• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6775-6778
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• 2013

Human papilloma virus (HPV) is one of the most common sexually transmitted agents and its infection is the most established cause of cervical cancer. Midwives play a key position in the implementation of cervical cancer. This descriptive study aimed to determine the level of knowledge concerning HPV and HPV vaccination among 268 midwifery students. Data were collected between November 15 and 30, 2011, through a self-reported questionnaire. The mean age of participants was $20.75{\pm}1.60$. Among all students, 44.4% had heard of HPV, while 40.4% had heard of HPV vaccinatiob. The relationship between the midwifery student knowledge on HPV and HPV vaccine and their current educational year was significant (p=0.001). In conclusion midwifery students have moderate level of knowledge about HPV and its vaccine and relevant information should be included in their teaching curriculum.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.4
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• pp.1581-1585
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• 2015

Human papillomaviruses (HPVs) remain a serious world health problem due to their association with cervical and head and neck cancers. While over 100 HPV types have been identified, only a few subtypes are associated with malignancies. HPV 16 and 18 are the most prevalent oncogenic types in head and neck cancers. Although it has been proven that some subsets of benign and malignant head and neck lesions are associated with HPV, the general population have very little awareness and knowledge of their association with HPV. Therefore, the purpose of this study was to determine the knowledge of HPV and its links with head and neck benign and malignant lesions in a group of Pakistani dental patients who attended the Dental Department of the Sandeman provincial hospital in Quetta, Pakistan. One hundred and ninety-two patients were recruited and requested to answer a questionnaire. It was revealed that there was a low level of knowledge about HPV and its association with head and neck benign and malignant lesions among the participants. This result suggested that more education regarding the relationship of HPV in inducing head and neck benign and malignant lesions is required in this group of patients.

• Korean Journal of Human Ecology
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• v.17 no.6
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• pp.1151-1165
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• 2008

This study examined the current status of consumer competency such as consumer knowledge, consumer role attitude, and consumer skill and the related factors among female marriage immigrants. The data were collected from 220 female marriage immigrants living in Yeungnam region. The results of this study were as follows. First, the mean for consumer knowledge was 5.555, the mean for consumer role attitude was 4.067, and the mean for consumer skill was 3.841. Second, the results of t-tests and ANOVA showed that there were differences in the category of consumer knowledge according to age, educational level, marriage duration period, and Internet contact frequency. And the differences were found in age, educational level. communication with family about consumption and TV contact frequency for consumer role attitude, whereas in age, educational level, marriage duration period, employment status, communication with family about consumption and communication with friends about consumption for consumer skill. Third, according to the regression analyses, educational level was significant for consumer knowledge. Additionally, communication with family about consumption and TV contact frequency were significant for consumer role attitude, and age, educational level, communication with family about consumption and communication with friends about consumption were significant for consumer skill.

• ETRI Journal
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• v.33 no.4
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• pp.600-610
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• 2011

This paper presents a novel approach to face detection by localizing faces as the goal-specific saliencies in a scene, using the framework of selective visual attention of a human with a particular goal in mind. The proposed approach aims at achieving human-like robustness as well as efficiency in face detection under large scene variations. The key is to establish how the specific knowledge relevant to the goal interacts with the bottom-up process of external visual stimuli for saliency detection. We propose a direct incorporation of the goal-related knowledge into the specification and/or modification of the internal process of a general bottom-up saliency detection framework. More specifically, prior knowledge of the human face, such as its size, skin color, and shape, is directly set to the window size and color signature for computing the center of difference, as well as to modify the importance weight, as a means of transforming into a goal-specific saliency detection. The experimental evaluation shows that the proposed method reaches a detection rate of 93.4% with a false positive rate of 7.1%, indicating the robustness against a wide variation of scale and rotation.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.43-54
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• 2019

Currently, a majority of artificial intelligence is used to secure big data; however, it is concentrated in a few of major companies. Therefore, automatic data augmentation and efficient learning algorithms for small-scale data will become key elements in future artificial intelligence competitiveness. In addition, it is necessary to develop a technique to learn meanings, correlations, and time-related associations of complex modal knowledge similar to that in humans and expand and transfer semantic prediction/knowledge inference about unknown data. To this end, a neural memory model, which imitates how knowledge in the human brain is processed, needs to be developed to enable knowledge expansion through modality cooperative learning. Moreover, declarative and procedural knowledge in the memory model must also be self-developed through human interaction. In this paper, we reviewed this essential methodology and briefly described achievements that have been made so far.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.116-122
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• 2010

