• Clinical Endoscopy
• /
• 제51권6호
• /
• pp.547-551
• /
• 2018

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.

• International Journal of Computer Science & Network Security
• /
• 제24권5호
• /
• pp.129-134
• /
• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Smart Structures and Systems
• /
• 제24권5호
• /
• pp.567-585
• /
• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• ETRI Journal
• /
• 제42권1호
• /
• pp.90-100
• /
• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• Asia pacific journal of information systems
• /
• 제6권2호
• /
• pp.77-105
• /
• 1996

The accuracy of knowledge is a major concern for expert system developers and users. Machine learning approaches have recently been found to be useful in knowledge acquisition for expert systems. However, the accuracy of concept acquired from machine learning could not be analyzed in most cases. In this paper we develop a comprehensive knowledge error analysis methodology for practical use of expert systems. Decision tree induction is an important type of machine learning method for business expert systems. Here we start to analyze with knowledge acquired from decision tree induction method, and extend the results to develop error analysis methodology for general machine learning methods. We give several examples and illustrations for these results. We also discuss the applicability of these results to multistrategy learning approaches.

• 동의생리병리학회지
• /
• 제30권6호
• /
• pp.412-418
• /
• 2016

The emergence of systems biology in the 21st century is changing the paradigm of biomedical research. Whereas the reductionist approaches focus on components rather than time or contexts, systems biology focus more on interrelationships, dynamics, and contexts. The key ideas of the systems biology shares much with the philosophy of Korean Medicine(KM) and therefore, the paradigm shift is shedding light on understanding the mechanism of action of KM at system level. In this article, I provide a guide to learning systems biology for KM researchers using online learning resources. Thanks to the recent development of MOOC(massive open online courses) and other online learning platforms, learners can access to plenty of high-quality resources from top-tier universities in the world. I expect this guide help researchers to employ systems biology methods into their KM researches, and will lead to the development of future curricula for training "bi-lingual" experts, KM and computational approaches.

• 한국과학교육학회지
• /
• 제11권1호
• /
• pp.15-24
• /
• 1991

The purpose of this study was to investigate the effectiveness of Learning Cycle approach to change the concept of density. The results of the study were as follows : 1) Students already had various types of preconception related to density before formal learning. These preconceptions mostly differ from scientific concepts. 2) Male students were much better than female ones in the development of scientific concepts before formal learning. These differences were found statistically significant(P<0.01). 3) The higher the cognitive level of the students, the better the development of scientific concepts. 4) In the change of preconceptions to scientific concepts by treatment, there was significant difference between control group and experimental group at the 0.05 level. It was found that Learning Cycle approaches were more effective than traditional approaches in acquiring the concept of density. 5) It was found that there was no significant difference On the retention level of the concept of density between control group and experimental group.

• International Journal of Internet, Broadcasting and Communication
• /
• 제10권2호
• /
• pp.19-24
• /
• 2018

This study investigated the academic stress and the immersion in learning in relation to AR and VR assisted instructions compared to traditional approaches. To that end, 78 $8^{th}$ graders in T and S city in Gangwondo were assigned to experimental and control groups. The experimental group received the VR and AR lessons. The academic stress was measured with the pre- and post-test scores, while the immersion in learning was measured with the post-test scores. In brief, AR and VR assisted lessons made statistically significant differences in the academic stress and immersion in learning in comparison to the traditional approaches.

• International Journal of Advanced Culture Technology
• /
• 제4권3호
• /
• pp.6-12
• /
• 2016

The concept of intergenerational and intercultural approaches in education and learning are changing nowadays. Intergenerational approach in the third age education and learning programs can be defined as planned activities that link various generations with the goal of exchanging knowledge, experiences and receiving mutual benefits. The goal is to connect people by using mutually beneficial activities that encourage understanding, cooperation and respect between generations, as well as contribute to the society. Intercultural approach in the third age education is connected with activities that link people of various cultures aimed at receiving mutual benefits. This paper discusses the development of third age education in Saint Petersburg, Russia and shows how the intercultural and intergenerational approaches are used in this type of education. The third age universities in Saint Petersburg do not have a lot of experience in this. In the article examples of the using intercultural and intergenerational approaches in the third age education are showed.

• 한국과학교육학회지
• /
• 제30권5호
• /
• pp.668-680
• /
• 2010

이 연구에서는 알갱이의 크기에 따른 혼합물의 분리 원리에 대해 초등학생들이 만든 비유의 특성, 대응관계 이해도, 대응 오류, 비유 만들기에 대한 인식을 학습접근양식에 따라 조사했다. 초등학교 4학년 92명을 선정하여 학습접근양식 검사, 비유 만들기 검사, 비유 만들기에 대한 인식 검사를 실시했다. 연구 결과, 기계적 학습접근양식을 지닌 학생들보다 유의미학습접근양식을 지닌 학생들이 더 많은 수의 비유를 만드는 것으로 나타났다. 만든 비유 유형의 경우, 기계적 학습접근양식보다 유의미 학습접근양식을 지닌 학생들이 구조적/기능적 비유, 부연 비유, 고체계성 비유를 더 많이 만드는 경향이 있었으나 표현 방식(글, 그림, 글/그림)과 상황의 작위성(작위적, 일상적), 추상도(추상적, 구체적) 항목에서는 학습접근양식에따른 차이가 거의 없었다. 기계적 학습접근양식보다 유의미 학습접근양식을 지닌 학생들이 비유에 대한 이해도가 더 높았고, 비유물에 포함된 공유 속성의 수와 대응 관계 이해도 점수도 유의미하게 높았으며, 대응 오류를 범하는 경우도 더 적었다. 학습접근양식에 관계없이 많은 학생들이 비유 만들기 활동에 대해 다양한 인지적 동기적 측면에서 긍정적으로 인식하는 것으로 나타났다. 반면, 비유 만들기 활동에서의 다양한 어려움 등과 같은 단점을 지적하기도 했다. 이에 대한 교육적 함의를 논했다.