• 제목/요약/키워드: Machine Learning

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Analysis of Automatic Machine Learning Solution Trends of Startups

  • Lee, Yo-Seob
    • International Journal of Advanced Culture Technology
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    • v.8 no.2
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    • pp.297-304
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    • 2020
  • Recently, open source automatic machine learning solutions have been applied in many fields. To apply open source automated machine learning to real world problems, you need to write code with expertise in machine learning. Writing code without machine learning knowledge is challenging. To solve this problem, the automatic machine learning solutions provided by startups are made easy to use with a clean user interface. In this paper, we review automatic machine learning solutions of startups.

Analysis on Trends of Machine Learning-as-a-Service

  • Lee, Yo-Seob
    • International Journal of Advanced Culture Technology
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    • v.6 no.4
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    • pp.303-308
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    • 2018
  • Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.

Analysis of Machine Learning Education Tool for Kids

  • Lee, Yo-Seob;Moon, Phil-Joo
    • International Journal of Advanced Culture Technology
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    • v.8 no.4
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    • pp.235-241
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    • 2020
  • Artificial intelligence and machine learning are used in many parts of our daily lives, but the basic processes and concepts are barely exposed to most people. Understanding these basic concepts is becoming increasingly important as kids don't have the opportunity to explore AI processes and improve their understanding of basic machine learning concepts and their essential components. Machine learning educational tools can help children easily understand artificial intelligence and machine learning. In this paper, we examine machine learning education tools and compare their features.

Machine Learning Frameworks for Automated Software Testing Tools : A Study

  • Kim, Jungho;Ryu, Joung Woo;Shin, Hyun-Jeong;Song, Jin-Hee
    • International Journal of Contents
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    • v.13 no.1
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    • pp.38-44
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    • 2017
  • Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

Comparing the Performance of 17 Machine Learning Models in Predicting Human Population Growth of Countries

  • Otoom, Mohammad Mahmood
    • International Journal of Computer Science & Network Security
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    • v.21 no.1
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    • pp.220-225
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    • 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.

머신러닝 및 딥러닝 연구동향 분석: 토픽모델링을 중심으로 (Research Trends Analysis of Machine Learning and Deep Learning: Focused on the Topic Modeling)

  • 김창식;김남규;곽기영
    • 디지털산업정보학회논문지
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    • v.15 no.2
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    • pp.19-28
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    • 2019
  • The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

디지털 헬스케어 데이터 분석을 위한 머신 러닝 기술 활용 동향 (Trend of Utilization of Machine Learning Technology for Digital Healthcare Data Analysis)

  • 우영춘;이성엽;최완;안창원;백옥기
    • 전자통신동향분석
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    • v.34 no.1
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    • pp.98-110
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    • 2019
  • Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

Underwater Acoustic Research Trends with Machine Learning: General Background

  • Yang, Haesang;Lee, Keunhwa;Choo, Youngmin;Kim, Kookhyun
    • 한국해양공학회지
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    • v.34 no.2
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    • pp.147-154
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    • 2020
  • Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

Landslide susceptibility assessment using feature selection-based machine learning models

  • Liu, Lei-Lei;Yang, Can;Wang, Xiao-Mi
    • Geomechanics and Engineering
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    • v.25 no.1
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    • pp.1-16
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    • 2021
  • Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

수문학적 활용을 위한 머신러닝 기반의 강우보정기술 개발 (The Development of a Rainfall Correction Technique based on Machine Learning for Hydrological Applications)

  • 이영미;고철민;신성철;김병식
    • 한국환경과학회지
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    • v.28 no.1
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    • pp.125-135
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    • 2019
  • For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.