• International journal of advanced smart convergence
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• v.13 no.1
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• pp.48-56
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• 2024

The application of artificial intelligence (AI) is becoming increasingly important to coping with air pollution. AI is effective in coping with it in various ways including air pollution forecasting, monitoring, and control, which is attracting a lot of attention. This attention has created high need for analyzing studies on AI and air pollution. To contribute for satisfying it, this study performed bibliometric analyses on the studies on AI and air pollution from 2012 to 2022 using the Web of Science database. This study analyzed them in various aspects such as the trend in the number of articles, the trend in the number of citations, the top 10 countries of origin, the top 10 research organizations, the top 10 research funding agencies, the top 10 journals, the top 10 articles in terms of total citations, and the distribution by languages. This study not only reports the bibliometric analysis results but also reveals the eight distinct features in the research steam in studies on AI and air pollution, identified from the bibliometric analysis results. They are expected to make a useful contribution for understanding the research stream in AI and air pollution.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.388-393
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• 2024

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.17-23
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• 2018

The rapid development of mobile technology has increased the use of mobile devices, and the possibility of security incidents is also increasing. The leakage of information through photos is the most representative. Previous methods for preventing this are disadvantageous in that they can not take pictures for other purposes. In this paper, we design and implement a system to prevent information leakage through photos using object recognition and beacon. The system inspects pictures through object recognition based on deep learning and verifies whether security policies are violated. In addition, the location of the mobile device is identified through the beacon and the appropriate rules are applied. Web applications for administrator allow you to set rules for taking photos by location. As soon as a user takes a photo, they apply appropriate rules to the location to automatically detect photos that do not conform to security policies.

• Ocean and Polar Research
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• v.35 no.4
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• pp.427-439
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• 2013

Carbon cycling and productivity within Weno Island of Micronesia enclosed by the coral reef may be likely self-maintained and insignificantly affected by the open ocean. Therefore, it is important to understand the role of the mangrove known as providing the organic matter and habitats for many organisms in this enclosed area. In order to trace the nutritional source of fauna (mostly invertebrates) in the mangrove forest of Weno island, we analyzed the fatty acid (FA) and carbon and nitrogen stable isotopes of potential nutritional sources (mangrove leaf & pneumatophore, seagrass leaf & root, surface sediment, and particulate organic matter (POM) in water) and consumers (4 gastropods and anomura). The mangrove and seagrass contained the abundance of 18:2${\omega}$6, and 18:3${\omega}$3, whereas FAs associated with phytoplankton and bacteria were accounted for a high proportion in the surface sediment and POM. FA composition of consumers was found to be similar to those of the surface sediment, mangrove, and seagrass. These were also confirmed through the mixing model of stable isotope for contribution of nutritional sources to consumers. Overall results with the feeding types of investigated mangrove fauna indicate that investigated mangrove fauna obtained their nutrition from the various sources, i.e. the mangrove for Littorina cf. scabra, the microalgae for Strombus sp., and omnivorous Pagurus sp. and Terebralia cf. palustris. However, it is obvious that the nutrition of most species living in the mangrove ecosystem is highly dependent on the mangrove, either directly or indirectly. More detail food-web structure and function of the mangrove ecosystem would be established with the analysis of additional fauna and flora.

• Korean Journal of Cleft Lip And Palate
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• v.3 no.2
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• pp.41-50
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• 2000

The alar base on the cleft side in unilateral complete cleft lip, alveolus and palate is markedly displaced laterally, caudally and dorsally, By incising the pyriform margin from the cleft margin of the alveolar process, including mucosa of the anterior part of the inferior turbinate, to the upper end of the postnasal vestibular fold, the alar base is released from the maxilla, A physiological correction of nasal deformity can be accomplished by careful reconstruction of nasolabial muscle integrity, functional repair of the orbicular muscle, raising and rotating the displaced alar cartilage, and finally by lining the lateral nasal vestibule, The inferior maxillary head of the nasal muscle complex is identified as the deeper muscle just below the web of the nostril, The muscle is repositioned inframedially, so that it is sutured to the periosteum that overlies the facial aspect of the premaxilla in the region of the developing lateral incisor tooth, And then, the deep superior part of the orbicular muscle is sutured to the periosteum and the fibrous tissue at the base of the septum, just in front of the anterior nasal spine, The nasal floor is surgically created by insertions of the nasal muscle complex in deep plane and of the orbicular muscle in superficial one, The upper part of the lateral nasal vestibular defect is sutured by shifting the alar flap cephalically, The middle and lower parts of this defect are closed by use of cleft margin flaps of the philtral and lateral segments, respectively, Authors stress the importance of nasal floor reconstruction at primary surgery and report the technique and postoperative results.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1013-1023
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• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.123-130
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• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.44-49
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• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.505-512
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• 2021

Background: The occurrence of environmental disease is known to be associated with chronic exposure to toxic chemicals, including waterborne contaminants, air/indoor pollutants, asbestos, ingredients in humidifier disinfectants, etc. Objectives: In this study, we reviewed toxicological studies related to environmental disease as defined by the Environmental Health Act in Korea and toxic chemicals. We also suggested a direction for future toxicological research necessary for the prevention and management of environmental disease. Methods: Trends in previous studies related to environmental disease were investigated through PubMed and Web of Science. A detailed review was provided on toxicological studies related to the humidifier disinfectants. We identified adverse outcome pathways (AOPs) that can be linked to the induction of environmental diseases, and proposed a chemical screening system that uses AOP, chemical toxicity big data, and deep learning models to select chemicals that induce environmental disease. Results: Research on chemical toxicity is increasing every year, but there is a limitation to revealing a clear causal relationship between exposure to chemicals and the occurrence of environmental disease. It is necessary to develop various exposure- and effect-biomarkers related to disease occurrence and to conduct toxicokinetic studies. A novel chemical screening system that uses AOP and chemical toxicity big data could be useful for selecting chemicals that cause environmental diseases. Conclusions: From a toxicological point of view, developing AOP related to environmental diseases and a deep learning-based chemical screening system will contribute to the prevention of environmental diseases in advance.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.