• 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.

• 한국압력기기공학회 논문집
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• 제19권2호
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• 한국간호교육학회지
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• 제14권2호
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• pp.252-261
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• 2008

Purpose: The purpose of this study was to explore how an educator can empower students by fostering communication, problem-solving, and self-directed learning ability. Method: In order to accomplish this purpose, 136 students who were attending J University and 105 students attending M University participated in the questionnaire. The students were freshman in the nursing or social welfare departments, There were 136 in the control group and 105 in the experimental group. The control group was given an applicable class of project learning. On the other hand, the experimental group was given traditional lessons once a week for 15 weeks. The research instrument used the measuring instruments developed by KEDI for communication and problem-solving and self-directed learning ability. Data was analysed by ANCOVA with SPSS/PC. Result: The results of analysis show that communication, problem-solving, and self-directed learning ability significantly increased in the experimental group. Conclusion: Based on the research finding, project learning has an educational value. Interdisciplinary cooperation project learning is effective for communication, problem-solving, and self-directed learning ability.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.9-12
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• 2001

Learning vector quantization (LVQ) is a supervised learning technique that uses class information to move the vector quantizer slightly, so as to improve the quality of the classifier decision regions. In this paper we propose a selection method of initial codebook vectors for a teaming vector quantization (LVQ3) using support vector machines (SVM). The method is experimented with artificial and real design data sets and compared with conventional methods of the condensed nearest neighbor (CNN) and its modifications (mCNN). From the experiments, it is discovered that the proposed method produces higher performance than the conventional ones and then it could be used efficiently for designing nonparametric classifiers.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2012년도 춘계학술발표대회논문집
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• pp.123-126
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• 2012

In general, there are 3 subjects of discussion in education field; namely past, present and future. Related with those facts, there is several information or knowledge that relatively hard to be presented in the real world. In this matter, digital content shows its contribution especially in the education field. Digital content can virtually represent the information or knowledge that seems to be difficult to be visualized in the real world before. In this project, researcher develops a mobile device's application that consist the skeleton's 3D virtual content. This application is expected to solve the above explained problem that usually appears during learning human skeleton in senior high-school's biology class. Besides, the application of digital content will make the learning process become easier because the student will have a visual learning tool. Last, the mobile device that is used in this prototype has offer an important beneficial namely mobility beneficial, so that the user can access the content anytime and anywhere.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.929-932
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• 1990

This paper considers the problem of patterns recognition using the artificial neural network systems. The artificial neural network systems provide an effective tool for classifying patterns and/or characters by learning them in a certain repeated hashion. The mechanism of the learning process and the structure of neural network systems used are main concerns in the accurate and fast classification of the patterns which are slightly different each other. The neural network system employed in this study has three layers structure which is composed of input, intermidiate, and output layers. Our main concern is to develope an effective learning mechanism how to learn the patterns fastly and accurately. The experimental study performed shows that there exists an effective learning method to get higher recognition ratio in classifying the several different patterns by artificial neural network system constructed.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.44-47
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• 1997

This paper is a study on the hand gesture recognition using Instance-based teaming, Symbolic learning algorithms and Power Glove which supplies information on finger position, hand position and orientation. The data were carefully examined, and a few features of the data that would serve as good discriminants between signs when used with the learning algorithms were extracted. The hand gesture data collected from 5 people were applied to the teaming algorithms. In spite of the noise and accuracy constraints of the equipment used, some accuracy rates were achieved.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.783-786
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• 2016

E-learning is a new teaching model nowadays, and Virtual Reality (VR) technology is reported that the use of virtual reality as an education tool can increase student interests, understanding, and creative learning because of encouraging students to learn by exploring and interacting with the information on the virtual environment. Besides that, LEGOs have long been the favorite of many children. LEGOs provide a mechanism to understand and do for many concepts from spatial relationships to robotics platforms. In this paper, we present a virtual reality application based on the assembly of LEGO-Like bricks to increase math and science learning by improving spatial thinking. It not only encourages students to pursue careers in science, technology, engineering, or mathematics but also enhances learner's ability to analyze and solve problems. The application is built by Processing 2.0 as the easier programming language which is a top-down approach to build the 3D interactive program.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.368-371
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• 2016

정보 통신 기술의 급속한 발달로 인터넷 환경의 학습은 학습 혁명을 주도하는 핵심적인 위치에 있다. 인터넷 환경은 시공간의 제약 없이 자신이 필요로 하는 다양한 교육과정에 접근이 가능하고 생업과 병행하여 학습을 할 수 있는 접근성을 갖는다. 그러나 인터넷에 대한 접근성의 차이는 디지털 디바이스 현상을 발생하게 하여 온라인 학습자와 오프라인 학습자의 교육격차를 더 크게 한다. 이러한 문제를 해결하기 위해 본 연구는 인터넷 환경이 원활하지 않은 경우에는 지속적인 학습을 진행할 수 있도록 PDF 시스템을 활용한다. 이 시스템을 통해 오프라인에서의 학습 결과를 온라인에 전송하여 이력을 제공하는 시스템을 설계하고자 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.768-770
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• 1996

This paper propose neural network which is one of self-organizing techniques. It is composed neural network controller as input signal is error and change of error which is optimal output, and is learned system by using a error back-propagation learning algorithm is one of error mimizing learning methods. In order to achieve practical real time control reduce on learning time, it is applied to load-frequency control of nonlinear power system with using a moment learning method. It is described in such a case considering constraints for a rate of increace generation-rate.