• Title, Summary, Keyword: machine learning

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A Study on the Work Type of Machine Learning Administrative Service in Metropolitan Government (광역자치단체의 기계학습 행정서비스 업무유형에 관한 연구 -서울시를 중심으로-)

  • Ha, Chung-Yeol;Jung, Jin-Teak
    • Journal of Digital Convergence
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    • v.18 no.12
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    • pp.29-36
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    • 2020
  • The background of this study is that machine learning administrative services are recently attracting attention as a major policy tool for non-face-to-face administrative services in the post-corona era. This study investigated the types of work expected to be effective when introducing machine learning administrative services for Seoul Metropolitan Government officials who are piloting machine learning administrative services. The research method is a machine that can be introduced by organizational unit by distributing and collecting questionnaires for Seoul administrative organizations that have performed machine learning-based administrative services for one month in July 2020 targeting Seoul public officials using machine learning-based administrative services. By analyzing the learning administration service and application service, the business characteristics of each machine learning administration service type such as supervised learning work type, unsupervised learning work type, and reinforced learning work type were analyzed. As a result of the research analysis, it was found that there were significant differences in the characteristics of administrative tasks by supervised and unsupervised learning areas. In particular, it was found that the reinforcement learning domain contains the most appropriate business characteristics for machine learning administrative services. Implications were drawn. The results of this study can be provided as a reference material to practitioners who want to introduce machine learning administration services, and can be used as basic data for research to researchers who want to study machine learning administration services in the future.

Study on Automatic Bug Triage using Deep Learning (딥 러닝을 이용한 버그 담당자 자동 배정 연구)

  • Lee, Sun-Ro;Kim, Hye-Min;Lee, Chan-Gun;Lee, Ki-Seong
    • Journal of KIISE
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    • v.44 no.11
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    • pp.1156-1164
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    • 2017
  • Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.

Modern Probabilistic Machine Learning and Control Methods for Portfolio Optimization

  • Park, Jooyoung;Lim, Jungdong;Lee, Wonbu;Ji, Seunghyun;Sung, Keehoon;Park, Kyungwook
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.14 no.2
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    • pp.73-83
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    • 2014
  • Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

An Effective Data Model for Forecasting and Analyzing Securities Data

  • Lee, Seung Ho;Shin, Seung Jung
    • International journal of advanced smart convergence
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    • v.5 no.4
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    • pp.32-39
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    • 2016
  • Machine learning is a field of artificial intelligence (AI), and a technology that collects, forecasts, and analyzes securities data is developed upon machine learning. The difference between using machine learning and not using machine learning is that machine learning-seems similar to big data-studies and collects data by itself which big data cannot do. Machine learning can be utilized, for example, to recognize a certain pattern of an object and find a criminal or a vehicle used in a crime. To achieve similar intelligent tasks, data must be more effectively collected than before. In this paper, we propose a method of effectively collecting data.

Short-term Wind Power Prediction Based on Empirical Mode Decomposition and Improved Extreme Learning Machine

  • Tian, Zhongda;Ren, Yi;Wang, Gang
    • Journal of Electrical Engineering and Technology
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    • v.13 no.5
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    • pp.1841-1851
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    • 2018
  • For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

Machine Learning Based Neighbor Path Selection Model in a Communication Network

  • Lee, Yong-Jin
    • International journal of advanced smart convergence
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    • v.10 no.1
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    • pp.56-61
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    • 2021
  • Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

Identifying the Effects of Repeated Tasks in an Apartment Construction Project Using Machine Learning Algorithm (기계적 학습의 알고리즘을 이용하여 아파트 공사에서 반복 공정의 효과 비교에 관한 연구)

  • Kim, Hyunjoo
    • Journal of KIBIM
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    • v.6 no.4
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    • pp.35-41
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    • 2016
  • Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

ACCELERATION OF MACHINE LEARNING ALGORITHMS BY TCHEBYCHEV ITERATION TECHNIQUE

  • LEVIN, MIKHAIL P.
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.22 no.1
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    • pp.15-28
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    • 2018
  • Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

Trend Analysis of Korea Papers in the Fields of 'Artificial Intelligence', 'Machine Learning' and 'Deep Learning' ('인공지능', '기계학습', '딥 러닝' 분야의 국내 논문 동향 분석)

  • Park, Hong-Jin
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.13 no.4
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    • pp.283-292
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    • 2020
  • Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

Generating Training Dataset of Machine Learning Model for Context-Awareness in a Health Status Notification Service (사용자 건강 상태알림 서비스의 상황인지를 위한 기계학습 모델의 학습 데이터 생성 방법)

  • Mun, Jong Hyeok;Choi, Jong Sun;Choi, Jae Young
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.1
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    • pp.25-32
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    • 2020
  • In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.