• Advanced Industrial SCIence
• /
• v.1 no.1
• /
• pp.7-15
• /
• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Journal of Life Science
• /
• v.33 no.1
• /
• pp.25-33
• /
• 2023

The fermentation process of kimchi, which is a traditional Korean food, influences the resulting compo- sition of microorganisms, such as the genera Leuconostoc, Weissella, and Lactobacillus. In addition, several factors, including the type of kimchi, fermentation conditions, materials, and ingredients, can influence the distribution of the kimchi microbial community. In this study, next-generation sequencing (NGS) of kimchi samples obtained from central (Gangwon-do and Gyeonggi-do) and southern (Jeolla-do and Gyeongsang-do) regions in Korea was performed, and the microbial communities in samples from the two regions were compared. Good's coverage prediction for all samples was higher than 99%, indicating that there was sufficient reliability for comparative analysis. However, in a α -diversity analysis, there was no significant difference in species richness and diversity between samples. The Firmicutes phylum was common in both regions. At the species level, Weissella kandleri dominated in central (46.5%) and southern (30.8%) regions. Linear discriminant analysis effect size (LEfSe) analysis was performed to identify biomarkers representing the microbial community in each region. The LEfSe results pointed to statistically significant differences between the two regions in community composition, with Leuconostocaceae (71.4%) dominating in the central region and Lactobacillaceae (61.0%) dominating in the southern region. Based on these results, it can be concluded that the microbial communities of kimchi are significantly influenced by regional properties and that it can provide more useful scientific data to study the relationship between regional characteristics of kimchi and their microbial distribution.

• Journal of Gastric Cancer
• /
• v.23 no.2
• /
• pp.355-364
• /
• 2023

Background: There are no clear guidelines to determine whether to perform D1 or D1+ lymph node dissection in early gastric cancer (EGC). This study aimed to develop a nomogram for estimating the risk of extraperigastric lymph node metastasis (LNM). Materials and Methods: Between 2009 and 2019, a total of 4,482 patients with pathologically confirmed T1 disease at 6 affiliated hospitals were included in this study. The basic clinicopathological characteristics of the positive and negative extraperigastric LNM groups were compared. The possible risk factors were evaluated using univariate and multivariate analyses. Based on these results, a risk prediction model was developed. A nomogram predicting extraperigastric LNM was used for internal validation. Results: Multivariate analyses showed that tumor size (cut-off value 3.0 cm, odds ratio [OR]=1.886, P=0.030), tumor depth (OR=1.853 for tumors with sm2 and sm3 invasion, P=0.010), cross-sectional location (OR=0.490 for tumors located on the greater curvature, P=0.0303), differentiation (OR=0.584 for differentiated tumors, P=0.0070), and lymphovascular invasion (OR=11.125, P<0.001) are possible risk factors for extraperigastric LNM. An equation for estimating the risk of extraperigastric LNM was derived from these risk factors. The equation was internally validated by comparing the actual metastatic rate with the predicted rate, which showed good agreement. Conclusions: A nomogram for estimating the risk of extraperigastric LNM in EGC was successfully developed. Although there are some limitations to applying this model because it was developed based on pathological data, it can be optimally adapted for patients who require curative gastrectomy after endoscopic submucosal dissection.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.30-30
• /
• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.4
• /
• pp.587-594
• /
• 2023

In this paper, we present the design and optimization process of an on-body microstrip patch antenna with a paired T-type defect for monitoring fracture recovery of human legs. This antenna is designed to be light, thin and compact despite the improvement of return loss and bandwidth performance by adjusting the size of the T-type defect. The structure around the applied human leg is structured as a 5-layer dielectric plane, and the complex dielectric constant of each layer is calculated using the 4-pole Cole-Cole model parameters. In a normal case without bone fracture, the return loss of the on-body antenna is -66.71dB at 4.0196GHz, and the return loss difference ΔS₁₁ is 37.95dB when the gallus layer have a length of 10.0mm, width of 1.0mme, and height of 2.0mm. A 3'rd degree polynomial is presented to predict the height of the gallus layer for the change in return loss, and the polynomial has a very high prediction suitability as RSS = 1.4751, R² = 0.9988246, P-value = 0.0001841.

