• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.6
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• pp.95-104
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• 2006

The presence of cavitations under pavements or behind tunnel linings of concrete is likely to result in collapse. One method of detecting such voids by non-destructive means is low frequency type radar(GPR). By the way, the size and thickness of small cavitation can't be detected by the present radar technology with low frequency and low resolution when it apply to civil structures like that. To overcome these problems and limitations, this study aims to develope and propose a new analysis method for estimating the depth, cross-sectional size and thickness of cavitations using low frequency radar. A new proposed method is based on the experiments that are carried out for analyzing the correlation between the measurement values(the amplitudes of radar return) of low frequency radar and various type of cavitations. In this process, the threshold value for radar image processing which aims to represent only cavitations to be fitted size can be obtained. As the results, it is clarified that a proposed method has a possibility of estimating cavitation depth, size and thickness with good accuracy in laboratory scale.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.1-8
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• 2024

We present a novel algorithm for creating 2D maps tailored for autonomous navigation within orchards. Recognizing that fruit trees in orchards are typically aligned in rows, our primary goal is to accurately detect these tree rows and project this information onto the map. Initially, we propose a simple algorithm that recognizes trees from point cloud data by analyzing the spatial distribution of points. We then introduce a method for detecting fruit tree rows based on the positions of recognized fruit trees, which are integrated into the 2D orchard map. Validation of the proposed approach was conducted using real-world orchard point cloud data acquired via LiDAR. The results demonstrate high tree detection accuracy of 90% and precise tree row mapping, confirming the method's efficacy. Additionally, the generated maps facilitate the development of natural navigation paths that align with the orchard's layout.

• The Korean journal of internal medicine
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• v.39 no.3
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• pp.430-438
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• 2024

Background/Aims: A poor prognostic factor for Crohn's disease (CD) includes perianal fistulizing disease, including perianal fistula and/or perianal abscess. Currently, a tool to assess perianal symptoms in patients with CD remains nonexistent. This study aimed to develop a perianal fistulizing disease self-screening questionnaire for patients with CD. Methods: This prospective pilot study was conducted at three tertiary referral centers between January 2019 and May 2020. We formulated questions on perianal symptoms, including tenesmus, anal discharge, bleeding, pain, and heat. A 4-point Likert scale was used to rate each question. Patients with CD completed a questionnaire and underwent pelvic magnetic resonance imaging (MRI). Results: Overall, 93 patients were enrolled, with 51 (54.8%) diagnosed with perianal fistulizing disease, as determined by pelvic MRI. The Spearman correlation findings demonstrated that anal pain (p = 0.450, p < 0.001) and anal discharge (p = 0.556, p < 0.001) were the symptoms that most significantly correlated with perianal disease. For anal pain and discharge, the area under the receiver operating characteristic curve of the scores was significantly higher than that of the combined score for all five symptoms (0.855 vs. 0.794, DeLong's test p = 0.04). For the two symptoms combined, the sensitivity, specificity, and positive predictive and negative predictive values were 88.2, 73.8, 80.4, and 83.8%, respectively, with 81.7% accuracy for detecting perianal fistulizing disease. Conclusions: This study indicates that simple questions regarding anal pain and discharge can help accurately identify the presence of perianal fistulizing disease in patients with CD.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5385-5389
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• 2012

Introduction: Sentinel lymph node biopsy (SLNB) is a precise procedure for lymphatic staging in early breast cancer. In a valid SLNB procedure, axillary lymph node dissection (ALND) can be omitted in nodenegative cases without compromising patient safety. In this study, detection rate, accuracy and false negative rate of SLNB for breast cancer was evaluated in a setting with simple modified conventional pathology facilities without any serial sectioning or immunohistochemistry. Material and Medthod: Patients with confirmed breast cancer were enrolled in the study. SLNB and ALND were performed in all cases. Lymph node metastasis was evaluated in SLN and in nodes removed by ALND to determine the false negative rate. Pathologic assessment was carried out only by modified conventional technique with only 3 sections. Detection rate was determined either by lymphoscintigraphy or during surgery. Results: 78 patients with 79 breast units were evaluated. SLN was detected in 75 of 79 cases (95%) in lymphoscintigraphy and 76 of 79 cases (96%) during surgery. SLN metastases was detected in 30 of 75 (40%) cases either in SLNB and ALND groups. Accuracy of SLNB method for detecting LN metastases was 92%. False negative rate was 3 of 30 of positive cases: 10%. In 7 of 10 cases with axillary lymphadenopathy, LN metastastates was detected. Conclusion: SLNB is recommended for patients with various tumor sizes without palpable lymph nodes. In modified conventional pathologic examination of SLNs, at least macrometastases and some micrometastases could be detected similar to ALND. Consequently, ALND could be omitted in node-negative cases with removal of all palpable LNs. We conclude that SLNB, as one of the most important developments in breast cancer surgery, could be expanded even in areas without sophisticated pathology facilities.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5427-5432
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• 2012

