• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.556-561
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• 2006

It is very important to classify the DNA Chip image pattern in order to acquire useful information about genetic disease of people. In this paper, we developed the novel pattern classification method of DNA Chip image using MLP based back-propagation and Self organizing Map learning algorithm. And then we compared and analyzed these classified pattern results. Also we carried out experiment in the MV2440 board using CPU Cote for S3C2440(ARM 920T) and PC environment, and displayed its results in order to give the genetic information to user mote easily in various environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1623-1632
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• 2008

In food choice, consumers consider importantly food safely and reliability focusing on a high qualify control. Also they are concerned about sustainable production and consumption considering of area and environment. In this paper, We propose and design the high-quality seafood production support system that is based on international sanitary standard haccp, traceability and eco-fiendly seafood certification for safety and reliability of the seafood production. The system is categorized into five part: aquafarm haccp, environmental control monitoring, traceability, disease prevention and messenger service and seafood price inquiry service. The proposed system utilize diverse ubiquitous-it technologies like usn, network cctv, mobile device etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.585-588
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• 2009

Wireless Sensor Network (WSN) is consisted of lots of tiny sensor nodes with limited processing power and computing resources. Thus, the most critical and fundamental element of WSN technology is sensor node, which gathers environmental information and transmits it to the user application systems. Due to the technological advancement, sensor nodes are become smaller and more intelligent, hence, expand their application area. Specifically, implantable wireless sensor node technology, to monitor and treat disease by implanting tiny sensor nodes into human body or livestock, shows further directions of WSN. In this paper, we have designed an implantable wireless sensor node to monitor livestock body temperature in real time. We also discussed on the additional considerations to implement real time bio-monitoring systems.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.588-600
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• 2019

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.3
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• pp.245-255
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• 2021

Necrotizing enterocolitis (NEC) is a disease with high morbidity and mortality that occurs mainly in premature born infants. The pathophysiologic mechanisms indicate that gastrointestinal dysbiosis is a major risk factor. We searched for relevant articles published in PubMed and Google Scholar in the English language up to October 2020. Articles were extracted using subject headings and keywords of interest to the topic. Interesting references in included articles were also considered. Network meta-analysis suggests the preventive efficacy of Bifidobacterium and Lactobacillus spp., but even more for mixtures of Bifidobacterium, Streptococcus, and Bifidobacterium, and Streptococcus spp. However, studies comparing face-to-face different strains are lacking. Moreover, differences in inclusion criteria, dosage strains, and primary outcomes in most trials are major obstacles to providing evidence-based conclusions. Although adverse effects have not been reported in clinical trials, case series of adverse outcomes, mainly septicemia, have been published. Consequently, systematic administration of probiotic bacteria to prevent NEC is still debated in literature. The risk-benefit ratio depends on the incidence of NEC in a neonatal intensive care unit, and evidence has shown that preventive measures excluding probiotic administration can result in a decrease in NEC.

• The Korean Journal of Health Service Management
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• v.12 no.2
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• pp.113-123
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• 2018

Objectives : The purpose of this study was to investigate the effect of participation in social activities on the subjective health satisfaction of the elderly in groups with and without chronic diseases. Methods : Data were used from the "2014 the Korean Elderly Survey" and the subjects were 10,451 persons aged 65 years or older. Data analysis was conducted using SPSS 18.0 statistical package. Results : The results of this study were as follows. In the case of the elderly without chronic diseases, only the employment status (${\beta}=.135$, p<.01) had a significant effect on the health of the elderly. In the case of elderly people with chronic illness, participation in lifelong education (${\beta}=.183$, p<.001), participation in social group (${\beta}=.277$, p<.001), volunteer work experience (${\beta}=.060$, p<.05), and employment status (${\beta}=.342$, p<.001) had a significant effect on health. Conclusions : Policies and systems are needed to actively encourage and support the social activities of the elderly. Additionly, care and attention are needed to provide social jobs for the elderly and build a sustainable network.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.1-11
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• 2022

Online video telephony systems have been increasingly used in several industrial areas because of coronavirus disease 2019 (COVID-19) spread. The existing session initiation protocol (SIP)-based video call system is being usefully utilized, however, there is a limitation that it is very inconvenient for users to transmit additional information during conversation to the other party in real time. To overcome this problem, an enhanced scheme is presented based on augmented real-time reality (AR). In this scheme, augmented information is automatically searched from the Internet and displayed on the user's device during video telephony. The proposed approach was qualitatively evaluated by comparing it with other conferencing systems. Furthermore, to evaluate the feasibility of the approach, we implemented a simple network application that can generate SIP call requests and answer with AR object pre-fetching. Using this application, the call setup time was measured and compared between the original SIP and pre-fetching schemes. The advantage of this approach is that it can increase the convenience of a user's mobile phone by providing a way to automatically deliver the required text or images to the receiving side.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.