• Title, Summary, Keyword: machine learning

Search Result 3,003, Processing Time 0.04 seconds

Unsupervised Machine Learning based on Neighborhood Interaction Function for BCI(Brain-Computer Interface) (BCI(Brain-Computer Interface)에 적용 가능한 상호작용함수 기반 자율적 기계학습)

  • Kim, Gui-Jung;Han, Jung-Soo
    • Journal of Digital Convergence
    • /
    • v.13 no.8
    • /
    • pp.289-294
    • /
    • 2015
  • This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

Hybrid Approach to Sentiment Analysis based on Syntactic Analysis and Machine Learning (구문분석과 기계학습 기반 하이브리드 텍스트 논조 자동분석)

  • Hong, Mun-Pyo;Shin, Mi-Young;Park, Shin-Hye;Lee, Hyung-Min
    • Language and Information
    • /
    • v.14 no.2
    • /
    • pp.159-181
    • /
    • 2010
  • This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

  • PDF

Effectiveness of Normalization Pre-Processing of Big Data to the Machine Learning Performance (빅데이터의 정규화 전처리과정이 기계학습의 성능에 미치는 영향)

  • Jo, Jun-Mo
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.14 no.3
    • /
    • pp.547-552
    • /
    • 2019
  • Recently, the massive growth in the scale of data has been observed as a major issue in the Big Data. Furthermore, the Big Data should be preprocessed for normalization to get a high performance of the Machine learning since the Big Data is also an input of Machine Learning. The performance varies by many factors such as the scope of the columns in a Big Data or the methods of normalization preprocessing. In this paper, the various types of normalization preprocessing methods and the scopes of the Big Data columns will be applied to the SVM(: Support Vector Machine) as a Machine Learning method to get the efficient environment for the normalization preprocessing. The Machine Learning experiment has been programmed in Python and the Jupyter Notebook.

Predictive maintenance architecture development for nuclear infrastructure using machine learning

  • Gohel, Hardik A.;Upadhyay, Himanshu;Lagos, Leonel;Cooper, Kevin;Sanzetenea, Andrew
    • Nuclear Engineering and Technology
    • /
    • v.52 no.7
    • /
    • pp.1436-1442
    • /
    • 2020
  • Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

Evaluation of geological conditions and clogging of tunneling using machine learning

  • Bai, Xue-Dong;Cheng, Wen-Chieh;Ong, Dominic E.L.;Li, Ge
    • Geomechanics and Engineering
    • /
    • v.25 no.1
    • /
    • pp.59-73
    • /
    • 2021
  • There frequently exists inadequacy regarding the number of boreholes installed along tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling geological characterization, they aim to detect specific object (e.g., water body and karst cave). There remains great motivation for the industry to develop a real-time identification technology relating complex geological conditions with the existing tunnelling parameters. This study explores the potential for the use of machine learning-based data driven approaches to identify the change in geology during tunnel excavation. Further, the feasibility for machine learning-based anomaly detection approaches to detect the development of clayey clogging is also assessed. The results of an application of the machine learning-based approaches to Xi'an Metro line 4 are presented in this paper where two tunnels buried in the water-rich sandy soils at depths of 12-14 m are excavated using a 6.288 m diameter EPB shield machine. A reasonable agreement with the measurements verifies their applicability towards widening the application horizon of machine learning-based approaches.

Fruit price prediction study using artificial intelligence (인공지능을 이용한 과일 가격 예측 모델 연구)

  • Im, Jin-mo;Kim, Weol-Youg;Byoun, Woo-Jin;Shin, Seung-Jung
    • The Journal of the Convergence on Culture Technology
    • /
    • v.4 no.2
    • /
    • pp.197-204
    • /
    • 2018
  • One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

Fault Detection for Seismic Data Interpretation Based on Machine Learning: Research Trends and Technological Introduction (기계 학습 기반 탄성파 자료 단층 해석: 연구동향 및 기술소개)

  • Choi, Woochang;Lee, Ganghoon;Cho, Sangin;Choi, Byunghoon;Pyun, Sukjoon
    • Geophysics and Geophysical Exploration
    • /
    • v.23 no.2
    • /
    • pp.97-114
    • /
    • 2020
  • Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.

Comparison of Models to Forecast Real Estates Index Introducing Machine Learning (머신러닝을 이용한 부동산 지수 예측 모델 비교)

  • Lee, Ju-mi;Park, Sung-Hoon;Cho, Sang-ho;Kim, Ju-Hyung
    • Journal of the Architectural Institute of Korea
    • /
    • v.37 no.1
    • /
    • pp.191-199
    • /
    • 2021
  • As the real estates occupy major portion of domestic households assets, relevant issue has been dealt seriously by the Korean government. However, apartment prices in downtown Seoul, the capital city, have soared despite various policies. Forecasting the real estate market trend has become an important research topic in order to provide information for establishing policies. In the prediction of the real estate market in the previous studies, two research directions were classified as follows: quantitative economic models and machine learning models. Regarding this trend, there was a need for comparative research on machine learning models, emerging methods, that are used to compare and predict various real estate indices. In this study, the machine learning model RF(Random Forest), XGBoost(eXtreme Gradient Boosting), and LSTM (Long Short Term Memory) are used to select suitable machine learning models for selected real estate index and conduct a comparative study to validate predictive power of machine learning models. Apartment sales index, land price index, charter price index, and real estate psychological index using univariate variables are predicted. In addition, RF, XGBoost and LSTM models all tended to be generally marginal with RMSE values of 0.0268, 0.0296, and 0.0259 in charter(Jeonse), Korean traditional pre-deposit rental system, price index data with linear but small variants. This shows that the prediction of the real estate index is deviated from the prediction accuracy of machine learning models depending on the periodic characteristics and data characteristics of the real estate index.

RFA: Recursive Feature Addition Algorithm for Machine Learning-Based Malware Classification

  • Byeon, Ji-Yun;Kim, Dae-Ho;Kim, Hee-Chul;Choi, Sang-Yong
    • Journal of the Korea Society of Computer and Information
    • /
    • v.26 no.2
    • /
    • pp.61-68
    • /
    • 2021
  • Recently, various technologies that use machine learning to classify malicious code have been studied. In order to enhance the effectiveness of machine learning, it is most important to extract properties to identify malicious codes and normal binaries. In this paper, we propose a feature extraction method for use in machine learning using recursive methods. The proposed method selects the final feature using recursive methods for individual features to maximize the performance of machine learning. In detail, we use the method of extracting the best performing features among individual feature at each stage, and then combining the extracted features. We extract features with the proposed method and apply them to machine learning algorithms such as Decision Tree, SVM, Random Forest, and KNN, to validate that machine learning performance improves as the steps continue.

Machine Learning Applied to Uncovering Gene Regulation

  • Craven, Mark
    • Proceedings of the Korean Society for Bioinformatics Conference
    • /
    • /
    • pp.61-68
    • /
    • 2000
  • Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

  • PDF