• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.111-117
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• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.64-64
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• 2011

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ~80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million lightcurves, and found 1620 QSO candidates. During the selection, none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution (SAGE) LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.455-465
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• 2008

Recently the discussion on broadcasting and telecommunications convergence has been actively advanced, the convergence service which pulls down the guard of broadcasting and telecommunications industries is already embodied from the market. We need to clearly define a broadcasting and telecommunications convergence in order to develop promising convergence technologies based on future demands for broadcasting and telecommunications convergence. In this study, we consider both broadcasting and telecommunications as 'services-centered industries' and suggest new R&D project selection criteria. This could be used to present a new direction in R&D project management.

• Journal of Nuclear Fuel Cycle and Waste Technology
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• v.1 no.1
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• pp.9-27
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• 2013

Nagra was established in 1972 by the Swiss nuclear power plant operators and the Federal Government to implement permanent and safe disposal of all types of radioactive waste generated in Switzerland. The Swiss Nuclear Energy Act specifies that these shall be disposed of in deep geological repositories. A number of different geological formations and sites have been investigated to date and an extended database of geological characteristics as well as data and state-of-the-art methodologies required for the evaluation of the long-term safety of repository systems have been developed. The research, development, and demonstration activities are further supported by the two underground research facilities operating in Switzerland, the Grimsel Test Site and the Mont Terri Project, along with very active collaboration of Nagra with national and international partners. A new site selection process was approved by the Federal Government in 2008 and is ongoing. This process is driven by the long-term safety and feasibility of the geological repositories and is based on a step-wise decision-making approach with a strong participatory component from the affected communities and regions. In this paper a brief history and the current status of the Swiss radioactive waste management program are presented and special characteristics that may be useful beyond the Swiss program are highlighted and discussed.

• Korea Journal of Hospital Management
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• v.21 no.4
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• pp.45-54
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• 2016

The purpose of this study is to identify patient and hospital characteristics with pulmonary tuberculosis and to analyze factors which were influencing length of stay and treatment. The Korean National Hospital Discharge In-depth Injury Survey database from 2006 to 2012 was used for analysis. Study subjects were 4,704 patients and analyzed by using frequency, chi-square and logistic regression through using STATA 12.0. To avoid selection bias, we used propensity score matching. Analysis results show that the length of stay and treatment of pulmonary tuberculosis was different between insurance types. Patients characteristic(female, comorbidity, admission by outpatient department, medical insurance type) and hospital characteristic(500-999 beds, over 1000 beds) significantly influence length of stay. Admission by outpatient department and over 1000 beds are significantly influence treatment. Based on these findings, it is necessary to clarify between length of stay and treatment outcome by medical aids beneficiaries and audit hospitals follow discharge guidelines in pulmonary tuberculosis patients.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.231-238
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• 2008

With the development of methodologies, there are also the researches for the concrete item selection for selecting the future emerging researches and technologies. In this paper, we use scientometrics for that purpose in the sectors of construction and transportation. In our scientometric analysis, we use Scopus database, top 1% cited papers, bibliographic coupling, cosine coefficient, and hierarchical clustering and then carry additional experts verification on our results. We try to show the detailed process of scientometric analysis and its possibility as objective methodologies to select the future emerging researches and technologies.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• Advances in concrete construction
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• v.14 no.1
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• pp.15-34
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• 2022

The use of waste rubber in concrete can reduce natural aggregate consumption and improve some technical properties of concrete. Although there are several equations for estimating the mechanical properties of concrete containing waste rubber, limited numbers of machine learning-based models have been proposed to predict the mechanical properties of rubbercrete. In this study, an extensive database of the mechanical properties of rubbercrete was gathered from a comprehensive survey of the literature. To model the mechanical properties of rubbercrete, M5P tree and linear gene expression programming (LGEP) methods as two machine learning techniques were employed to achieve reliable mathematical equations. Two procedures of input variable selection were considered in this study. The crucial component ratios of rubbercrete and concrete age were assumed as the input variables in the first procedure. In contrast, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber were considered the second procedure of the input variables. The results show that the models obtained by LGEP are more accurate than those achieved by the M5P model tree and existing traditional equations. Besides, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber are better predictors of the mechanical properties of rubbercrete compared to the first procedure of input variable selection.