• 한국IT서비스학회지
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• 제16권4호
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.209-214
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• 2016

The purpose of this study was to classify healty persons and Parkinson disease patients from the vocal characteristics of healty persons and the of Parkinson disease patients using Machine Learning algorithms. So, we compared the most widely used algorithms for Machine Learning such as J48 algorithm and REPTree algorithm. In order to evaluate the classification performance of the two algorithms, the results were compared with depending on vocal characteristics. The classification performance of depending on vocal characteristics show 88.72% and 84.62%. The test results showed that the J48 algorithms was superior to REPTree algorithms.

• 전자통신동향분석
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• 제35권4호
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• pp.103-114
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• 2020

In this study, a machine learning (ML) algorithm is analyzed and summarized as a self-organizing network (SON) realization technology that can minimize expert intervention in the planning, configuration, and optimization of mobile communication networks. First, the basic concept of the ML algorithm in which areas of the SON of this algorithm are applied, is briefly summarized. In addition, the requirements and performance metrics for ML are summarized from the SON perspective, and the ML algorithm that has hitherto been applied to an SON achieves a performance in terms of the SON performance metrics.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• 정보처리학회논문지D
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• 제16D권5호
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• pp.729-736
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• 2009

소프트웨어 공학에서 정확한 개발 비용 예측은 성공적인 개발 프로젝트를 위한 필수적인 요소로, 현재까지 많은 소프트웨어 비용산정을 위한 모델들이 개발되어 왔다. 전통적인 통계적 기법부터 기계학습을 적용한 알고리즘까지 다양한 분야의 아이디어를 접목하고 있다. 본 논문에서는 소프트웨어 개발 비용 예측을 위한 방법으로 유전 알고리즘과 서포트 벡터 머신의 회귀모델인 서포트 벡터 회귀를 결합한 GA-SVR 모델을 제안한다. 제안된 모델은 기존의 연구에 비해 향상된 결과를 보이고 있다.

• 한국인공지능학회지
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• 제11권3호
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• pp.23-27
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• 2023

The purpose of this study is to identify and analyze suicide factors of adolescents using the Random Forest algorithm. According to statistics on the cause of death by the National Statistical Office in 2019, suicide was the highest cause of death in the 10-19 age group, which is a major social problem. Using machine learning algorithms, research can predict whether individual adolescents think of suicide without investigating suicidal ideation and can contribute to protecting adolescents and analyzing factors that affect suicide, establishing effective intervention measures. As a result of predicting with the random forest algorithm, it can be said that the possibility of identifying and predicting suicide factors of adolescents was confirmed. To increase the accuracy of the results, continuous research on the factors that induce youth suicide is necessary.

• 한국정보통신학회논문지
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• 제25권2호
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• pp.165-170
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• 2021

기계학습은 최근 대부분의 분야에서 적용하여 연구를 하고 있다. 이것은 기계학습의 결과가 결정된 것이 아니라 입력데이터의 학습으로 목적함수를 생성하고, 이를 통해 통하여 새로운 데이터에 대한 판단이 가능하기 때문이다. 또한, 축적된 데이터의 증가는 기계학습 결과의 정확도에 영향을 미친다. 이에 수집된 데이터는 기계학습에 중요한 요인이다. 제안하는 본 시스템은 서비스 제공을 위한 클라우드 시스템과 지역의 포그 시스템의 융합 시스템이다. 이에 클라우드 시스템은 서비스를 위한 머신러닝과 기반 구조를 제공하고, 포그 시스템은 클라우드와 사용자의 중간에 위치하여 데이터 수집 및 정제를 수행한다. 이를 적용하기 위한 데이터는 스마트기기에서 발생하는 센세 데이터로 한다. 이에 적용된 기계학습 기법은 분류를 위한 SVM알고리즘, 상태 인지를 위한 RNN 알고리즘을 이용한다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.982-990
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• 2018

In recent years, many wearable devices and mobile apps related to life care have been developed, and a service for measuring the movement during walking and showing the amount of exercise has been provided. However, they do not measure walking in detail, so there may be errors in the total calorie consumption. If the user's behavior is measured by a multi-axis sensor and learned by a machine learning algorithm to recognize the kind of behavior, the detailed operation of walking can be autonomously distinguished and the total calorie consumption can be calculated more than the conventional method. In order to verify this, we measured activities and created a model using a machine learning algorithm. As a result of the comparison experiment, it was confirmed that the average accuracy was 12.5% or more higher than that of the conventional method. Also, in the measurement of the momentum, the calorie consumption accuracy is more than 49.53% than that of the conventional method. If the activity recognition is performed using the wearable device and the machine learning algorithm, the accuracy can be improved and the energy consumption calculation accuracy can be improved.

• 반도체디스플레이기술학회지
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• 제19권3호
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• pp.77-81
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• 2020

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.