• Journal of Information Processing Systems
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• 제17권2호
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• pp.213-226
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• 2021

Given that most of the link prediction algorithms for signed social networks can only complete sign prediction, a novel algorithm is proposed aiming to achieve both link prediction and sign prediction in signed networks. Based on the structural balance theory, the local link tightness and global link tightness are defined respectively by using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the total similarity of the node pair can be obtained by combining them. Its absolute value measures the possibility of the two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectiveness and correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are also carried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, and PSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like area under the curve (AUC), Precision, improved AUC', improved Accuracy', and so on. Results show that the proposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy and computational complexity.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권9호
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• pp.437-444
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• 2017

영화 매출에 대한 연구가 많이 있었지만 공통적인 핵심주제는 영화 매출에 대한 효율적인 예측모델을 훈련하는 것이다. 그러나 과거의 연구에서는 예측 오차를 발생시키는 요인에 대한 분석이 부족하여 이러한 오차를 줄이는 방법에 대한 연구가 이루어지지 않았다. 본 연구에서는 같은 시기에 개봉되고 있는 영화들 간의 영향이 예측 오차에 대한 주요인이라는 가정하에 한 영화가 다른 경쟁영화에서 영향을 받는 정도(경쟁값)를 분석하여 영화매출예측 성능을 향상시키는 것을 목표로 한다. 경쟁값을 예측하기 위하여, 먼저 경쟁값의 극성(양수/음수)에 대해 분류하고 양수의 확률과 음수의 확률을 계산한 다음 회귀분석을 이용하여 양수인 값과 음수인 값을 예측한다. 마지막으로, 확률값과 예측값을 통하여 경쟁값의 기댓값을 계산하여 초기 예측된 매출을 보정한다. 실험 결과에 의하면 제안 방법을 통하여 영화 매출 예측의 정확도가 향상됨을 알 수 있었다.

• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.8-16
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• 2024

Communication with the deaf has always been crucial. Deaf and hard-of-hearing persons can now express their thoughts and opinions to teachers through sign language, which has become a universal language and a very effective tool. This helps to improve their education. This facilitates and simplifies the referral procedure between them and the teachers. There are various bodily movements used in sign language, including those of arms, legs, and face. Pure expressiveness, proximity, and shared interests are examples of nonverbal physical communication that is distinct from gestures that convey a particular message. The meanings of gestures vary depending on your social or cultural background and are quite unique. Sign language prediction recognition is a highly popular and Research is ongoing in this area, and the SVM has shown value. Research in a number of fields where SVMs struggle has encouraged the development of numerous applications, such as SVM for enormous data sets, SVM for multi-classification, and SVM for unbalanced data sets.Without a precise diagnosis of the signs, right control measures cannot be applied when they are needed. One of the methods that is frequently utilized for the identification and categorization of sign languages is image processing. African Buffalo Optimization using Support Vector Machine (ABO+SVM) classification technology is used in this work to help identify and categorize peoples' sign languages. Segmentation by K-means clustering is used to first identify the sign region, after which color and texture features are extracted. The accuracy, sensitivity, Precision, specificity, and F1-score of the proposed system African Buffalo Optimization using Support Vector Machine (ABOSVM) are validated against the existing classifiers SVM, CNN, and PSO+ANN.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.57-60
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• 2003

We have proposed the adaptive noise reduction algorithm using the MDFT. The algorithm proposed use the linear prediction coefficients of the AR method based on Sign algorithm that is the modified LMS instead of the least mean square(LMS). The signals with a random noise tracking performance are examined through computer simulations and confirmed that the high speed adaptive noise reduction processing system is realized with rapid convergence.

• 동의생리병리학회지
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• 제25권1호
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• pp.138-143
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• 2011

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. As a part of researches for standardization and objectification of differentiation of syndromes for stroke, in this present study, we tried to develop the statistical diagnostic tool discriminating the 4 subtypes of syndrome differentiation using the essential indices considering the sex. Discriminant analysis was carried out using clinical data collected from 1,448 stroke patients who was identically diagnosed for the syndrome differentiation subtypes diagnosed by two clinical experts with more than 3 year experiences. Empirical discriminant model(V) for different sex was constructed using 61 significant symptoms and sign indices selected by stepwise selection. We comparison. We make comparison a between discriminant model(V) and discriminant model(IV) using 33 significant symptoms and sign indices selected by stepwise selection. Development of statistical diagnostic tool discriminating 4 subtypes by sex : The discriminant model with the 24 significant indices in women and the 19 significant indices in men was developed for discriminating the 4 subtypes of syndrome differentiation including phlegm-dampness, qi-deficiency, yin-deficiency and fire-heat. Diagnostic accuracy and prediction rate of syndrome differentiation by sex : The overall diagnostic accuracy and prediction rate of 4 syndrome differentiation subtypes using 24 symptom and sign indices was 74.63%(403/540) and 68.46%(89/130) in women, 19 symptom and sign indices was 72.05%(446/619) and 70.44%(112/159) in men. These results are almost same as those of that the overall diagnostic accuracy(73.68%) and prediction rate(70.59%) are analyzed by the discriminant model(IV) using 33 symptom and sign indices selected by stepwise selection. Considering sex, the statistical discriminant model(V) with significant 24 symptom and sign indices in women and 19 symptom and sign indices in men, instead of 33 indices would be used in the field of oriental medicine contributing to the objectification of syndrome differentiation with parsimony rule.