As the importance of HCI(Human-Computer Interface) caused by dramatically developed computer technology is getting high, the requirement for the design of human friendly systems is also getting high. First of all, the personality and Emotional factor should be considered for implementing more human friendly systems. Many studies on Knowledge, Emotion and personality have been made, but the combined methods connecting these three factors is not so many investigated yet. It is known that memorizing process includes not only knowledge but also the emotion and the emotion state has much effects on the process of reasoning and decision making step. Accordingly, for implementing more human friendly efficient sophisticated intelligent system, the system considering these three factors should be modeled and designed. In this paper, knowledge-emotion reaction model was designed. Five types are defined for representing the personality and emotion reaction mechanism calculating emotion vector based on the extracted Thought threads by Type matching selection was proposed. This system is applied to the virtual memory and its emotional reactions are simulated.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.525-531
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• 2009

Data mining is the process of extracting hidden patterns from collected data. At this time, for collected data which take important role as the basic information for prediction and recommendation, the process to discriminate incorrect data in order to enhance the performance of analysis result, is needed. The existing methods to discriminate unexpected data from collected data, mainly relies on methods which are based on statistics or simple distance between data. However, for these methods, the problematic point that even meaningful data could be excluded from analysis due that the environment and characteristic of the relevant data are not considered, exists. This study proposes a method to endow human heuristic knowledge with weight value through the comparison between collected data and human heuristic knowledge, and to use the value for creating a decision tree. The data discrimination by the method proposed is more credible as human knowledge is reflected in the created tree. The validity of the proposed method is verified through an experiment.

• Journal of the Ergonomics Society of Korea
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• v.25 no.3
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• pp.49-60
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• 2006

Technological encroachment provides human operators with flood of information that must be analyzed to understand the environment and make judgments that lead to strategic actions. Further, the environment is not static and therefore uncertain, changing its aspect dynamically. Complexity accompanied with its dynamics imposes substantial difficulty to human operators' task. Criticality of having situational understanding becomes more important than ever. Situationalunderstanding requires the human operators possessing tacit knowledge in order for them to make the sense out of the situation while interacting with information from many heterogeneous sources, the notion of sensemaking. Sensemaking refers to the process of developing mental framework to assemble pieces of information representing different aspects of the environment that can be used to develop one's own actionable knowledge to implement their judgments in the uncertain environment. Therefore, judgment process and performance is a key component of sensemaking process. Among many judgment and decision making models, the lens model with its extension can be utilized to partially describe the judgmental aspect of sensemaking. One of the lens model parameters, unmodeled knowledge, can be a corresponding quantitative measure for the tacit knowledge that plays an important role in sensemaking. In this paper, a comprehensive literature for sensemaking is provided to formally define the notion of sensemaking in the military domain. Also, it is proposed that there is a crucial link between the sensemaking and human judgment process and performance from the lens model perspective. Potential implications for experimental framework are also proposed.

• Korean Journal of Human Ecology
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• v.22 no.4
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• pp.693-705
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• 2013

This study was conducted to identify adults' knowledge, dietary behavior related to sodium, the attitude towards a low-salt diet, and to examine the relation between these variables. The participants were 366 adults in Jeonbuk area. The data were analyzed using Pearson correlation coefficients, ANOVA and Duncan test with SPSS v. 12.0. The score for participants' knowledge about sodium was 12.32 points of a possible 16, the score for dietary behavior related to sodium was 45.74 points of a possible 70, and their score for attitude towards a low-salt diet was 30.35 points of a possible 50. The knowledge showed significant differences by gender (p<.05), and concern about health (p<.05). The dietary behavior of sodium use showed significant differences by gender (p<.001), age (p<.001), educational level (p<.05), job (p<.001), income (p<.05), BMI (p<.05), smoking (p<.01), drinking (p<.01), exercise (p<.05), regularity of health checkup (p<.001), and concern about health (p<.01). The attitude towards a low-salt diet showed significant differences by gender (p<.001), age (p<.001), job (p<.001), income (p<.001), smoking (p<.05), regularity of health checkup (p<.001), and concern about health (p<.001). There was a significant positive correlation between knowledge about sodium, dietary behavior related to sodium, attitude towards a low-salt diet. Dietary behavior related to sodium showed a positive correlation with attitudes towards a low-salt diet. In conclusion, it is necessary to consider the related factors for the development and implementation of systematic education programs that can encourage and promote preventive dietary behavior for disease, e.g. stomach cancer, and hypertension among adults.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.