now he is a Vice Rector of VNU University of Engineering and Technology, Ha Noi city, Vietnam. He majors in Machine Learning, Intelligence Computing and Computer Science.
DOI: https://doi.org/10.5815/ijitcs.2015.03.04, Pub. Date: 8 Feb. 2015
Facial Expression is a key component in evaluating a person's feelings, intentions and characteristics. Facial Expression is an important part of human-computer interaction and has the potential to play an equal important role in human-computer interaction. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) applying for facial expression classification. We propose the ANN_KNN model using ANN and K-NN classifier. ICA is used to extract facial features. The ratios feature is the input of K-NN classifier. We apply ANN_KNN model for seven basic facial expression classifications (anger, fear, surprise, sad, happy, disgust and neutral) on JAFEE database. The classifying precision 92.38% has been showed the feasibility of our proposal model.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2012.05.05, Pub. Date: 8 May 2012
Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. Firstly, we separate the image into many sub-images based on the features of images. Each sub-image is classified into the responsive class by an ANN. Finally, SVM has been compiled all the classify result of ANN. Our proposal classification model has brought together many ANN and one SVM. Let it denote ANN_SVM. ANN_SVM has been applied for Roman numerals recognition application and the precision rate is 86%. The experimental results show the feasibility of our proposal model.[...] Read more.
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