• Journal of Computing Science and Engineering
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• 제10권3호
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• pp.95-101
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• 2016

In this study, we propose a new system for constructing parallel corpora for sign languages, which are generally under-resourced in comparison to spoken languages. In order to achieve scalability and accessibility regarding data collection and corpus construction, our system utilizes deep learning-based techniques and predicts depth information to perform pose estimation on hand information obtainable from video recordings by a single RGB camera. These estimated poses are then transcribed into expressions in SignWriting. We evaluate the accuracy of hand tracking and hand pose estimation modules of our system quantitatively, using the American Sign Language Image Dataset and the American Sign Language Lexicon Video Dataset. The evaluation results show that our transcription system has a high potential to be successfully employed in constructing a sizable sign language corpus using various types of video resources.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권1호
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• pp.6-14
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• 2011

This paper presents a high-speed QR decomposition architecture for the multi-input-multi-output (MIMO) receiver based on Givens rotation. Under fast-varying channel, since the inverse matrix calculation has to be performed frequently in MIMO receiver, a high performance and low latency QR decomposition module is highly required. The proposed QR decomposition architecture is composed of Sign-Select Lookahead (SSL) coordinate rotation digital computer (CORDIC). In the SSL-CORDIC, the sign bits, which are computed ahead to select which direction to rotate, are used to select one of the last iteration results, therefore, the data dependencies on the previous iterations are efficiently removed. Our proposed QR decomposition module is implemented using TSMC 0.25 ${\mu}M$ CMOS process. Experimental results show that the proposed QR architecture achieves 34.83% speed-up over the Compact CORDIC based architecture for the 4 ${\times}$ 4 matrix decomposition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.6017-6037
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• 2017

In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.

• 대한한방소아과학회지
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• 제31권4호
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• pp.31-38
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• 2017

Objectives The purpose of this study was to find out the clinically reliable relationships between the Risser sign and chronological age, bone age, menarche, and adult height prediction (AHP) and to evidence the reliability of the Risser sign. Methods This study had been carried out with 50 children who had their growth checked in an oriental medical hospital from January 2015 to February 2017. We investigated Risser sign in AP X-rays with iliac crest, bone age, AHP for all 50 children and the timing of menarche from the 22 girls in the study subjects. We also investigated a correlation between the Risser stage and the other indicators to analyze statistical data. Results The mean chronological ages of Risser 1, 2, 3 and 4 were 11.2, 12.6, 14.4, and 15.5 years respectively for the boys and 10.8, 12.2, 13.8 and 14.8 years respectively for the girls. The mean bone ages of Risser 1, 2, 3 and 4 were 12.3, 13.6, 15.7 and 16.5 years respectively for the boys and 11.7, 13.8, 14.3 and 14.9 years respectively for the girls. We analyzed 22 girls' Risser stages in accordance with the duration from menarche. The result showed that in the first six months after menarche, all girls were in Risser 1 and 2; in the next six months, the girls were in Risser 2 on average; in the next 12 months, all girls were in Risser 3 and 4; after more than two years from menarche, all girls were in Risser 4. The mean remaining growth height of Risser 1, 2, 3 and 4 were 27.8, 17.3, 4.4 and 1.0 cm respectively for the boys and 14.5, 5.1, 3.1 and 1.1 cm respectively for the girls. The Risser stage was correlated strongly with chronological age (Spearman's rho=0.707 (boy), 0.841 (girl)), bone age (Spearman's rho=0.869 (boy), 0.875 (girl)), duration from menarche (Spearman's rho=0.909) and remaining growth height (Spearman's rho=-0.784 (boy), -0.878 (girl)). Conclusions This study showed that the Risser sign can be useful in assessing skeletal maturity and predicting remaining growth height based on the Risser stage and the other growth indicators.