• 제목/요약/키워드: machine learning

검색결과 5,100건 처리시간 0.036초

Analysis on Trends of No-Code Machine Learning Tools

  • Yo-Seob, Lee;Phil-Joo, Moon
    • International Journal of Advanced Culture Technology
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    • 제10권4호
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    • pp.412-419
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    • 2022
  • The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

Analysis of Automatic Machine Learning Solution Trends of Startups

  • Lee, Yo-Seob
    • International Journal of Advanced Culture Technology
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    • 제8권2호
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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.

PSO 알고리즘을 이용한 퍼지 Extreme Learning Machine 최적화 (Optimization of Fuzzy Learning Machine by Using Particle Swarm Optimization)

  • 노석범;왕계홍;김용수;안태천
    • 한국지능시스템학회논문지
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    • 제26권1호
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    • pp.87-92
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    • 2016
  • 본 논문에서는 일반적인 신경회로망의 단점인 느린 학습속도를 획기적으로 개선한 네트워크인 Extreme Learning Machine과 전문가들의 언어적 정보들을 기술 할 수 있는 퍼지 이론을 접목한 퍼지 Extreme Learning Machine을 최적화하기 위하여 Particle Swarm Optimization 알고리즘을 이용하였다. 퍼지 Extreme Learning Machine의 활성화 함수를 일반적인 시그모이드 함수를 사용하지 않고, 퍼지 C-Means 클러스터링 알고리즘의 활성화 레벨 함수를 이용하였다. Particle Swarm Optimization 알고리즘과 같은 최적화 알고리즘을 통하여 퍼지 Extreme Learning Machine의 활성화 함수의 파라미터들을 최적화 한다. Particle Swarm Optimization과 같은 최적화 알고리즘을 통한 제안된 모델의 최적화 하고 최적화된 모델의 분류성능을 평가하기 위하여 다양한 머신 러닝 데이터 집합을 사용하여 평가한다.

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

  • Lee, Yo-Seob
    • International Journal of Advanced Culture Technology
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    • 제6권4호
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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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    • 제8권4호
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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.

Comparison of Machine Learning Tools for Mobile Application

  • Lee, Yo-Seob
    • International Journal of Advanced Culture Technology
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    • 제10권3호
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    • pp.360-370
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    • 2022
  • Demand for machine learning systems continues to grow, and cloud machine learning platforms are widely used to meet this demand. Recently, the performance improvement of the application processor of smartphones has become an opportunity for the machine learning platform to move from the cloud to On-Device AI, and mobile applications equipped with machine learning functions are required. In this paper, machine learning tools for mobile applications are investigated and compared the characteristics of these tools.

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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    • 제15권2호
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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.

Modeling of AutoML using Colored Petri Net

  • Yo-Seob, Lee
    • International Journal of Advanced Culture Technology
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    • 제10권4호
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    • pp.420-426
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    • 2022
  • Developing a machine learning model and putting it into production goes through a number of steps. Automated Machine Learning(AutoML) appeared to increase productivity and efficiency by automating inefficient tasks that occur while repeating this process whenever machine learning is applied. The high degree of automation of AutoML models allows non-experts to use machine learning models and techniques without the need to become machine learning experts. Automating the process of applying machine learning end-to-end with AutoML models has the added benefit of creating simpler solutions, generating these solutions faster, and often generating models that outperform hand-designed models. In this paper, the AutoML data is collected and AutoML's Color Petri net model is created and analyzed based on it.

Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement Learning, and Q-Learning

  • Park, Ji Su;Park, Jong Hyuk
    • Journal of Information Processing Systems
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    • 제16권5호
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    • pp.1001-1007
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    • 2020
  • In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

Review on Applications of Machine Learning in Coastal and Ocean Engineering

  • Kim, Taeyoon;Lee, Woo-Dong
    • 한국해양공학회지
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    • 제36권3호
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    • pp.194-210
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    • 2022
  • Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.