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Thu Huong L.T.,Vietnam National University of Agriculture | Nam N.H.,Institute of Materials Science | Doan D.H.,Institute of Materials Science | My Nhung H.T.,Hanoi University of Science | And 5 more authors.
Materials Chemistry and Physics | Year: 2016

Study and development of drug delivery nanosystem for cancer treatment are attracting great attention in recent years. In this work, we studied the role of folic acid as a targeting factor on magnetic nanoparticle Fe3O4 based curcumin loading nanosystem. Characteristics of the nanosystems were investigated by Fourier transform infrared spectroscopy (FTIR) and field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), thermal gravimetric analysis (TGA) and vibrating sample magnetometer (VSM), while targeting role of folic was accessed in vivo on tumor bearing mice. The results showed that folate attached Fe3O4 based curcumin loading nanosystem has very small size and exhibits better targeting effect compared to the counterpart without folate. In addition, magnetic induction heating of this nanosystem evidenced its potential for cancer hyperthermia. © 2016 Elsevier B.V. All rights reserved. Source


Manh N.Q.,Hanoi University of Science and Technology | Tuan N.D.,Hanoi University of Science and Technology | Sang D.V.,Hanoi University of Science and Technology | Binh H.T.T.,Hanoi University of Science and Technology | Thuy N.T.,Vietnam National University of Agriculture
Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015 | Year: 2015

Social media mining from Internet has been an emerging research topic. The problem is challenging because of massive data contents from various sources, especially image data from user upload. In recent years, dictionary learning based image classification has been widely studied and gained significant success. In this paper, we propose a framework for automatic detection of interested uniforms in image streams from social networks. The systems is composed of a powerful feature extraction module based on dense SIFT feature and a state-of-the-art discriminative dictionary learning approach. Beside that, a parallel implementation of feature extraction is deployed to make the system work real time. An extensive set of experiments has been conducted on four real-life datasets. The experimental results show that we can obtain the detection rate up to 100% on some datasets. We also get real time performance with a speed of image stream of about 40 images per second. The framework can be applied to emerging applications such as uniform detection, automated image tagging, content base image retrieval or online advertisement based on image content. © 2015 IEEE. Source


Tuan N.D.,Hanoi University of Science and Technology | Manh N.Q.,Hanoi University of Science and Technology | Sang D.V.,Hanoi University of Science and Technology | Binh H.T.T.,Hanoi University of Science and Technology | Thuy N.T.,Vietnam National University of Agriculture
Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015 | Year: 2015

Dictionary learning (DL) approach has been successfully applied to many pattern classification problems. Sparse property has played an important role in the success of DL-based classification models. However, the sparsity constraints make the learning problem expensive. Recently, there has been an emerged trend in relaxing the sparsity constraints by using L2-norm constraint. The new approach has shown its advantages in both accuracy and classification time. However, the relationship between the quality of the data and the dictionary learning issues that affect the performance of the system has not been investigated. In this paper, we present a comparative study on non-sparse coding dictionary learning for pattern classification. We then propose a dictionary learning model with a non-sparsity constraint on representation coefficients using L2-norm. Our experimental results on three popular benchmark datasets for image classification show that our proposed model can outperform state-of-the-art models and be a promising approach for dictionary learning based classification. © 2015 IEEE. Source


Sang D.V.,Hanoi University of Science and Technology | Loi M.D.,Hanoi University of Science and Technology | Quang N.T.,Hanoi University of Science and Technology | Binh H.T.T.,Hanoi University of Science and Technology | Thuy N.T.,Vietnam National University of Agriculture
ACM International Conference Proceeding Series | Year: 2014

Semantic image segmentation is a major and challenging problem in computer vision, which has been widely researched over decades. Recent approaches attempt to exploit contextual information at different levels to improve the segmentation results. In this paper, we propose a new approach for combining semantic texton forests (STFs) and Markov random fields (MRFs) for improving segmentation. STFs allow fast computing of texton codebooks for powerful low-level image feature description. MRFs, with the most effective algorithm in message passing for training, will smooth out the segmentation results of STFs using pairwise coherent information between neighboring pixels. We evaluate the performance of the proposed method on two wellknown benchmark datasets including the 21-class MSRC dataset and the VOC 2007 dataset. The experimental results show that our method impressively improved the segmentation results of STFs. Especially, our method successfully recognizes many challenging image regions that STFs failed to do. Copyright 2014 ACM. Source


Quang N.T.,Hanoi University of Science and Technology | Sang D.V.,Hanoi University of Science and Technology | Thuy N.T.,Vietnam National University of Agriculture | Binh H.T.T.,Hanoi University of Science and Technology
ACM International Conference Proceeding Series | Year: 2015

Detection of buildings in aerial images is an important and challenging task in computer vision and aerial image interpretation. This paper presents an efficient approach that combines Random forest (RF) and a fully connected conditional random field (CRF) on various features for the detection and segmentation of buildings at pixel level. RF allows one to learn extremely fast on big aerial image data. The unary potentials given by RF are then combined in a fully connected conditional random field model for pixelwise classification. The use of high dimensional Gaussian filter for pairwise potentials makes the inference tractable while obtaining high classification accuracy. Experiments have been conducted on a challenging aerial image dataset from a recent ISPRS Semantic Labeling Contest [9]. We obtained state-of-the-art accuracy with a reasonable computation time. © 2015 ACM. Source

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