• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.81-86
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• 2022

In this work, we propose a method for automatically determining the domain of columns of file data structured by csv format. New data can be generated through convergence between data and data, and the consistency of the joined columns must be maintained in order for these new data to become an important resource. One of the methods for measuring data quality is a domain-based quality diagnosis method. Domain is the broadest indicator that defines the nature of each column, so a method of automatically determining it is necessary. Although previous studies mainly studied domain automatic discrimination of relational databases, this study developed a model that can automate domains using the characteristics of file data. In order to specialize in the domain discrimination of file data, the data were simplified and patterned using a regular expression, and the contents of the data header corresponding to the column name were analyzed, and the suffix used was used as a derived variable. When derivatives of regular expressions and suffixes were added, the result of automatically determining the domain with an accuracy of 95% greater than the existing method of 87% was derived. This study is expected to reduce the quality measurement period and number of people by presenting an automation methodology to the quality diagnosis of public data.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.

• Journal of the korean academy of Pediatric Dentistry
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• v.47 no.3
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• pp.344-351
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• 2020

Amelogenesis imperfecta (AI) occurs either in isolation or in association with other dental abnormalities and systemic disorder. A rare syndrome associating AI with nephrocalcinosis was named as Enamel Renal Syndrome (ERS; OMIM #204690). This syndrome is characterized by severe enamel hypoplasia, failed tooth eruption, intra pulpal calcifications, enlarged gingiva, and nephrocalcinosis. Nephrocalcinosis is a condition where calcium salts are deposited in renal tissue, and this may lead to critical kidney complications. This rare syndrome shows pathognomonic oral characteristics that are easily detectable at an early age, which proceeds the onset of renal involvement. Pediatric dentists are the first oral health practitioners whom ERS patients will meet at early age. The role of pediatric dentists is critically important for early diagnosis and referral of patients to both nephrologists for renal assessment and geneticists for identification of causative mutation and diagnosis. Early detection of renal involvement may provide chances to prevent further undesired renal complications.

• Restorative Dentistry and Endodontics
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• v.18 no.1
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• pp.122-132
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• 1993

The aim of this study was to investigate the level of radiopacity of glass ionomer cements and to determine the optimum level of radiopacity that is the most compatible with the radiographic diagnosis of secondary caries. The experiments were performed in two parts. In the first part, the radiopacities of 9 glass ionomer cements (FI, FII, FI-LC, FII-LC, SI, SII, Vit, B-VLC, AC) and base materials(Ultra-Blend, Zinc phoaphate cements, Cavitec, Dycal) were measured by densitometer. Then all experimental materials were divided into 5 groups based on the level of radiopacity of enamel and dentin. In the second part, class III cavities with or without secondary caries were prepared in extracted anterior teeth. The representative materials of each group with different radiopacities were inserted into each cavity. The radiographs were interpreted by 15 dentists and seconsary caries were diagnosed according to a five-point confidence rating. Sensitivity and ROC analysis were used to compare observer performance. The following results were obtained : 1. The radipacity of glass ionomer cements varied between 1.111mm Al and 6.011mm Al equivalent. 2. Among experimental materials, three materials in group I had lower radiopacity than that of dentin. The radiopacity of two materials in group II slightly exeeded that of dentin. Three materials in group III had slightly lower radiopacity than that on enamel. The radiopacity of one material in group W was slightly higher than that of enamel. Four materals in group V had the radiopacity that exeeded over 2.0mm AI equivalent to that of enamel. 3. The group IV was the highest for sensitivity and the group V was the highest for ROC area. However, no significant differences were obtained among group II, III, IV and V (P<0.05) but only group I was significantly lower(P<0.01). 4. In comparison with the observer performance for the radiographic diagnosis of secondary caries, the group II, III, IV, and V were superior to the group I (P<0.01). And so the optimum level of radiopacity to detect the secondary caries was the radiopacity that is higher than that of dentin.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• Journal of Embryo Transfer
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• v.33 no.3
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• pp.149-157
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• 2018

