• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.3
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• pp.323-331
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• 2021

With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO₂, NH₃, H₂S, CH₄) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1252-1255
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• 2006

본 논문은 정보검색, 정보추출, 번역, 자연어처리 등의 작업을 위한 통계적 방법론에서 필요한 학습 데이터 구축을 효율적으로 하기 위한 학습 데이터 반자동 구축 장치 및 그 방법에 대하여 기술한다. 본 논문에서는 학습 데이터 구축양을 줄이기 위해서 능동 학습을 이용한다. 또한 최근 각광 받고 있는 Conditional Random Fields(CRF)를 능동학습에 이용하기 위해서 CRF를 이용한 Confidence measure를 정의한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.111-120
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• 2015

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

• MALSORI
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• no.66
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• pp.87-103
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• 2008

We describe a new active learning method in conditional random fields (CRFs) framework for keyphrase extraction. To save elaboration in annotation, we use diversity and representative measure. We select high diversity training candidates by sentence confidence value. We also select high representative candidates by clustering the part-of-speech patterns of contexts. In the experiments using dialog corpus, our method achieves 86.80% and saves 88% training corpus compared with those of supervised method. From the results of experiment, we can see that the proposed method shows improved performance over the previous methods. Additionally, the proposed method can be applied to other applications easily since its implementation is independent on applications.

• Communications of the Korean Mathematical Society
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• v.21 no.4
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• pp.779-786
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• 2006

Let $\mathbb{X}_t$ be an m-dimensional linear process defined by $\mathbb{X}_t=\sum{_{j=0}^\infty}\;A_j\;\mathbb{Z}_{t-j}$, t = 1, 2, $\ldots$, where $\mathbb{Z}_t$ is a sequence of m-dimensional random vectors with mean 0 : $m\times1$ and positive definite covariance matrix $\Gamma:m{\times}m$ and $\{A_j\}$ is a sequence of coefficient matrices. In this paper we give sufficient conditions so that $\sum{_{t=1}^{[ns]}\mathbb{X}_t$ (properly normalized) converges weakly to Wiener measure if the corresponding result for $\sum{_{t=1}^{[ns]}\mathbb{Z}_t$ is true.

• Journal of the Korean Institute of Telematics and Electronics
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• v.8 no.6
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• pp.33-41
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• 1971

This paper presents a theoretical calculation and measurements for the RCS(Radar Cross Scetion) value of a moving complex target, a small metal aircraft. The front view of aircraft on the drawing is divided in to several simple models to calculate its RCS value by the relative phase nlethod and the random phase method at the given frequency. The aircraft, cessna 305, inbounded from 170$^{\circ}$ to X international airport, is searched by radar with the wave length of 11cm to measure its miximum range which is necessary to determine the RCS value. The measured data are found to be similar to the theoretical values.

• Journal of the Korean Statistical Society
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• v.3 no.1
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• pp.13-16
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• 1974

In my former paper [3] I defined an almost periodicity of weakly sationary random processes (a.p.w.s.p.) and presented some basic results of it. In this paper I shall present some notes on the Fourier series of an a.p.w.s.p., resulting from [3]. All the conditions at the introduction of [3] are assumed to hold without repreating them here. The essential facts are as follows : The weakly stationary process $X(t,\omega), t\in(-\infty,\infty), \omega\in\Omega$, defined on a probability space $(\Omega,a,P)$, has a spectral representation $$X(t,\omega)=\int_{-\infty}^{infty}{e^{it\lambda\xi}(d\lambda,\omega)},$$ where $\xi(\lambda)$ is a random measure. Then, the continuous covariance $\rho(\mu) = E(X(t+u) X(t))$ has the form $$\rho(u)=\int_{-\infty}^{infty}{e^{iu\lambda}F(d\lambda)},$$ $E$\mid$\xi(\lambda+0)-\xi(\lambda-0)$\mid$^2 = F(\lambda+0) - F(\lambda-0) \lambda\in(-\infty,\infty)$, assumimg that $\rho(u)$ is a uniformly almost periodic function.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.35-43
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• 1993

The principal objective of a sampling plan is to make efficient use of the budget allocated and to obtain as precise an estimate of a population parameter as possible. In order to estimate the proportion of defectives produced or to determine some measure of product Quality, it is necessary to select random samples which represent a population parameter of the process. In this case, the two stage sampling is more efficient and convenient than simple random sampling. Therefore this paper aims to propose the design procedures of two stage sampling plan to obtain a representative samples in considering the sampling precision under the restricted sampling unspection cost.

• Safety and Health at Work
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• v.12 no.3
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• pp.296-303
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• 2021

Background: Rapid population aging in developed countries has resulted in the working-age population increasingly being tasked with the provision of informal care. Methods: An educational intervention was delivered to 21 carer-employees employed at a Canadian University. Work role function, job security, schedule control, work-family conflict, familywork conflict, and supervisor and coworker support were measured as part of an aggregated workplace experience score. This score was used to measure changes pre/post intervention and at a follow-up period approximately 12 months post intervention. Three random intercept models were created via linear mixed modeling to illustrate changes in participants' workplace experience across time. Results: All three models reported statistically significant random and fixed effects intercepts, with a positive coefficient of change. Conclusion: This suggests that the intervention demonstrated an improvement of the workplace experience score for participants over time, with the association particularly strong immediately after intervention.