Songyot Nakariyakul

Songyot Nakariyakul
Thammasat University · Department of Electrical and Computer Engineering

PhD

About

39
Publications
2,788
Reads
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574
Citations
Introduction
Songyot Nakariyakul received the B.S. degree in electrical engineering from Columbia University, NY, USA, in 2001, and the M.S. and Ph.D. degrees in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, USA in 2003 and 2007, respectively. He is currently a faculty member in the Department of Electrical and Computer Engineering at Thammasat University, Thailand. His research interests include feature selection, hyperspectral image processing, and bioinformatics.
Skills and Expertise
Additional affiliations
January 2014 - present
Thammasat University
Position
  • Professor (Associate)
August 2010 - July 2011
Shanghai Institutes for Biological Sciences
Position
  • PostDoc Position
September 2009 - January 2014
Thammasat University
Position
  • Professor (Assistant)

Publications

Publications (39)
Article
Full-text available
This paper addresses the high-dimensional classification problem, which is very important in machine learning. When the number of features of the data is very high, the classification performance of a given classifier can degrade because there are not enough samples for training. One of the solutions to cope with this problem is to perform feature...
Article
Full-text available
We address gene selection and machine learning methods for cancer classification using microarray gene expression data. Due to the high dimensionality of microarray data, traditional gene selection algorithms are filter-based, focusing on intrinsic properties of the data such as distance, dependency, and correlation. These methods are fast but sele...
Article
With the rapid growth of high-dimensional data sets in recent years, the need for reducing the dimensionality of data has grown significantly. Although wrapper approaches tend to achieve higher accuracy rates than filter techniques for the same number of selected features, only a few wrapper algorithms are applicable for high-dimensional data sets...
Conference Paper
Many Thai people with visual impairments make a living by selling lottery tickets. To assist the blind in selling their tickets, we present a new Android app called LottoTU for scanning and reading aloud Thai lottery numbers for the blind. Our system includes lottery number segmentation, thresholding using Otsu’s method, digit recognition using the...
Conference Paper
This paper introduces a new Android navigation app called NavTU for Thai people with visual impairments. Our NavTU app is designed to promote safe and independent travel for Thai people who are blind or have low vision. It is the integration of the GPS-based and camera-based technologies that not only helps the blind pedestrians navigate outdoors f...
Conference Paper
Gene selection is an important pre-processing step in microarray analysis and classification. While traditional gene selection algorithms focus on identifying relevant and irredundant genes, we present a new gene selection algorithm that chooses gene subsets based on their interaction information. Many individual genes may be irrelevant with the cl...
Conference Paper
Two new sequential search algorithms for feature selection in hyperspectral remote sensing images are proposed. Since many wavebands in hyperspectral images are redundant and irrelevant, the use of feature selection to improve classification results is highly needed. First, we present a new generalized steepest ascent (GSA) feature selection techni...
Article
The branch and bound algorithm is an optimal feature selection method that is well-known for its computational efficiency. However, when the dimensionality of the original feature space is large, the computational time of the branch and bound algorithm becomes very excessive. If the optimality of the solution is allowed to be compromised, one can f...
Article
The branch and bound algorithm is an optimal feature selection method that is well-known for its computational efficiency. However, when the dimensionality of the original feature space is large, the computational time of the branch and bound algorithm becomes very excessive. If the optimality of the solution is allowed to be compromised, one can f...
Article
This work presents a statistical method for internal damage inspection of almond nuts based on advanced waveband selection and supervised pattern recognition techniques using near-infrared spectral data. Our proposed method employs an optimal adaptive branch and bound algorithm to select a small set of wavebands for use in a support vector machine...
Article
The PDZ domain is one of the most ubiquitous protein domains that are involved in coordinating signaling complex formation and protein networking by reversibly interacting with multiple binding partners. It has been linked to many devastating diseases such as avian influenza, Fraser syndrome, Usher syndrome and Dejerine-Sottas neuropathy. Understan...
Article
We present a new fast spatial averaging technique that efficiently implements operations for spatial averaging or mean filtering. To perform spatial averaging of an M×N image with an averaging filter of size m×n, our proposed method requires approximately 4MN additions and no division. This is very promising, since the major computations required b...
Conference Paper
The PDZ domain is one of the largest families of protein domains that are involved in targeting and routing specific proteins in signaling pathways. PDZ domains mediate protein-protein interactions by binding the C-terminal peptides of their target proteins. Using the dipeptide feature encoding, we develop a PDZ domain interaction predictor using a...
Article
Detecting thermophilic proteins is an important task for designing stable protein engineering in interested temperatures. In this work, we develop a simple but efficient method to classify thermophilic proteins from mesophilic ones using the amino acid and dipeptide compositions. Since most of the amino acid and dipeptide compositions are redundant...
Article
Hyperspectral transmission spectra of almond nuts are studied for discriminating internally damaged almond nuts from normal ones. We introduce a novel internally damaged almond detection method that requires only two sets of ratio features (the ratio of the responses at two different spectral bands) for classification. Our proposed method avoids ex...
Chapter
Detection of concealed damage in almonds is an important production inspection application. Internally damaged almonds are not easily distinguished from normal ones by their external appearances, and, when cooked, they taste bitter. Prior study showed that using the whole spectrum of hyperspectral data from 700-1400 nm could distinguish internally...
Chapter
Detection of internal damage in almonds is an important production inspection application. Internally damaged almonds are not easily distinguished from normal ones by their external appearances, and, when cooked, they taste bitter. Prior study showed that using the whole spectrum of hyperspectral data from 700-1400 nm could distinguish internally d...
Conference Paper
Multispectral and polarimetric data have been shown to provide detailed information useful for automatic target recognition applications. A major limitation of using these data in remote sensing is that they often consist of a large number of features with an inadequate number of samples. To reduce the number of features, we thus present a new gene...
