• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.164-169
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• 2024

The history of animals raised by humans began in prehistoric times, and in modern times they were classified as livestock and pets. As social awareness changes, the term 'companion animal' is used instead of 'pet', and related content has also become more diverse. Recently, digital contents such as virtual pet training, memorial space, and AI health diagnosis using metaverse and AI technology are developing. Developed digital content makes pet care convenient and provide emotional support and economic benefits to users. As technology develops and content becomes more diverse, the relationship between pets and humans will become closer in the future, and related laws and ethical guidelines will need to be established.

• The Journal of Bigdata
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• v.6 no.1
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• pp.37-49
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• 2021

The number of dementia patients in Korea is estimated to be over 800,000, and the severity of dementia is becoming a social problem. However, no treatment or drug has yet been developed to cure dementia worldwide. The number of dementia patients is expected to increase further due to the rapid aging of the population. Currently, early detection of dementia and delaying the course of dementia symptoms is the best alternative. This study presented a methodology for early diagnosis of dementia by measuring and analyzing amyloid plaques. This vital protein can most clearly and early diagnose dementia in the retina through AI-based image analysis. We performed binary classification and multi-classification learning based on CNN on retina data. We also developed a deep learning algorithm that can diagnose dementia early based on pre-processed retinal data. Accuracy and recall of the deep learning model were verified, and as a result of the verification, and derived results that satisfy both recall and accuracy. In the future, we plan to continue the study based on clinical data of actual dementia patients, and the results of this study are expected to solve the dementia problem.

• Journal of Drive and Control
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• v.20 no.4
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• Annual Conference of KIPS
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• 2024.10a
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• pp.507-510
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• 2024

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive function. Accurate and early diagnosis of AD is crucial for effective management and treatment. Traditional machine learning models, though commonly applied, often fall short in capturing the intricate relationships between diverse tabular data. Furthermore, the missing data issue, typically addressed using conventional imputation techniques, leads to reduced accuracy and generalizability of AD classification models. This paper introduces ADxClass, a novel deep learning framework that enhances AD classification by leveraging multi-domain attention fusion and data type-based imputation techniques for handling missing heterogeneous tabular data. ADxClass integrates data from various domains, including demographic, cognitive, genetic, and biomarkers obtained from neuroimaging measurements, to improve the robustness and accuracy of AD classification models. The model's efficiency is validated via a 5-fold cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, showing significant improvements in classification performance compared to traditional machine learning approaches.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.3
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• pp.299-302
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• 1996

Milk samples(n = 78) were taken 19d, 20d, 21d, 22d after artificial insemination(AI) for early pregnancy diagnosis by using the Enzyme immunoassay(EIA) kit. The progesterone ($P_4$) concentration in the whole milk was measured on the same day of pregnancy diagnosis. Rectal palpation(RP) was accomplished between 60d and 70d after AI to estimate the ovary condition and pregnancy status. Milk progesterone concentrations measured by Radio-immunoassay(RIA) method, in the pregnant cows at 17d, 19d, 21d after insemination were $17.10{\pm}0.91$, $17.60{\pm}0.46$, and $18.43{\pm}0.79nmol/l$, whereas those in the not-pregnant cows were $6.57{\pm}1.03$, $2.63{\pm}0.29$, and $0.67{\pm}0.08nmol/l$, respectively. When the progesterone concentration was less than 7 nmol/l, the color of the EIA kit was lighter and when the progesterone concentration was ${\geq}16nmol/l$, the color of the EIA kit was darker compared to the standard color. The detection rates of error by judging the color differences were 5.1% and 20.7%, respectively. In the early pregnancy diagnosis by the EIA kit and RIA method, the accuracy rates in the pregnancy of cows were 82% and 87%, and those in not-pregnant cows were 86% and 91%, respectively. For ovarian status estimated by the RIA method and certified by RP, the accuracy rates of the ovarian atrophy, follicular cyst and luteal cyst were 80, 91 and 83% and the progesterone concentrations were 2.51, 2.03, and 26.7 nmol/l, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• Parasites, Hosts and Diseases
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• v.50 no.2
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• pp.99-102
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• 2012

Serum samples, 100 in the total number, were collected from different laboratories in Tehran, Iran and tested for anti-Toxoplasma specific IgG and IgM antibodies using indirect immunofluorescent antibody test (IFAT). Using the IgG (chronic) and IgM (acute) positive samples, the IgG avidity test was performed by ELISA in duplicate rows of 96-well microtiter plates. One row was washed with 6 M urea and the other with PBS (pH 7.2), then the avidity index (AI) was calculated. Sixteen out of 18 (88.9%) sera with acute toxoplasmosis showed low avidity levels ($AI{\leq}50$), and 76 out of 82 (92.7%) sera in chronic phase of infection showed high avidity index (AI>60). Six sera had borderline ranges of AI. The results showed that the IgG avidity test by ELISA could distinguish the acute and chronic stages of toxoplasmosis in humans.

• Journal of Chest Surgery
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• v.26 no.11
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• pp.821-826
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• 1993

Between January 1983 and December 1992, we had experienced 79 patients of ventricular septal defect [ VSD ] associated with aortic insufficiency [AI] which constitute 4.6 % of total numbers of VSD. The mean age of the patients was 10.2 years with a range of 1 to 35 years and the average degree of aortic insufficiency classified by Sellers was 2.1. The type of VSD was subpulmonic in 57 patients and perimembranous in 22. Most common pathologic finding causing AI was prolapse of right coronary cusp [ 54 cases ; 71.4% ] ,followed by prolapse of both right and non-coronary cusp[ 12 cases ; 7.9% ]. VSD closure alone was performed in 51 patients and their mean age was 7.7 years [ ranged 1 to 13 years ]. VSD closure and aortic valve reconstruction was performed in 22 patients, VSD closure and aortic valve replacement in 6 patients, and the mean age of the patients was 14.5 years [ ranged 2 to 28 years ], 20.4 years [ ranged 18 to 35 years ] respectively. There was no hospital mortality. All patients were followed up from 1 month to 9 year 4 months [average; 21.4 months ] and there was one late death. Our data suggests that, early closure of VSD without any manipulation on the valve may be sufficient procedure to improve or at least withhold progression of AI in children and furthermore patients with VSD associated AI should be corrected promptly after diagnosis.