• Investigative Magnetic Resonance Imaging
• /
• 제23권2호
• /
• pp.81-99
• /
• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• International Journal of Computer Science & Network Security
• /
• 제21권1호
• /
• pp.220-225
• /
• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• 대한지구과학교육학회지
• /
• 제3권1호
• /
• pp.55-64
• /
• 2010

This study was intended to survey and analyze the illustrations of Chinese elementary science textbooks. The analysis criterion was composed of two categories, the kind of illustration and the role of illustration. The kind of illustration was divided into six subcategories categories such as photograph, picture, illustration, cartoon, diagram and recording sheet. The role of illustration was divided into four subcategories such as motive induction, guidance for learning, the supply of learning material, the presentation of learning result. The findings of this study were as follows. Chinese elementary science textbooks have about 3.55 illustrations per page. Compared with Korean ones, Chinese ones have more illustrations. From the analysis of the kinds of the illustrations on grade basis, it was found that the order of percentage of illustrations of Chinese elementary science textbooks is photograph, cartoon et al.. Photograph is prominent in entire grade. And From the analysis of the kinds of the illustrations on domain basis, the same results was founded. From the analysis of the roles of the illustrations on grade basis showed that both supply of learning material and guidance for learning are dominant in entire grade. The role of supply of learning material is a little more major than that of guidance for learning. From the analysis of the roles of the illustrations on domain basis, it was found that in domain of physics and chemistry the role of guidance for learning is major, and in domain of biology and earth science the role of supply of learning material is major.

• International Journal of Computer Science & Network Security
• /
• 제24권1호
• /
• pp.187-195
• /
• 2024

This is an extended paper explaining the role of E-learning and quality development in the current situation. Amid Covid:19, E-Learning has achieved a new miles stone in imparting education and all levels of institutions have transformed their learning platform from face to face to virtual learning. In this scenario E-Learning is facing two major challenges, first to ensure the ability of computer systems or software to exchange and make use of information on virtual platform (interoperability) and secondly, developing quality learning through e-Learning. To impart learning and teaching (L&T) through E-learning, Middle East University (MEU) has adopted Learning Management Services (LMS) through Blackboard. The university has three types of L&T methods; full online, Blended and Supportive. This research studies the concept, scope and dimensions of interoperability (InT) of E-Learning in MEU then the connection and interdependence between with quality development. In this paper we have described the support and the importance of finest standards and specifications for the objectives of InT of E-Learning and quality development in MEU. The research is based principally on secondary data observed from MEU E-Learning deanship. Also sample of 20 E-Learning experts at MEU were given closed ended as well as semi closed questionnaires for evaluating the assurance of InT of E-Learning and quality development. These experts are mainly certified online facilitators and admin staff. Results provide the verification of application and presence of InT of E-Learning and assured the quality development process in MEU.

• 한국과학교육학회지
• /
• 제35권3호
• /
• pp.363-374
• /
• 2015

본 연구의 목적은 고등학교 과학 교과에서 추출한 학습 내용과 사범대학 예비과학교사 교재에서 추출한 교육내용을 비교하여 그 연관성을 분석하고자 하였다. 이를 위해 소위 융합형 과학으로 불리는 고등학교 과학 교과서 7종과 물리, 화학, 생물, 지구과학 등 4개 과학 분야에서 사용하고 있는 11종 사범대학 예비과학교사 교재를 분석하였다. 고등학교 과학의 교육 내용과 예비과학교사 교재의 교육내용을 비교 분석한 결과, 관련성이 있는 것을 알 수 있었다. 특히 '에너지와 환경' 단원은 가장 많은 예비과학교사 교육내용이 포함된 것으로 나타났다. 그러나 전체 고등학교 과학 교과 내용의 86.6% 정도가 사범대학 4개 분야의 교재에 소개되고 있을 뿐 나머지는 예비교사들이 스스로 공부해야 하는 부담을 갖는 것으로 나타났다. 심지어 일부 학습내용들은 그 내용 수준이 사범대학 예비과학교사 교육내용보다 높은 것을 확인 할 수 있었다. 게다가 또 다른 일부 학습내용들은 공학과 기술 영역에 포함되어, 이를 대비하기 위한 별도의 교사교육이 필요하다. 위와 같은 내용을 종합했을 때, 고등학교 과학 교과의 교육내용을 적정화 할 필요가 있다. 또한 본 교과를 가르칠 교사와 예비교사들을 위해 다양한 교육 프로그램을 개발할 필요가 있으며, 예비과학교사들이 과학을 지도할 수 있도록 도울 수 있는 사범대학 교육과정이 개편이 필요하다.

• 디지털융복합연구
• /
• 제13권9호
• /
• pp.11-19
• /
• 2015

본 연구는 간호대학생을 대상으로 학습효과를 향상시키기 위한 학습전략으로써 블렌디드 러닝을 적용하여 자기주도학습력, 학습동기, 학습만족도와의 상관관계를 확인하고 학습만족도에 어떠한 영향을 미치는지 규명하기 위해 시도된 조사연구이다. 연구대상자는 D대학교 성인간호학 수업에 참여하는 학생 140명으로 7주 동안 면대면 수업과 6주 이러닝 수업을 진행한 후 2014년 6월 9일부터 6월 14일까지 자료를 수집하였다. 연구결과, 일반적 특성에 따른 변수들의 차이는 없었으며, 학생들의 학습만족도는 자기주도학습력과 유의한 상관관계가 있었다(r=.25, p=.003). 자기주도학습력이 학습만족도에 영향을 미치는 관련 요인으로 설명력은 22.1%로 나타났다(F=20.74, p<.001). 본 연구를 통해 간호대학생의 학습만족도를 향상시키기 위해서는 자기주도학습력을 고려한 블렌디드 러닝 콘텐츠 개발이 요구되고 다양한 블렌디드 러닝 운영방법에 관한 연구가 필요하다고 본다.

• 한국초등과학교육학회지:초등과학교육
• /
• 제28권4호
• /
• pp.519-528
• /
• 2009

The purpose of this study was to investigate the effect of social interaction on metacognition, learning motive and academic achievement in elementary science learning. The science lessons emphasizing social interactions that is applied to this study was comprised of 5 stages, 'introduction', 'inquiry activity', 'small group emergent activity', 'large group emergent activity', 'conclusion and assessment'. The results of this study were as follows: First, applying the learning model emphasizing social interaction to the experimental group led to a significant difference between the result of the pre- and post-test, regarding metacognition, especifically those of declarative knowledge. And meaningful difference was drawn from the results of all elements in the lower category of regulation of cognition between the experimental and comparison group. Second, a significant difference was found between the pre- and post-test regarding learning motive, especially those of attention, relation, and self-confidence. Third, after applying the learning model emphasizing social interaction to the science classes of the experimental group, students' academic achievement improved significantly in the post-test, compared to the results of pre-test.

• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.193-203
• /
• 2022

After the everyday use of systems and applications of artificial intelligence in our world. Consequently, machine learning technologies have become characterized by exceptional capabilities and unique and distinguished performance in many areas. However, these applications and systems are vulnerable to adversaries who can be a reason to confer the wrong classification by introducing distorted samples. Precisely, it has been perceived that adversarial examples designed throughout the training and test phases can include industrious Ruin the performance of the machine learning. This paper provides a comprehensive review of the recent research on adversarial machine learning. It's also worth noting that the paper only examines recent techniques that were released between 2018 and 2021. The diverse systems models have been investigated and discussed regarding the type of attacks, and some possible security suggestions for these attacks to highlight the risks of adversarial machine learning.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.39-48
• /
• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.202-209
• /
• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.