• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.45-46
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• 2018

This paper focuses on human activities monitoring problem using onboard smartphone sensors as data generator. Monitoring such activities can be very important to detect anomalies and prevent disease from patients. Machine learning (ML) algorithms appear to be ideal approaches to use for processing data from smartphone to get sense of how to classify human activities. ML algorithms depend on quality, the quantity and even more important, the properties or features, that can be learnt from data. This paper proposes a mobile virtual reality visualization system that helps to view data representation in a very immersive way so that its quality and discriminative characteristics may be evaluated and improved. The proposed system comes as well with a handy data collecting application that can be accessed directly by the VR visualization part.

• ETRI Journal
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• v.43 no.3
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.539-541
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• 2023

COVID-19 is a viral pandemic disease that spreads widely all around the world. The only way to identify COVID-19 patients at an early stage is to stop the spread of the virus. Different approaches are used to diagnose, such as RT-PCR, Chest X-rays, and CT images. However, these are time-consuming and require a specialized lab. Therefore, there is a need to develop a time-efficient diagnosis method to detect COVID-19 patients. The proposed machine learning (ML) approach predicts the presence of coronavirus based on clinical symptoms. The clinical dataset is collected from the Israeli Ministry of Health. We used different ML classifiers (i.e., XGB, DT, RF, and NB) to diagnose COVID-19. Later, classifiers are optimized with the Bayesian hyperparameter optimization approach to improve the performance. The optimized RF outperformed the others and achieved an accuracy of 97.62% on the testing data that help the early diagnosis of COVID-19 patients.

• Journal of Microbiology and Biotechnology
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• v.15 no.6
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• pp.1353-1358
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• 2005

A rapid and quantitative real-time PCR was developed to target the invasion A (invA) gene of Salmonella spp. We developed quantitative standard curves based on plasmids containing the invA gene. Based on these curves, we detected Salmonella spp. in artificially contaminated buffered peptone water (BPW) and milk samples. We were able to determine the invA gene copy number per ml of food samples, with the minimum detection limit of $4.1{\times}10^{3}$ copies/ml of BPW and $3.3{\times}10^{3}$ copies/ml of milk. When applied directly to detect and quantify Salmonella spp. in BPW and milk, the present real-time PCR assay was as sensitive as the plate count method; however, copy numbers were one to two logs higher than the colony-forming units obtained by the plate count methods. In the present work, the real-time PCR assay was shown to significantly reduce the total time necessary for the detection of Salmonella spp. in foods and to provide an important model for other foodborne pathogens.

• The Journal of the Acoustical Society of Korea
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• v.24 no.8
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• pp.447-453
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• 2005

Laboratory measurements were performed in a uni-algae medium Cochlodinium polykrikoides (Phytoplankton, dinoflagellates) using an Underwater Ultrasound $(5\~15\;MHz)$ to study Characteristics of Acoustic Backscattering of Harmful algae. In an effort to detect the harmful algal scatterers with population density of less than 300 cells/ml that corresponds to the precaution stage of red tide, backscattered signals from various scatterer-density samples were obtained and analyzed. Correlations between volume backscattering strength (Sv) and population density (cells/ml) of scatterers in the medium have been investigated. Comparison of Volume Backscattering Strengths calculated with the fluid-sphere model [1] and the measured values showed an agreement.

• Korean Journal of Veterinary Research
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• v.18 no.1
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• pp.1-7
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• 1978

In order to detect the clinical effect of combelen which is used for sedation of domestic animals, 10 heads of clinically healthy Korean native goats were used in this study. They were divided into two groups; one is dose level of 1ml per 10 kg of body weight with 1% combelen and the other is dose level of 3 ml. Clinical observations and changes in blood components after administration of combelen were made. 1. There was no adverse effect due to combelen, but sedative effect was insufficient. 2. During sedative period the changes in heart rate and respiratory rate showed noticeable change, and body temperature was slightly decreased. 3. In ECG recordings, except for slight changes in T wave, significant change was not observed. 4. Erythrocytes, leukocytes, hemoglobin concentration and packed cell volume showed tendency to decrease during the period of sedation. 5. SGOT activity showed a remarkable increase and BUN showed a great decrease 24 hours after administration in the group of 3ml/10kg. Blood glucose level increased during the period of sedation in both groups.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.155-162
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• 2022

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.

• Radiation Oncology Journal
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• v.17 no.2
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• pp.120-129
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• 1999

Purpose : Serum squamous cell (SCC) antigen levels were examined in uterine cervix cancer undergoing radiation therapy, and authors analyzed the relationship between SCC antigen levels and treatment results. Materials and Methods :This is a retrospective study of 181 conical carcinoma patients who received radiotherapy and examined serial serum SCC antigen from 1991 to 1997 at Soonchunhyang University Hospital. One hundred and eighteen patients underwent SCC antigen evaluation at diagnosis The relationship between the serum tumor marker level and disease free survival, recurrence pattern, and other prognostic factors were analyzed according to various statistical methods. Results : The Positivity rate (initial serum value above 2.5 ng/ml) was increased with FIGO stage (IB-IIA 57% to IV 91%) and more discriminative than cutoff value of 1.5 ng/ml. Five year disease free survival rates for the stage IB-IIA, IIB, III and IV were 79.2%, 68.7%, 33.4% and 0%, respectively. The 5-year disease free survival rate for patients with serum SCC antigen levels above 5.0 ng/ml was 34% versus 55~62% for patients with normal range (>1.5 ng/ml) or mildly elevated levels (1.5~5.0 ng/ml). Rising SCC antigen levels preceded the clinical detection of disease by a mean of 4.8 months (range 1 ~13 months). Negative linear correlation was observed between initial SCC antigen levels and relapse free survival (r=-0.226), and by multivariate analysis, initial SCC antigen level had a large impact on the relapse free survival. Conclusion : SCC antigen assay is a useful aid to predict the prognosis of squamous cell carcinoma of the uterine cervix and to detect recurrence.

• Journal of Veterinary Clinics
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• v.27 no.3
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• pp.284-288
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• 2010

The aim of this report is to compare quantitatively computed radiography (CR) and screen-film radiography (SFR) in the detection of peritoneal effusion in dogs. Normal four beagle dogs and one Maltese dog were used. Each five CR and SFR abdominal images of right lateral and ventro-dorsal position were obtained after lodge of 6 ml, 8 ml, 12 ml, 15 ml, and 18 ml of normal saline by intraperitoneal injection within the abdomen. The reviewers were asked to evaluate each SFR and CR images for the presence of peritoneal effusion using the score by the presence of a peritoneal effusion on a five-point ordinal scale. A receiver operating curve (ROC) analysis compared the two imaging modalities. The present study showed that there was no statistical difference between SFR and CR in the detecting peritoneal effusion, but CR was relatively more sensitive based on the increased area under its ROC analysis. Moreover, Readers were more likely to detect peritoneal effusion on CR images than SFR.

• Speech Sciences
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• v.10 no.2
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• pp.77-84
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• 2003

In this research, we seek the beginning of the speech and detect the stationary speech region using lip information. Performing running average of the estimated speech signal in the stationary region, we reduce the effect of musical noise which is inherent to the conventional MlMSE (Minimum Mean Square Error) speech enhancement algorithm. In addition to it, SFM (Spectral Flatness Measure) is incorporated to reduce the speech signal estimation error due to speaking habit and some lacking lip information. The proposed algorithm with Wiener filtering shows the superior performance to the conventional methods according to MOS (Mean Opinion Score) test.