• Title/Summary/Keyword: 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 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.

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.

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.

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

  • Kim, Chang-Sik;Kim, Namgyu;Kwahk, Kee-Young
    • Journal of Korea Society of Digital Industry and Information Management
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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.

The Present and Perspective of Quantum Machine Learning (양자 기계학습 기술의 현황 및 전망)

  • Chung, Wonzoo;Lee, Seong-Whan
    • Journal of KIISE
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    • v.43 no.7
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    • pp.751-762
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    • 2016
  • This paper presents an overview of the emerging field of quantum machine learning which promises an innovative expedited performance of current classical machine learning algorithms by applying quantum theory. The approaches and technical details of recently developed quantum machine learning algorithms that have been able to substantially accelerate existing classical machine learning algorithms are presented. In addition, the quantum annealing algorithm behind the first commercial quantum computer is also discussed.

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

  • Woo, Y.C.;Lee, S.Y.;Choi, W.;Ahn, C.W.;Baek, O.K.
    • Electronics and Telecommunications Trends
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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.

Machine learning approaches for wind speed forecasting using long-term monitoring data: a comparative study

  • Ye, X.W.;Ding, Y.;Wan, H.P.
    • Smart Structures and Systems
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    • v.24 no.6
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    • pp.733-744
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    • 2019
  • Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

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.

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

  • Lee, Young-Mi;Ko, Chul-Min;Shin, Seong-Cheol;Kim, Byung-Sik
    • Journal of Environmental Science International
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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.