Son V. T. Dao
Vietnam National University, Ho Chi Minh City
26 Papers
73 Citations
Son V. T. Dao is an academic researcher from Vietnam National University, Ho Chi Minh City. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 4, co-authored 19 publications. Previous affiliations of Son V. T. Dao include Kindai University & International University, Cambodia.
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Papers
Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection
TL;DR: This research aims at detecting early disease on plant leaves with small disease blobs, which can only be detected with higher resolution images, by an artificial neural network (ANN) approach and compares the results with another approach using popular CNN models enhanced with transfer learning.
A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic
TL;DR: In this article, a machine learning model was proposed to predict early onset of diabetes patients using wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an adaptive particle swam optimization (APSO) to optimize the multilayer perceptron to reduce the number of required input attributes.
Application of Machine Learning in Epileptic Seizure Detection
TL;DR: In this article , a machine learning-based approach for detecting epileptic seizures in EEG signals is presented. But, the proposed method is not suitable for the detection of epileptic seizure in clinical applications.
A 16 Mfps 165kpixel backside-illuminated CCD
Takeharu Etoh,Dung H. Nguyen,Son V. T. Dao,Cuong L. Vo,Masatoshi Tanaka,Kohsei Takehara,Tomoo Okinaka,Harry van Kuijk,Wilco Klaassens,Jan T. Bosiers,Michael Lesser,David Ouellette,Hirotaka Maruyama,Tetsuya Hayashida,Toshiki Arai +14 more
- 07 Apr 2011
TL;DR: TheISIS-V16, a backside-illuminated image sensor mounting the ISIS structure and the CCM, charge-carrier multiplication, on the front side, is developed by incorporating technologies to increase the frame rate with those to achieve very high sensitivity, which was confirmed by evaluation of ISIS-V12.
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A Feature Selection Approach for Fall Detection Using Various Machine Learning Classifiers
TL;DR: Wang et al. as mentioned in this paper proposed a novel feature subset selection to reduce the number of effective input attributes based on a hybridized metaheuristic - an adaptive particle swarm and Grey Wolf Optimization (APGWO).