Conference Paper
We present a new fast spatial averaging (FSA) technique that efficiently implements operations for spatial averaging or mean filtering. To perform spatial averaging of an M x N image with an averaging filter of size m x n, the basic method requires (mn 1) MN additions and MN divisions, while recent work using the column-addition and store-and-fetch...
Article
Hyperspectral reflectance imaging data are analyzed for poultry skin tumor detection. We consider selecting only a few wavebands from hyperspectral data for potential use in a real-time multispectral camera. To do this, we improve our prior tumor detection system by employing our new adaptive branch and bound algorithm and a support vector machine...
Conference Paper
This paper presents a novel generalized steepest ascent algorithm for selecting a subset of features. Our proposed algorithm is an improvement upon the prior steepest ascent algorithm by selecting a better starting search point and performing a more thorough search than the steepest ascent algorithm. For any given criterion function used to evaluat...
Conference Paper
The adaptive branch and bound algorithm was recently introduced to accelerate the search speed for optimal feature selection. The algorithm improves upon prior branch and bound algorithms in many aspects. One of the major improvements is to model the criterion function as a simple mathematical function and to adapt it in the proposed jump search st...
Article
A new improved forward floating selection (IFFS) algorithm for selecting a subset of features is presented. Our proposed algorithm improves the state-of-the-art sequential forward floating selection algorithm. The improvement is to add an additional search step called “replacing the weak feature” to check whether removing any feature in the current...
Article
We address feature selection algorithms for choosing a small set of spectral bands (wavelengths) in hyperspectral (HS) data for on-line contaminant detection. For cases when an optimal solution is not realistic, we introduce our new improved forward floating selection (IFFS) algorithm; we call it a quasi-optimal (close to optimal) algorithm. Our al...
Conference Paper
The branch and bound algorithm is an optimal feature selection method that is well-known for its computational efficiency. The recently developed adaptive branch and bound algorithm has been shown to be several times faster than other versions of the branch and bound algorithm. If the optimality of the algorithm is allowed to be compromised, we can...
Conference Paper
We present results on two new databases for a new improved forward floating selection (IFFS) algorithm for selecting a subset of features. The algorithm is an improvement upon the state-of-the-art sequential forward floating selection algorithm that includes a new search strategy to check whether removing any feature in the selected feature set and...
Article
We propose a new adaptive branch and bound algorithm for selecting the optimal subset of features in pattern recognition applications. The algorithm improves the search speed by avoiding unnecessary criterion function calculations at nodes in the solution tree. Our algorithm includes the following new properties: (i) ordering the tree nodes by the...
Conference Paper
Contaminant detection on chicken carcasses is an important product inspection application. The four contaminant types of interest contain three types of feces from different gastrointestinal regions (duodenum, ceca, and colon) and ingesta (undigested food) from the gizzard. Use of automated or semi-automated inspection systems for detecting fecal c...
Conference Paper
We consider new methods to select useful sets of ratio features in hyperspectral data to detect contaminant regions on chicken carcasses using data provided by ARS (Athens, GA). A ratio feature is the ratio of the response at each pixel for two different wavebands. Ratio features perform a type of normalization and can thus help reduce false alarms...
Conference Paper
Reduction of the potential health risks to consumers caused by food-borne infections is a very important food safety issue of public concern; one of the leading causes of food-borne illnesses is fecal contamination. We consider detecting fecal contaminants on chicken carcasses using hyperspectral imagery. We introduce our new improved floating forw...
Article
We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral (HS) reflectance data. Detection of chicken tumors is difficult because the tumors vary in size and shape; some tumors are small, early-stage tumor spots. We make use of the fact that a chicken skin tumor consists of a lesion region surrounded by a...
Conference Paper
Detection of skin tumors on chicken carcasses is considered. A chicken skin tumor consists of an ulcerous lesion region surrounded by a region of thickened-skin. We use a new adaptive branch-and-bound (ABB) feature selection algorithm to choose only a few useful wavebands from hyperspectral data for use in a real-time multispectral camera. The ABB...
Conference Paper
We propose a new adaptive branch and bound (ABB) algorithm for selecting the optimal subset of features in hyperspectral applications. The algorithm improves the search speed by avoiding unnecessary criterion function calculations at nodes in the solution tree. Our algorithm includes the following new properties: (i) ordering the tree nodes by the...
Conference Paper
Detection is one of the most formidable problems in automatic target recognition, since it involves locating multiple classes of targets of interest with distortions present in cluttered scenes. Fast and efficient algorithms are needed for detection, since in detection we need to analyze every local region of large image scenes. Minimum noise and c...
Conference Paper
We consider using minimum noise and correlation energy (Minace) filters to detect objects in high-resolution Electro-Optical (EO) visible imagery. EO data is a difficult detection problem because only primitive features such as edges and corners are useful. This occurs because the targets and the background in EO data can have very similar gray lev...
Conference Paper
We describe a fast method for dimensionality reduction and feature selection of ratio features for classification in hyperspectral data. The case study chosen is to discriminate internally damaged almond nuts from normal ones. For this case study, we find that using the ratios of the responses in several wavebands provides better features than a su...
Conference Paper
We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral reflectance data. This allows for faster data collection than does fluorescence data. A chicken skin tumor is an ulcerous lesion region surrounded by a region of thickened-skin. Detection of chicken tumors is a difficult detection problem because t...
Conference Paper
We consider new distortion-invariant filters (DIFs) to detect objects in high-resolution Electro-Optical (EO) visible imagery. EO data is a difficult detection problem, because only primitive features such as edges and corners are useful. No hot spots (present in IR data) or bright reflectors (present in SAR data) exist in EO data. We thus expect m...

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