• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.187-194
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• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• 환경위생공학
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• 제15권2호
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• pp.85-94
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• 2000

The comparative evaluation is the most effective method for site selection because the selection of waste treatment facility is to determine the optimum site out of limited candidate sites. This study adopted the ordinal scale evaluation, one of methods of comparative evaluation. The ordinal scale evaluation aims to determine the investigating items referring to the character of sites, to determine the importance factors for investigating items, and to determine the optimum site according to the quantitative evaluation. This study has focussed on reflecting the opinion of residents to the maximum extent with a weight on social and economic aspects, considering the state of confrontation against each other between the autonomous government organization and local residents, which is being emerged as a social issue relating to the evaluation rating on each item of site selection of an optimum site. Therefore, rational, and clear validity investigations and proper reduction of both time and expenses in site selection as well through this a study.

• International Journal of Internet, Broadcasting and Communication
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• 제9권1호
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• pp.72-80
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• 2017

In this work, an efficient transmit antenna selection approach for the quadrature spatial modulation (QSM) systems is proposed. The conventional Euclidean distance antenna selection (EDAS)-based schemes in QSM have too high computational complexity for practical use. The proposed antenna selection algorithm is based on approximation of the EDAS decision metric employed for QSM. The elimination of imaginary parts in the decision metric enables decoupling of the approximated decision metric, which enormously reduces the complexity. The proposed method is also evaluated via simulations in terms of symbol error rate (SER) performance and compared with the conventional EDAS methods in QSM systems.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 추계 학술논문 발표대회
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• pp.218-219
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• 2014

The selection of an appropriate contract method is vital for the successful operation of the project. However, there has been a lack of studies on objective decision making support models for use in the planning stage of a project contract. The present study had the goal of analyzing the factors that influence contract method selection, as an initial study for developing a project contract method selection model. The existing related studies were analyzed, and the factors considered in the literature were selected. Then, based on the findings, the opinions of an expert group on the important factors for contract method selection were collected through a survey. The collected opinions were analyzed using factor analysis, a statistical analysis method. The results will be utilized in the future as preliminary data for developing a decision making model for selecting a contract method.

• 한국도서관정보학회지
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• 제43권3호
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• pp.457-479
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• 2012

본 연구에서는 현재 국내 공공도서관 자료선정업무의 현황을 자료구입비 규모, 자료선정정책과 자료선정기준, 자료선정 담당 인력과 자료선정위원회, 자료선정방법과 자료선정도구 등의 카테고리로 나누어 살펴 본 다음 사서들의 자료선정업무에 대한 인식을 조사하였다. 이러한 현황을 바탕으로 효율적인 자료선정이 이루어질 수 있는 방안을 자료선정정책, 자료선정인력, 자료선정방법의 세 가지 측면으로 나누어 논의하였다. 자료선정업무현황 분석을 위한 데이터 수집은 국내 공립공공도서관 315개관을 대상으로 한 설문조사를 통하여 이루어졌다.

• 대한안전경영과학회지
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• 제9권2호
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• pp.195-214
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• 2007

The purpose of this study is to explore the applicability of AHP(Analytic Hierarchy Process) to select more productive projects among various proposed projects in a particular company. To achieve this research objective, the characteristics of project evaluation and selection are first reviewed with respect to when, where, and how the decision is made. Then the theoretical basis of the AHP is briefly reviewed along with its mathematical underpinnings to construct the framework of project evaluation and selection. To be more specific, the evaluation and selection criteria were reorganized in the AHP-based framework to make the process of project evaluation and selection more productive. Project evaluation and selection is one of the most important activities for the most companies to be more advantageous in the market. Despite the importance of decision making process of project selection, not many of how to choose the best project were suggested as the reliable project selection methods in the industries. It may be because it involves various activities related to conflict resolution among different evaluation criteria, high uncertainties of market, and the unclear tradeoff among various project objectives. Furthermore, the decision, once made at this point, tends to be irrevocable until the whole process turns out to be a complete success or failure. As the result, the AHP method showed better financial performance rather than the traditional method in a case study.

• 스마트미디어저널
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• 제12권3호
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• 보건행정학회지
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• 제12권2호
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• pp.92-114
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• 2002

While making a vigorous discussion about self-regulation for exponential growth of harmful health information on the Internet, many countries lave made various efforts to select and circulate high quality health information on the Internet. The purpose of this study Is to review the serf-regulation methods of health information on the Internet and to suggest quality control methods of health information on the Internet suitable for Korea. Self-regulation methods of the health information on the Internet include ‘content rating system(or content selection system)’, ‘codes of conduct or guideline’, ‘internet hot-line’, ‘education for information providers and consumers’. Any self-regulation method should be used with other methods. We can regulate health information on the Internet effectively by using both self-regulation methods and compulsive methods such as law. Also information providers, information consumers, specialists, consumer representatives, scholars, governments officers should take part in doing these efforts and make concern.

• Korean Journal of Mathematics
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• 제22권2호
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• pp.289-305
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• 2014

In this paper we propose a new approach to the variable selection problem for a primary resolution and wavelet basis functions in wavelet regression. Most wavelet shrinkage methods focus on thresholding the wavelet coefficients, given a primary resolution which is usually determined by the sample size. However, both a primary resolution and the basis functions are affected by the shape of an unknown function rather than the sample size. Unlike existing methods, our method does not depend on the sample size and also takes into account the shape of the unknown function.