• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• Research in Community and Public Health Nursing
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• v.13 no.2
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• pp.363-375
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• 2002

Objectives: The aim of this study was to analyze the trend of research on cancer prevention and early detection in Korea, in order to suggest a future direction of research on cancer prevention and early detection for Koreans. Methods: A total of 97 studies published from 1980 to 2001 were analyzed according to the year of publication, research design, journal type, cancer type, major study concepts, and findings. Results: 1) The number of studies related to cancer prevention and early detection had increased rapidly since the year 1995. 2) The most frequently used research design in the studies was the descriptive study design (55.7%). 3) There were 10 master's theses on cancer prevention and early detection, and 10 studies published in the Korean Epidemiology Journal. 4) When classified by the published field, 47 studies (48.5%) were published in nursing journals, 46 studies (47.4%) were published in medical journals, and 4 studies (4.1%) were published in public health journals. 5) The major topics of the studies were cancer prevention (51.5%), early detection (44.4%), and cancer prevention and early detection (4.1%). 6) Breast cancer was the most largely addressed issue in the studies (N=25; 25.7%), followed by lung cancer (N=23; 23.7%), hepatoma (N=17; 17.5%), gastric cancer (N=16; 16.5%), other general type of cancer (N=6; 6.2%), colorectal cancer (N=5; 5.2%) and cervical cancer (N=5; 5.2%). Conclusion: It is suggested that there should be more studies on cancer prevention and early detection in the future, and, particularly, experimental studies to exam the effects of intervention on cancer prevention and early detection are considered necessary.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1117-1123
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• 2021

Object recognition is to identify the location and class of a specific object by analyzing the given image when a specific image is input. One of the fields in which object recognition technology is actively applied in recent years is autonomous vehicles, and this paper describes the trend of image-based object recognition artificial intelligence technology in autonomous vehicles. The image-based object detection algorithm has recently been narrowed down to two methods (a single-step detection method and a two-step detection method), and we will analyze and organize them around this. The advantages and disadvantages of the two detection methods are analyzed and presented, and the YOLO/SSD algorithm belonging to the single-step detection method and the R-CNN/Faster R-CNN algorithm belonging to the two-step detection method are analyzed and described. This will allow the algorithms suitable for each object recognition application required for autonomous driving to be selectively selected and R&D.

• Convergence Security Journal
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• v.4 no.2
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• pp.1-7
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• 2004

The growth of incidents on the Internet has reflected growth of the internet itself and growth of the computing Power. while in Previous years, external attacks tended to originate from those interested trend in exploring the Internet for its own sake and testing their skills, there is an increasing trend towards intrusions motivated by financial, Political, and military objectives. so, attacks on the nation's computer infrastructures are becoming an increasingly serious problem. Even though the problem is ubiquitious, government agencies are particularly appealing targets and they tend to be more willing to reveal such events than commercial organizations. The threat of damage made necessity of security's recognition, as a result, many researches have been carried out into security of system actively. Intrusion Detection technology is detection of intrusion using audit data differently from using traditional simple filtering and informs manager of it. It has security manager of system deal with the intrusion more quickly. but, cause current environment of Internet manager can't doing response Intrusion alert immediately That's why IPS needed. IPS can response automatically the intrusion alert. so, manager is more comfortable and can response quickly.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.131-141
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• 2006

A visual decision by clinical experts like physical therapists is a best way to detect onset and offset time of muscle activation. The current computer-based algorithms are being researched toward similar results of clinical experts. The new algorithm in this paper has an ability to extract a trend from noisy input data. Kalman smoother is used to recognize the trend to be revealed from disorderly signals. Histogram of smoothed signals by Kalman smoother has a clear boundary to separate muscle contractions from relaxations. To verify that the Kalman smoother algorithm is reliable way to detect onset and offset time of muscle contractions, the algorithm of Robert P. Di Fabio (published in 1987) is compared with Kalman smoother. For 31 templates of subjects, an average and a standard deviation are compared. The average of errors between Di Fabio's algorithm and experts is 109 milliseconds in onset detection and 142 milliseconds in offset detection. But the average between Kalman smoother and experts is 90 and 137 milliseconds in each case. Moreover, the standard deviations of errors are 133 (onset) and 210 (offset) milliseconds in Di Fabio's one, but 48 (onset) and 55 (offset) milliseconds in Kalman smoother. As a result, the Kalman smoother is much closer to determinations of clinical experts and more reliable than Di Fabio's one.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4377-4384
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• 2014

