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Comparative Usefulness of Naver and Google Search Information in Predictive Models for Youth Unemployment Rate in Korea (한국 청년실업률 예측 모형에서 네이버와 구글 검색 정보의 유용성 분석)

  • Jung, Jae Un
    • Journal of Digital Convergence
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    • v.16 no.8
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    • pp.169-179
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    • 2018
  • Recently, web search query information has been applied in advanced predictive model research. Google dominates the global web search market in the Korean market; however, Naver possesses a dominant market share. Based on this characteristic, this study intends to compare the utility of the Korean web search query information of Google and Naver using predictive models. Therefore, this study develops three time-series predictive models to estimate the youth unemployment rate in Korea using the ARIMA model. Model 1 only used the youth unemployment rate in Korea, whereas Models 2 and 3 added the Korean web search query information of Naver and Google, respectively, to Model 1. Compared to the predictability of the models during the training period, Models 2 and 3 showed better fit compared with Model 1. Models 2 and 3 correlated different query information. During predictive periods 1 (continuous with the training period) and 2 (discontinuous with the training period), Model 3 showed the best performance. During predictive period 2, only Model 3 exhibited a significant prediction result. This comparative study contributes to a general understanding of the usefulness of Korean web query information using the Naver and Google search engines.

Investigating Opinion Mining Performance by Combining Feature Selection Methods with Word Embedding and BOW (Bag-of-Words) (속성선택방법과 워드임베딩 및 BOW (Bag-of-Words)를 결합한 오피니언 마이닝 성과에 관한 연구)

  • Eo, Kyun Sun;Lee, Kun Chang
    • Journal of Digital Convergence
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    • v.17 no.2
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    • pp.163-170
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    • 2019
  • Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

An Analysis on Determinants of the Capesize Freight Rate and Forecasting Models (케이프선 시장 운임의 결정요인 및 운임예측 모형 분석)

  • Lim, Sang-Seop;Yun, Hee-Sung
    • Journal of Navigation and Port Research
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    • v.42 no.6
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    • pp.539-545
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    • 2018
  • In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

Application of Google Search Queries for Predicting the Unemployment Rate for Koreans in Their 30s and 40s (한국 30~40대 실업률 예측을 위한 구글 검색 정보의 활용)

  • Jung, Jae Un;Hwang, Jinho
    • Journal of Digital Convergence
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    • v.17 no.9
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    • pp.135-145
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    • 2019
  • Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.

Character Motion Control by Using Limited Sensors and Animation Data (제한된 모션 센서와 애니메이션 데이터를 이용한 캐릭터 동작 제어)

  • Bae, Tae Sung;Lee, Eun Ji;Kim, Ha Eun;Park, Minji;Choi, Myung Geol
    • Journal of the Korea Computer Graphics Society
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    • v.25 no.3
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    • pp.85-92
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    • 2019
  • A 3D virtual character playing a role in a digital story-telling has a unique style in its appearance and motion. Because the style reflects the unique personality of the character, it is very important to preserve the style and keep its consistency. However, when the character's motion is directly controlled by a user's motion who is wearing motion sensors, the unique style can be discarded. We present a novel character motion control method that uses only a small amount of animation data created only for the character to preserve the style of the character motion. Instead of machine learning approaches requiring a large amount of training data, we suggest a search-based method, which directly searches the most similar character pose from the animation data to the current user's pose. To show the usability of our method, we conducted our experiments with a character model and its animation data created by an expert designer for a virtual reality game. To prove that our method preserves well the original motion style of the character, we compared our result with the result obtained by using general human motion capture data. In addition, to show the scalability of our method, we presented experimental results with different numbers of motion sensors.

Study on Anomaly Detection Method of Improper Foods using Import Food Big data (수입식품 빅데이터를 이용한 부적합식품 탐지 시스템에 관한 연구)

  • Cho, Sanggoo;Choi, Gyunghyun
    • The Journal of Bigdata
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    • v.3 no.2
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    • pp.19-33
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    • 2018
  • Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

Improvement of precipitation forecasting skill of ECMWF data using multi-layer perceptron technique (다층퍼셉트론 기법을 이용한 ECMWF 예측자료의 강수예측 정확도 향상)

  • Lee, Seungsoo;Kim, Gayoung;Yoon, Soonjo;An, Hyunuk
    • Journal of Korea Water Resources Association
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    • v.52 no.7
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    • pp.475-482
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    • 2019
  • Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

Causal inference from nonrandomized data: key concepts and recent trends (비실험 자료로부터의 인과 추론: 핵심 개념과 최근 동향)

  • Choi, Young-Geun;Yu, Donghyeon
    • The Korean Journal of Applied Statistics
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    • v.32 no.2
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    • pp.173-185
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    • 2019
  • Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

Classification of Negative Emotions based on Arousal Score and Physiological Signals using Neural Network (신경망을 이용한 다중 심리-생체 정보 기반의 부정 감성 분류)

  • Kim, Ahyoung;Jang, Eun-Hye;Sohn, Jin-Hun
    • Science of Emotion and Sensibility
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    • v.21 no.1
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    • pp.177-186
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    • 2018
  • The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.

Study on High-speed Cyber Penetration Attack Analysis Technology based on Static Feature Base Applicable to Endpoints (Endpoint에 적용 가능한 정적 feature 기반 고속의 사이버 침투공격 분석기술 연구)

  • Hwang, Jun-ho;Hwang, Seon-bin;Kim, Su-jeong;Lee, Tae-jin
    • Journal of Internet Computing and Services
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    • v.19 no.5
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    • pp.21-31
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    • 2018
  • Cyber penetration attacks can not only damage cyber space but can attack entire infrastructure such as electricity, gas, water, and nuclear power, which can cause enormous damage to the lives of the people. Also, cyber space has already been defined as the fifth battlefield, and strategic responses are very important. Most of recent cyber attacks are caused by malicious code, and since the number is more than 1.6 million per day, automated analysis technology to cope with a large amount of malicious code is very important. However, it is difficult to deal with malicious code encryption, obfuscation and packing, and the dynamic analysis technique is not limited to the performance requirements of dynamic analysis but also to the virtual There is a limit in coping with environment avoiding technology. In this paper, we propose a machine learning based malicious code analysis technique which improve the weakness of the detection performance of existing analysis technology while maintaining the light and high-speed analysis performance applicable to commercial endpoints. The results of this study show that 99.13% accuracy, 99.26% precision and 99.09% recall analysis performance of 71,000 normal file and malicious code in commercial environment and analysis time in PC environment can be analyzed more than 5 per second, and it can be operated independently in the endpoint environment and it is considered that it works in complementary form in operation in conjunction with existing antivirus technology and static and dynamic analysis technology. It is also expected to be used as a core element of EDR technology and malware variant analysis.