• BLOOD RESEARCH
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• v.53 no.4
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• pp.299-306
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• 2018

Background IgG-mediated anaphylaxis occurs after infusion of certain monoclonal antibody-based therapeutics. New in vitro tests are urgently needed to diagnose such reactions. We investigated whether allergens trigger neutrophil oxidative burst (OB) and if neutrophil OB occurs due to allergen-specific IgG (sIgG). Methods Neutrophil OB was measured by dihydrorhodamine 123 flow cytometry using a leukocyte suspension spiked with a very small patch of the allergen crude extract, Dermatophagoides farinae (Der f). The mean fluorescence intensity ratio of stimulated to unstimulated samples was calculated as the neutrophil oxidative index (NOI). Results The Der f-specific NOI (Der f-sNOI) showed a time-dependent increase after Der f extract addition. At 15 min activation, higher Der f-sIgG levels were associated with lower Der f-sNOI values in 31 subjects (P<0.05). This inverse relationship occurs due to the initial blocking effect of free Der f-sIgG. Additionally, neutrophil OB was nearly absent (Der f-sNOI of -1) in two cases: a subject with undetectable Der f-sIgG levels and washed leukocyte suspensions deprived of Der f-sIgG. Conclusion Allergens can trigger neutrophil OB via preexisting allergen-sIgG. Neutrophil OB can be easily measured in a leukocyte suspension spiked with the allergen. This assay can be used to diagnose IgG-mediated anaphylaxis.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Journal of the Korean Institute of Rural Architecture
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• v.22 no.4
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• pp.79-86
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• 2020

In Korea, the Basic Act is guaranteed through the "Act on the Guarantee of Convenience Promotion for Disabled Persons, the Elderly, Pregnant Women, etc." and various ordinances. In order to improve this situation, it is necessary to introduce Universal Design (UD). By applying this where it is most needed, access to cultural properties is enhanced to promote multiple rights. Currently, the region with the largest population of the elderly in Korea is Gyeonggi-do, but the region with the highest proportion of the elderly is Jeolla-do. However, the Jeolla-do area is lagging behind in the revision of UD regulations or guidelines. Taking this into consideration and introducing it to each facility will also help to achieve balanced national development. In order to establish and apply effective universal design-related policies, it is necessary to diagnose the aspects of social change that affect our lives. In this study, the need for UD should be expanded as a basis for expanding social activities of socially disadvantaged people in Jeollabuk-do. Its goal is to diagnose the current status of UD and to suggest directions for application of improvements.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.19-24
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• 2022

Through the development and dissemination of embedded devices, studies that may help patients are rapidly emerging. Recently, as wearable devices have become one of the ways to diagnose diseases in daily life, they are being studied as a way to assist severely ill patients to lead their daily lives. Among them, a method of detecting and giving signals to detect and solve symptoms using acceleration sensors to diagnose Parkinson's disease is being studied, and there is no study to measure and analyze various factors that can affect Parkinson's disease. To solve them, we designed and developed a wearable device, P-Band, with various sensors capable of diagnosing related symptoms, including acceleration sensors capable of diagnosing Parkinson's disease. In this paper, the overall structure of the P-Band and the description and operation method of the measurable sensors are presented. In addition, it was confirmed that the symptoms of Parkinson's patients could be determined complexly through the results measured in actual patients.

• Journal of IKEEE
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• v.25 no.4
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• pp.672-678
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• 2021

New malware continues to increase and become advanced by every year. Although various studies are going on executable files to diagnose malicious codes, it is difficult to detect attacks that internalize malicious code threats in emails by exploiting non-executable document files, malicious URLs, and malicious macros and JS in documents. In this paper, we introduce a method of analyzing malicious code for email security through proactive detection and blocking of malicious email attacks, and propose a method for determining whether a non-executable document file is malicious based on AI. Among various algorithms, an efficient machine learning modeling is choosed, and an ML workflow system to diagnose malicious code using Kubeflow is proposed.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• Archives of Craniofacial Surgery
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• v.24 no.2
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• pp.73-77
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• 2023

Hidradenitis suppurativa (HS) is a chronic inflammatory condition that is difficult to diagnose, with a period of 10.0±9.6 years from symptom onset to diagnosis. A 32-year-old Asian man presented with bilateral postauricular abscesses that first appeared 5 years previously. Despite several incisions and drainage, the symptoms only temporarily improved and continued to recur. On physical examination, chronic scars and sinus tracts were observed around the lesion. Postauricular HS was diagnosed, and surgical treatment was performed. We performed a wide excision and reconstructed the defect using a posterior auricular artery perforator-based keystone flap. Histological examination confirmed the diagnosis of HS. The reconstruction was successful, and there was no recurrence for 2 years after surgery. HS is difficult to diagnose without specific attention. Although the postauricular region is not a typical site of HS, it can occur in this area. Therefore, if a patient presents with recurrent abscesses in the postauricular region, HS should be considered. Additionally, if HS is diagnosed in the postauricular region, wide excision with reconstruction using a posterior auricular artery perforator-based keystone flap can lead to a favorable outcome.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.121-138
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• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.12 no.2
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• pp.107-126
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• 2008

Background : There was seldom study about method that diagnose 8 Constitution beside method of pulse diagnosis in 8 Constitution Medicine. Objectives : This study is to make out 8 Constitution Medicine Diagnosis Expert System Development used CBR(Case based Reasoning). Methods : First, at case base construction process we constructed case base for CBR embodiment because gathering 925 cases all to patient who constitution is verified, and second, at study model establishment process superior expert system development by purpose CBR of reasoning process dividing fundamental type CBR that spend basis data value and expert type CBR that reflect weight in basis data value accordin I II III to advice expert opinion, and third, system embodiment process explained about way to give process and weight that diagnose constitution through Nearest Neighbor Method sampling process of CBR techniques, and fourth, at system estimation process we selected superior CBR model because comparing and estimate the diagnosis rate of expert system with fundamental type system (GECBR) model and expert type I II III CBR system (AVCBR, AACBR, AGCBR) model that reflect expert opinion in fundamental type system. GECBR and AGCBR chose on superior study model. Through such 4 study process, we developed 8 constitution diagnosis expert system lastly. Results : 1. When we select GECBR that is fundamental type by reasoning system, diagnosis rate 78.91% of 8 constitution diagnosis expert system is expected, and the constitution diagnosis rate Hepatonia 90.4%, Cholecystonia 63.0%, Pancreotonia 91.1%, Gastrotonia 0%, Pulmotonia 71.2%, Colonotonia 74.4%, Renotonia 37.5%, Vesicotonia 67.1% expect. 2. When we select AGCBR that is expert type III by reasoning system, diagnosis rate 77.51% of 8 constitution diagnosis expert system is expected, and the constitution diagnosis rate Hepatonia 93.4%, Cholecystonia 58.5%, Pancreotonia 91.1%, Gastrotonia 0%, Pulmotonia 73.1%, Colonotonia 64.4%, Renotonia 41.7%, Vesicotonia 72.2% expect. Conclusion : Based on this study, 8 constitution diagnosis expert system may give help to diagnose 8 constitution, and it is going to utilize as objective estimation tool of 8 constitution diagnosis, and further study for 8 Constitution Medicine Diagnosis Expert System Development used CBR(Case based Reasoning) is needed to supplement this study.