• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.43 no.3
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• pp.204-211
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• 2017

In dentistry, elevator instruments are used to luxate teeth, and this technique imparts forces to tooth particles that sever the periodontal ligament around tooth roots inside the socket and expand alveolar bone around tooth particles. These effects can result in extraction of the tooth particles or facilitate systematic forceps extraction of the tooth particles. This article presents basic oral surgery techniques for applying elevators to luxate teeth. Determination of the optimal luxation technique requires understanding of the functions of the straight elevator and the Cryer elevator, the concept of purchase points, how the design elements of elevator working ends and tips influence the functionality of an elevator, application of forces to tooth particles, sectioning teeth at furcations, and bone removal to facilitate luxation. The effectiveness of tooth particle luxation is influenced by elevator tip shape and size, the magnitude and the vectors of forces applied to the tooth particle by the tip, and sectioning and bone removal within the operating field. Controlled extraction procedures are facilitated by a dental operating microscope or the magnification of binocular surgical loupes telescopes, combined with co-axial illumination.

• ETRI Journal
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• v.38 no.1
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• pp.174-184
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• 2016

Information identification with image data by means of low-level visual features has evolved as a challenging research domain. Conventional text-based mapping of image data has been gradually replaced by content-based techniques of image identification. Feature extraction from image content plays a crucial role in facilitating content-based detection processes. In this paper, the authors have proposed four different techniques for multiview feature extraction from images. The efficiency of extracted feature vectors for content-based image classification and retrieval is evaluated by means of fusion-based and data standardization-based techniques. It is observed that the latter surpasses the former. The proposed methods outclass state-of-the-art techniques for content-based image identification and show an average increase in precision of 17.71% and 22.78% for classification and retrieval, respectively. Three public datasets - Wang; Oliva and Torralba (OT-Scene); and Corel - are used for verification purposes. The research findings are statistically validated by conducting a paired t-test.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• KSBB Journal
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• v.26 no.5
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• pp.453-458
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• 2011

In this study, two different extraction techniques, organic solvent extraction and supercritical carbon dioxide ($SCCO_2$) extraction, were employed to evaluate the extraction efficiency of oil from Chlorella vulgaris. In the organic solvent extraction, the effects of various organic solvent on the extraction yield were investigated. The $SCCO_2$ extraction was carried out while varying such operating parameters as temperature, pressure, $SCCO_2$ flow rate, and cosolvent. About 4.9 wt% of oil was extracted from ground Chrollera vulgaris for 18 h when dichloromethane/methanol (2：1, v/v) was used as an extraction solvent. The oil yield of the $SCCO_2$ extraction was found to be very low (0.53 wt%) and to increase up to about 0.86 wt% with the addition of cosolvent.

• Journal of Life Science
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• v.22 no.11
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• pp.1507-1514
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• 2012

Oxidative stress leads to damage in all components of the cell, including proteins, lipids, and DNA due to imbalance between reactive oxygen species production and cellular detoxification. Phytochemicals are well-known to contain antioxidants, and their physiological role has been intensively studied. The fruit of Cornus officianalis has been used in oriental medicine and has been reported to have many functions. In this study, four different extraction techniques were applied to extract functional components from the fruit of Cornus officianalis, and the content of loganin, which is an antioxidant having DPPH radical and hydrogen peroxide scavenging activity and reducing power, was analyzed in each extract. Extraction techniques employed in this study were heat extraction by water, 70% ethanol extraction, enzyme treatment, and combination of enzyme treatment and heat extraction by water. All extracts contained 11.8-18.0 mg/g loganin and showed antioxidation function assayed by measuring DPPH radical and hydrogen peroxide scavenging activity and reducing power. Among them, heat extraction was the most effective technique, showing a maximum amount of loganin (18.0 mg/g) and antioxidative activity at 100 mg/ml concentration. Each extract showed very low cytotoxicity up to at 500 mg/ml but 10-20% cytotoxicity at 1,000 mg/ml by in vitro MTT assay.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.2 no.2
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• pp.84-95
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• 2016

Considerably increasing interest in using the theranostic isotopes/ isotope pairs of radiometals like $^{44/47}Sc$ and $^{64/67}Cu$ for diagnosis and/or therapeutic applications in the nuclear medicine procedures necessitates its reliable production and supply. Separation and purification of no-carrier-added (NCA) isotopes from macro quantitates of the irradiated target matrix along with other impurities is a cardinal procedure amongst several other steps involved in its production. Multitudinous methods including but not limited to liquid-liquid (solvent) extraction, extraction chromatography (EXC), ion exchange, electrodeposition and sublimation are routinely applied either solitarily or in combination for the separation and purification of radioisotopes depending on their production routes, radioisotope of interest and impurities involved. However, application of EXC though has shown promises towards the numerous separation techniques have not received much attention as far as its application prospects in the field of nuclear medicine are concerned. Advances in the recent past for application of the EXC resins in separation and purification of the several medically important radioisotopes at ultra-high purity have shown promising behavior with respect to their operation simplicity, acidic and radiolytic stability, separation efficiencies and speedy procedures with the enhanced and excellent extraction abilities. In this mini review we will be talking about the recent developments in the application and the use of EXC techniques for the separation and purification of $^{44/47}Sc$ and $^{64/67}Cu$ for medical applications. Furthermore, we will also discuss the scientific and practical aspects of EXC in the view of separation of the NCA trace amount of radionuclides.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.227-230
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• 2004

Two extraction techniques, Ultrasonic and Microwave extraction method, were tested for the determination of arsenic in contaminated soil SRM (Montana Soil). The extraction mixture was prepared by mixing 1 M ortho-phosphoric acid and 0.1 M ascorbic acid. This extractant was known to preserve arsenic species. The appropriate extraction time was 10 min to 20 min and the recovery rate was about 80%. A coupled system, SPE-HG-ICP-AES, was used for the determination of inorganic arsenic species. The detection limit was around 2 $\mu\textrm{g}$/1 and the linearity of calibration curve was better than $R^2$=0.99.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.1087-1090
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• 2014

This paper describes an overview and research work related to web page segmentation. Over a period of time, various techniques have been used and proposed to extract meaningful information from web pages automatically. Due to voluminous amount of data this extraction demanded state of the art techniques that segment the web pages just like or close to humans. Motivation behind this is to facilitate applications that rely on the meaningful data acquired from multiple web pages. Information extraction, search engines, re-organized web display for small screen devices are few strong candidate areas where web page extraction has adequate potential and utility of usage.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.