According to the World Health Organization (WHO), 1.37 million people died of lung cancer all around the world in 2008, occupying the first place in all cancer-related deaths. However, this number might be decreased if patients were detected earlier and treated appropriately. Unfortunately, traditional imaging techniques are not sufficiently satisfactory for early detection of lung cancer because of limitations. As one alternative, breath volatile organic compounds (VOCs) may reflect the biochemical status of the body and provide clues to some diseases including lung cancer at early stage. Early detection of lung cancer based on breath analysis is becoming more and more valued because it is non-invasive, sensitive, inexpensive and simple. In this review article, we analyze the limitations of traditional imaging techniques in the early detection of lung cancer, illustrate possible mechanisms of the production of VOCs in cancerous cells, present evidence that supports the detection of such disease using breath analysis, and summarize the advances in the study of E-noses based on gas sensitive sensors. In conclusion, the analysis of breath VOCs is a better choice for the early detection of lung cancer compared to imaging techniques. We recommend a more comprehensive technique that integrates the analysis of VOCs and non-VOCs in breath. In addition, VOCs in urine may also be a trend in research on the early detection of lung cancer.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.240-242
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• 2005

It is well known that the PD (Partial Discharge) signals are generated if insulators have some defects such as voids in electrical facility and various PD detection methods are developed for preventing electrical troubles. So, an interest for the PD signals is higher and higher according to the high concern for the defects detection method of the aging electrical facility. When the equipment to detect PD signals installed at site and it works, a lot of noises flow in the equipment from surrounding situation and it will be mixed with original PD waveform. So we can not get the desired PD waveform. Therefore, there are many trial to reject or suppress the noise from the PD signals from long times ago. The greater of them used the hardware such as bridge circuits and frequency filters to suppress the noise. This paper proposed a novel noise rejection method in acquired data from PD detection equipment. The noise has the irregular phase and higher signal level than real PD, and noise decision is performed after inspection of pulse distribution in ${\Phi}$-q-n graph of acquired data from PD detection equipments. By experimental results on high voltage electric equipments, it is shown that proposed method has good performance. It is expected that this noise rejection technology is useful in numeric calculation and trend management of PD level.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.28 no.1
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• pp.100-107
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• 2018

Objectives: This study examines exposure to hazardous substances in the working environment caused by exposure to toxic substances produced in the aluminum die casting process in the automobile manufacturing industry. Materials and Methods: The exposure concentration levels, detection rates and time-trend of 15 hazardous factors in the aluminum die casting process over 10 years(from 2006 to 2016) were used as a database. Results: The study found that hazardous factors in the aluminum die casting process were mostly metals. The rate for detected samples was 70.6%(405 samples), and that for not detected samples was 29.4%. The noise for an eight-hour work shift showed a 49.7% exceedance rate for TLV-TWA. Average noise exposure was 89.0 dB. The maximum exposure level was 105.1 dB. Conclusion: The high numbers of no-detection rates for hazardous substance exposure shows that there is no need to do a work environment measurement. Therefore, alternatives are necessary for improving the efficiency and reliability of the work environment measurement. Moreover, to prevent noise damage, reducing noise sources from automation, shielding, or sound absorbents are necessary.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.6 no.4
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• pp.39-52
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• 1996

Networking revolution provides users with data and resources sharing, distributed processing, and computer communication in cyberspace. However, users may use computers as a way of unauthorized access, system destruction, and leakage of the stored data. In recent trend, incresing of hacking instances which are from domestic as well as abroad reaches to the level of seriousness. It, therefore, is required to develop a secure system for the National Depense computing resources and deploy in practice in the working field as soon as possible. In this paper, we focuss on finding the security requirements of a network and designing Intrusion Detection System using statical intrusion detection and rule-based intrusion detection analysis through accumulating audit data.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.