The objective of this study was to evaluate the effect of treating dairy cattle with exogenous human chorionic gonadotrophin (hCG), five (5) days post artificial insemination (AI) on serum progesterone (P4) concentration and pregnancy rate. In this experiment, five days after AI, cows were assigned randomly to two groups namely: a) treated group (67) which were administrered with 1500 IU hCG (Chorulon) and b) control group (61), which received no treatment. On day 5, 10, 15 and 20 after the artificial insemination, blood samples from a total of 8 cows (4 from each group) were collected and were analyzed for serum P4 concentration. Cows were detected for estrus according to standing heat by visual observation. Cows that were detected still in estrus after days 18-24 were re-inseminated and recorded as not pregnant (open). Pregnancy diagnosis was conducted by ultrasonographic examination and transrectal palpation of the uterus on approximately 60 days in cows that observed to be not in estrus. The conception rate in hCG treated and control groups were 52.5 and 36.1%, respectively. The results proved that there were no significant differences in conception rate between two groups (p=0.0568). However, pregnancy rates were reduced by hCG treatment. Average serum P4 concentrations did not differ between Hcg-treated and control groups on day 5 (0.377 versus 0.375 ng/ml). On day 20 serum P4 concentrations were greater in the treated group compared with the control group (3.085 versus 2.010 ng/ml). The treatment with hCG seemed to increase P4 level compared with the control. In conclusion, the results of this study showed that 1500 IU of hCG administered on 5 day post AI increased conception rate in dairy cows. This was supported by the results on serum P4 concentration which was greater in hCG treated group.

• Parasites, Hosts and Diseases
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• v.55 no.1
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• pp.55-60
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• 2017

Fascioliasis is a foodborne zoonotic parasitic disease. We report 4 cases occurring in the same family, in whom diagnosis of acute fascioliasis was established after series of tests. One case was hospitalized with fever, eosinophilia, and hepatic lesions. MRI showed hypodense changes in both liver lobes. The remaining 3 cases presented with the symptom of stomachache only. Stool analysis was positive for Fasciola eggs in 2 adult patients. The immunological test and molecular identification of eggs were confirmed at the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China. The results of serological detection were positive in all the 4 patients. DNA sequencing of PCR products of the eggs demonstrated 100% homology with ITS and cox1 of Fasciola hepatica. The conditions of the patients were not improved by broad-spectrum anti-parasitic drugs until administration of triclabendazole.

• Journal of radiological science and technology
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• v.43 no.5
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• pp.397-404
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• 2020

Coronavirus disease(COVID-19) is highly infectious disease that directly affects the lungs. To observe the clinical findings from these lungs, the Chest Radiography(CXR) can be used in a fast manner. However, the diagnostic performance via CXR needs to be improved, since the identifying these findings are highly time-consuming and prone to human error. Therefore, Artificial Intelligence(AI) based tool may be useful to aid the diagnosis of COVID-19 via CXR. In this study, we explored various Deep learning(DL) approach to classify COVID-19, other viral pneumonia and normal. For the original dataset and lung-segmented dataset, the pre-trained AlexNet, SqueezeNet, ResNet18, DenseNet201 were transfer-trained and validated for 3 class - COVID-19, viral pneumonia, normal. In the results, AlexNet showed the highest mean accuracy of 99.15±2.69% and fastest training time of 1.61±0.56 min among 4 pre-trained neural networks. In this study, we demonstrated the performance of 4 pre-trained neural networks in COVID-19 diagnosis with CXR images. Further, we plotted the class activation map(CAM) of each network and demonstrated that the lung-segmentation pre-processing improve the performance of COVID-19 classifier with CXR images by excluding background features.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.57-69
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• 2001

Expert system is one of AI area. It simulates the human's way of thinking to give solutions of problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depend on the control of efficiency of inference engine. Inference engine has to get features; first, if possible to minimize restrictions when it constructed the knowledge base. second, it has to serve various kinds of inferencing methods. In this paper we propose knowledge scheme for representing domain knowledge in ease, knowledge implementation technique for inferencing, and integrated knowledge-base engine with blackboard and inference engine. And we describe a expert system prototype that implemented in this paper using proposed methods, it perform diagnose about heavy industrial device. The fault diagnosis system prototype has been studied in this paper will be practical foundation in the research area of knowledge based system.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.19-25
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• 2024

The worldwide aging population trend is causing an increase in the incidence of major retinal diseases that can lead to blindness, including glaucoma, cataract, and macular degeneration. In the field of ophthalmology, there is a focused interest in diagnosing diseases that are difficult to prevent in order to reduce the rate of blindness. This study proposes a deep learning approach to accurately diagnose ocular diseases in fundus photographs using less data than traditional methods. For this, Convolutional Neural Network (CNN) models capable of effective learning with limited data were selected to classify Conventional Fundus Images (CFI) from various ocular disease patients. The chosen CNN models demonstrated exceptional performance, achieving high Accuracy, Precision, Recall, and F1-score values. This approach reduces manual analysis by ophthalmologists, shortens consultation times, and provides consistent diagnostic results, making it an efficient and accurate diagnostic tool in the medical field.