• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• Journal of Genetic Medicine
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• 제16권1호
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• pp.1-9
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• 2019

Noonan syndrome (NS) and NS-related disorders (cardio-facio-cutaneous syndrome, Costello syndrome, NS with multiple lentigines, or LEOPARD [lentigines, ECG conduction abnormalities, ocular hypertelorism, pulmonic stenosis, abnormal genitalia, retardation of growth and sensory neural deafness] syndrome) are collectively named as RASopathies. Clinical presentations are similar, featured with typical facial features, short stature, intellectual disability, ectodermal abnormalities, congenital heart diseases, chest & skeletal deformity and delayed puberty. During past decades, molecular etiologies of RASopathies have been growingly discovered. The functional perturbations of the RAS-mitogen-activated protein kinase pathway are resulted from the mutation of more than 20 genes (PTPN11, SOS1, RAF1, SHOC2, BRAF, KRAS, NRAS, HRAS, MEK1, MEK2, CBL, SOS2, RIT, RRAS, RASA2, SPRY1, LZTR1, MAP3K8, MYST4, A2ML1, RRAS2). The PTPN11 (40-50%), SOS1 (10-20%), RAF1 (3-17%), and RIT1 (5-9%) mutations are common in NS patients. In this review, the constellation of overlapping clinical features of RASopathies will be described based on genotype as well as their differential diagnostic points and management.

• 한국초등과학교육학회지:초등과학교육
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• 제38권2호
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• pp.275-286
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• 2019

The purpose of this study is to investigate the features of inquiry activities using technology in the 2015 revised elementary science digital textbooks. For this, we analyzed the features of inquiry context and inquiry method presented in inquiry activities using three kinds of technology: Virtual experiment, virtual reality and augmented reality. As a result, firstly, the most common types of 77 inquiry activities were realistic type which shows the phenomenon actually and vividly as possible and realistic-abstract type which shows the phenomena with the abstract concepts. Second, the ways of using three technologies were different depending on the processes of inquiry and the sub-domains of science. For example, virtual experiment technologies were mostly used in the contents of physics and chemistry with the inquiry context of realistic-abstract type for investigating the relationship between variables of experiments and describing the phenomena mechanically. On the other hand, virtual reality and augmented reality techniques tended to be used more frequently in biology and earth science contents with the inquiry context of realistic type for observing and describing the phenomena. Finally, we discussed educational implications in terms of developing and applying technology-based inquiry activities.

• 전기전자학회논문지
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• 제25권1호
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.704-707
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• 2021

Pigs' aggressive behavior represents one of the common issues that occur inside pigpens and which harm pigs' health and welfare, resulting in a financial burden to farmers. Continuously monitoring several pigs for 24 hours to identify those behaviors manually is a very difficult task for pig caretakers. In this study, we propose a lightweight video-based approach for monitoring pigs' aggressive behavior that can be implemented even in small-scale farms. The proposed system receives sequences of frames extracted from an RGB video stream containing pigs and uses MnasNet with a DM value of 0.5 to extract image features from pigs' ROI identified by predefined annotations. These extracted features are then forwarded to a lightweight LSTM to learn temporal features and perform behavior recognition. The experimental results show that our proposed model achieved 0.92 in recall and F1-score with an execution time of 118.16 ms/sequence.

• 산업경영시스템학회지
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• 제46권3호
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• pp.161-169
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• 2023

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권5호
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• pp.326-335
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• 2002

최근에 지연 계산 함수형 언어를 자바 프로그램으로 변환함으로써 지연 계산 함수형 언어 프로그램에 대해 코드 이동성을 제공하려는 연구가 있었다. 이러한 연구들은 자바와 지연 계산형 함수형 언어의 추상 기계가 가지는 구조적 유사성에 바탕을 두고 있다. 지연 계산 함수형 언어에 대한 추상 기계인 STGM(Spineless Tagless G-machine)과 자바 언어에 대한 추상 기계인 JVM(Java Virtual Machine)은 기억장소 재활용 체계와 스택 기계 구조를 가진다는 점에서 공통된 특징을 가지고 있다. 그러나 현재가지의 지연 계산 함수형 언어로부터 자바로의 변환 구조는 이와 같은 추상 기계 구조상의 공통점을 충분히 이용하지 못하였다. 본 논문에서는 STGM의 계산 모델을 eval-apply 모델로 새로이 정의함으로써 STGM과 JVM의 공통점을 충분히 이용하는 새로운 변환 구도를 제안한다. 새로이 제안된 변환 구도에서는 자바 스택(Java Virtual Machine Stack)을 사용하여 함수 계산을 수행하도록 함으로써 스택 시뮬레이션으로 인해 나타나는 자바에서의 배열 접근 부담을 제거하였다. 본 논문의 변환 구도에 의해 자바로 변환된 벤치마크 프로그램들은 기존의 변환 구도에 의해 변환된 경우보다 JDK 1.3에서 빠르게 동작한다.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제34권5호
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• pp.550-554
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• 2008

Bisphosphonates (BPs) are a class of agent used to treat patient with osteoporosis or malignant bone metastases. BPs can be categorized into 2 groups: nitrogen-containing and non-nitrogen containing. Nitrogen-containing BPs are considered to have more toxicity. Despite their clinical benefits, bisphosphonate-related osteonecrosis of jaw(BRONJ) is a significant complication to patients receveing these drugs. Since the first description of BRONJ in 2003 by Marx, the number of reports on BRONJ has been rapidly increasing. BRONJ is considered as an emerging problem in oral & maxillofacial surgery. Generally, osteonecrosis in the maxilla is rare, however BRONJ is found both in the maxilla and the mandible. This is an important feature of BRONJ compared to common infectious osteomyelitis of the jaw. Growing number of case reports, suggest that bisphosphonate therapy may cause exposed, necrotic bone. BRONJ has simillar features compared to IORN (infected osteoradionecrosis). BRONJ has meaningful features established through the interestigation on histopathologic and radiographic findings. These features have an impact on treatment plan and prognosis. This presentation contemplates on features of histopathologic and radiographic findings in bisphosphonate-related osteonecrosis of the jaw.