• Title/Summary/Keyword: Deep-Learning

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A Study on Development Deep Learning Based Learning System for Enhancing the Data Analytical Thinking (데이터 분석적 사고력 향상을 위한 딥러닝 기반 학습 시스템 개발 연구)

  • Lee, Young-ho;Koo, Duk-hoi
    • Journal of The Korean Association of Information Education
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    • v.21 no.4
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    • pp.393-401
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    • 2017
  • The purpose of this study is to develop a deep learning based learning system for improving learner's data analytical thinking ability. The contents of the study are as follows. First, deep learning was applied to the discovery learning model to improve data analytical thinking ability. This is a learning method that can generate a model showing the relationship of given data by using the deep learning method, then apply the model to new data to obtain the result. Second, we developed a deep learning based system for DBD learning model. Specifically, we developed a system to generate a model of data using the deep learning method and to apply this model. The research of deep learning based learning system will be a new approach to improve learner's data analytical thinking ability in future society where data becomes more important.

Sentiment Orientation Using Deep Learning Sequential and Bidirectional Models

  • Alyamani, Hasan J.
    • International Journal of Computer Science & Network Security
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    • v.21 no.11
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    • pp.23-30
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    • 2021
  • Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

A Survey of Deep Learning in Agriculture: Techniques and Their Applications

  • Ren, Chengjuan;Kim, Dae-Kyoo;Jeong, Dongwon
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1015-1033
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    • 2020
  • With promising results and enormous capability, deep learning technology has attracted more and more attention to both theoretical research and applications for a variety of image processing and computer vision tasks. In this paper, we investigate 32 research contributions that apply deep learning techniques to the agriculture domain. Different types of deep neural network architectures in agriculture are surveyed and the current state-of-the-art methods are summarized. This paper ends with a discussion of the advantages and disadvantages of deep learning and future research topics. The survey shows that deep learning-based research has superior performance in terms of accuracy, which is beyond the standard machine learning techniques nowadays.

A Study on Deep Learning Structure of Multi-Block Method for Improving Face Recognition (얼굴 인식률 향상을 위한 멀티 블록 방식의 딥러닝 구조에 관한 연구)

  • Ra, Seung-Tak;Kim, Hong-Jik;Lee, Seung-Ho
    • Journal of IKEEE
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    • v.22 no.4
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    • pp.933-940
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    • 2018
  • In this paper, we propose a multi-block deep learning structure for improving face recognition rate. The recognition structure of the proposed deep learning consists of three steps: multi-blocking of the input image, multi-block selection by facial feature numerical analysis, and perform deep learning of the selected multi-block. First, the input image is divided into 4 blocks by multi-block. Secondly, in the multi-block selection by feature analysis, the feature values of the quadruple multi-blocks are checked, and only the blocks with many features are selected. The third step is to perform deep learning with the selected multi-block, and the result is obtained as an efficient block with high feature value by performing recognition on the deep learning model in which the selected multi-block part is learned. To evaluate the performance of the proposed deep learning structure, we used CAS-PEAL face database. Experimental results show that the proposed multi-block deep learning structure shows 2.3% higher face recognition rate than the existing deep learning structure.

Privacy-Preserving Deep Learning using Collaborative Learning of Neural Network Model

  • Hye-Kyeong Ko
    • International journal of advanced smart convergence
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    • v.12 no.2
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    • pp.56-66
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    • 2023
  • The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

Machine Learning and Deep Learning Models to Predict Income and Employment with Busan's Strategic Industry and Export (머신러닝과 딥러닝 기법을 이용한 부산 전략산업과 수출에 의한 고용과 소득 예측)

  • Chae-Deug Yi
    • Korea Trade Review
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    • v.46 no.1
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    • pp.169-187
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    • 2021
  • This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

Deep Learning in MR Image Processing

  • Lee, Doohee;Lee, Jingu;Ko, Jingyu;Yoon, Jaeyeon;Ryu, Kanghyun;Nam, Yoonho
    • Investigative Magnetic Resonance Imaging
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    • v.23 no.2
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    • pp.81-99
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    • 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.

The Study for Type of Mask Wearing Dataset for Deep learning and Detection Model (딥러닝을 위한 마스크 착용 유형별 데이터셋 구축 및 검출 모델에 관한 연구)

  • Hwang, Ho Seong;Kim, Dong heon;Kim, Ho Chul
    • Journal of Biomedical Engineering Research
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    • v.43 no.3
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    • pp.131-135
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    • 2022
  • Due to COVID-19, Correct method of wearing mask is important to prevent COVID-19 and the other respiratory tract infections. And the deep learning technology in the image processing has been developed. The purpose of this study is to create the type of mask wearing dataset for deep learning models and select the deep learning model to detect the wearing mask correctly. The Image dataset is the 2,296 images acquired using a web crawler. Deep learning classification models provided by tensorflow are used to validate the dataset. And Object detection deep learning model YOLOs are used to select the detection deep learning model to detect the wearing mask correctly. In this process, this paper proposes to validate the type of mask wearing datasets and YOLOv5 is the effective model to detect the type of mask wearing. The experimental results show that reliable dataset is acquired and the YOLOv5 model effectively recognize type of mask wearing.

DeepBlock: Web-based Deep Learning Education Platform (딥블록: 웹 기반 딥러닝 교육용 플랫폼)

  • Cho, Jinsung;Kim, Geunmo;Go, Hyunmin;Kim, Sungmin;Kim, Jisub;Kim, Bongjae
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.3
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    • pp.43-50
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    • 2021
  • Recently, researches and projects of companies based on artificial intelligence have been actively carried out. Various services and systems are being grafted with artificial intelligence technology. They become more intelligent. Accordingly, interest in deep learning, one of the techniques of artificial intelligence, and people who want to learn it have increased. In order to learn deep learning, deep learning theory with a lot of knowledge such as computer programming and mathematics is required. That is a high barrier to entry to beginners. Therefore, in this study, we designed and implemented a web-based deep learning platform called DeepBlock, which enables beginners to implement basic models of deep learning such as DNN and CNN without considering programming and mathematics. The proposed DeepBlock can be used for the education of students or beginners interested in deep learning.

Damage Detection in Truss Structures Using Deep Learning Techniques (딥러닝 기술을 이용한 트러스 구조물의 손상 탐지)

  • Lee, Seunghye;Lee, Kihak;Lee, Jaehong
    • Journal of Korean Association for Spatial Structures
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    • v.19 no.1
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    • pp.93-100
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    • 2019
  • There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.