• Journal of Korea Water Resources Association
• /
• v.56 no.3
• /
• pp.185-194
• /
• 2023

There are more than 22,300 small streams distributed nationwide in Korea, and they have various runoff characteristics depending on basin area, topography and land use. For small stream disaster management, establishing detailed design standards suitable for the small streams is important, but most of the formulas currently proposed in the small stream design standard are based on the river design standard aimed at national and local rivers or foreign river design standards. The design-width is an important factor in determining the size of the stream. It is determined by using design-flood discharges or more variables such as design-flood discharges, basin area, slop, etc in the small stream design standard. This study collected various characteristics information such as the design-flood discharges, basin area, river length and river slop, and design-width values from 4,073 small streams distributed in 12 cities and provinces in Korea to suggest the appropriated design-width formula. This study developed two design-width formulas by using the regression analysis which one is using the design-flood discharges and the other is using various variables such as the design-flood discharges, basin area, river length and river slope collected from the small steams. It is expected that both equations developed in here can be used for small stream disaster management, such as improving small stream design standard or establishing a comprehensive small stream maintenance plan.

• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.85-97
• /
• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of the Korean Society of Radiology
• /
• v.81 no.5
• /
• pp.1164-1174
• /
• 2020

Purpose To evaluate a deep learning model to predict recurrence of thyroid tumor using preoperative ultrasonography (US). Materials and Methods We included representative images from 229 US-based patients (male:female = 42:187; mean age, 49.6 years) who had been diagnosed with thyroid cancer on preoperative US and subsequently underwent thyroid surgery. After selecting each representative transverse or longitudinal US image, we created a data set from the resulting database of 898 images after augmentation. The Python 2.7.6 and Keras 2.1.5 framework for neural networks were used for deep learning with a convolutional neural network. We compared the clinical and histological features between patients with and without recurrence. The predictive performance of the deep learning model between groups was evaluated using receiver operating characteristic (ROC) analysis, and the area under the ROC curve served as a summary of the prognostic performance of the deep learning model to predict recurrent thyroid cancer. Results Tumor recurrence was noted in 49 (21.4%) among the 229 patients. Tumor size and multifocality varied significantly between the groups with and without recurrence (p < 0.05). The overall mean area under the curve (AUC) value of the deep learning model for prediction of recurrent thyroid cancer was 0.9 ± 0.06. The mean AUC value was 0.87 ± 0.03 in macrocarcinoma and 0.79 ± 0.16 in microcarcinoma. Conclusion A deep learning model for analysis of US images of thyroid cancer showed the possibility of predicting recurrence of thyroid cancer.

• Journal of Radiation Protection and Research
• /
• v.8 no.1
• /
• pp.1-14
• /
• 1983

For the purpose of prediction and protection of radiation effects on the Korean people, we have collected autoptic data from the National Institute of Scientific Investigation. The mass of organs and the size of brains measured is analysed for 1,921 Korean people. The results obtained are as follows; 1. The weight of the kidney in the Reference Korean is 251.6g in male and 227.7g in female. 2. The weight of the lung in the Reference Korean is 1,204.4g in male and 957.4g in female. 3. The weight of the heart in the Reference Korean is 348.8g in male and 301.6g in female. 4. The weight of the liver in the Reference Korean is 1,863.9g in male and 1,610.9g in female. 5. The weight of the pancreas in the Reference Korean is 56.4g in male and 54.0g in female. ,6. The weight of the spleen in the Reference Korean is 67.3g in male and 58.2g in female. 7. The anteroposterior diameter, transverse diameter and vertical diameter of the brain in the Reference Korean are 17.6cm, 15.5cm and 8.6cm in male, respectively, and 17.7cm, 15.4cm and 8.2cm in female, respectively.