Background and Purpose: Ovarian cancer is the leading cause of death among gynecologic cancers because of the lack of effective early detection methods. Accuracies of the human epididymis protein 4 (HE4) and mesothelin in detecting ovarian cancer have never been systematically assessed. The current systematic review aimed to tackle this issue. Methods: MEDLINE, EMBASE, and Cochrane databases were searched (September 1995-November 2011) for studies on the diagnostic performances of HE4 and mesothelin in differentiating ovarian cancer from other benign gynecologic diseases. QUADAS items were used to evaluate the qualities of the studies. Meta-DiSc software was used to handle data from the included studies and to examine heterogeneity. All included studies for diagnostic performance were combined with sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratios (DORs) with 95% confidence intervals (CIs), summary receiver operating characteristic (SROC) curves, and areas under the SROC curves (AUC). Results: A total of 18 studies and 3,865 patients were eligible for the final analysis. The pooled sensitivity estimates for HE4 (74.4%) were significantly higher than those for mesothelin (49.3%). The pooled specificity estimates for mesothelin (94.5%) were higher than those for HE4 (85.8%). The pooled DOR estimates for HE4 (26.22) were higher than those for mesothelin (24.01). The SROC curve for HE4 showed better diagnostic accuracy than that for mesothelin. The PLR and NLR of HE4 were 6.33 (95% CI: 3.58 to 11.18) and 0.27 (95% CI: 0.21 to 0.34), respectively. The PLR and NLR for mesothelin were 11.0 (95% CI: 6.21 to 19.59) and 0.51 (95% CI: 0.42 to 0.62), respectively. The combination of the two tumor markers or their combination with CA-125 increased sensitivity and specificity to different extents. Conclusion: The diagnostic accuracy of HE4 in differentiating ovarian cancer from other benign gynecologic diseases is better than that of soluble mesothelin-related protein. Combinations of two or more tumor markers show more sensitivity and specificity.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.299-308
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• 2009

AAM(Active Appearance Model) is an algorithm to extract face feature points with statistical models of shape and texture information based on PCA(Principal Component Analysis). This method is widely used for face recognition, face modeling and expression recognition. However, the detection performance of AAM algorithm is sensitive to initial value and the AAM method has the problem that detection error is increased when an input image is quite different from training data. Especially, the algorithm shows high accuracy in case of closed lips but the detection error is increased in case of opened lips and deformed lips according to the facial expression of user. To solve these problems, we propose the improved AAM algorithm using lip feature points which is extracted based on a new lip detection algorithm. In this paper, we select a searching region based on the face feature points which are detected by AAM algorithm. And lip corner points are extracted by using Canny edge detection and histogram projection method in the selected searching region. Then, lip region is accurately detected by combining color and edge information of lip in the searching region which is adjusted based on the position of the detected lip corners. Based on that, the accuracy and processing speed of lip detection are improved. Experimental results showed that the RMS(Root Mean Square) error of the proposed method was reduced as much as 4.21 pixels compared to that only using AAM algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.111-119
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• 2013

It is essential and fundamental to extract boundary information of target object via massive three-dimensional point data acquired from laser scanner. Especially extracting boundary information of manmade features such as buildings is quite important because building is one of the major components consisting complex contemporary urban area, and has artificially defined shape. In this research, extended ${\chi}$-algorithm using geometry information of point data was proposed to extract boundary information of building from three-dimensional point data consisting building. The proposed algorithm begins with composing Delaunay triangulation process for given points and removes edges satisfying specific conditions process. Additionally, to make whole boundary extraction process efficient, we used Sweep-hull algorithm for constructing Delaunay triangulation. To verify the performance of the proposed extended ${\chi}$-algorithm, we compared the proposed algorithm with Encasing Polygon Generating Algorithm and ${\alpha}$-Shape Algorithm, which had been researched in the area of feature extraction. Further, the extracted boundary information from the proposed algorithm was analysed against manually digitized building boundary in order to test accuracy of the result of extracting boundary. The experimental results showed that extended ${\chi}$-algorithm proposed in this research proved to improve the speed of extracting boundary information compared to the existing algorithm with a higher accuracy for detecting boundary information.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.26 no.5
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• pp.462-469
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• 2000

Positron Emission Tomography(PET) is a new diagnostic method that can create functional images of the distribution of positron emitting radionuclides, which when administered intravenously in the body, makes possible anatomical and functional analysis by quantity of biochemical and physiological process. After genetic and biochemical changes in initial stage, malignant tumor undergoes functional changes before undergoing anatomical changes. So, early diagnosis of malignant tumors by functional analysis with PET can be achieved, replacing traditional anatomical analysis, such as computed tomography(CT) and magnetic resonance image(MRI), etc. Similarly, PET can identify malignant tumor without confusion with scar and fibrosis in follow up check. In the Korea Cancer Center Hospital(KCCH) from October 1997 to September 1999, clinical study was performed in 79 cases that underwent 89 times PET evaluation with [18F]-Fluorodeoxyglucose for diagnosis of oral and maxillofacial tumors, and the data was analysed by Bayesian $2{\times}2$ Classification Table. The results were as follows : Evaluation for initial diagnosis with FDG-PET (P<0.005) 1. Agreement rate or accuracy rate is 88.9%. 2. Sensitivity is 95.2%, and specificity 66.7%. 3. Positive predictive rate is 90.9%, and negative predictive rate 80.0%. 4. In consideration of tumor stage, diagnostic rate in less than stage II was 90% and in greater than stage III 100%. 5. In consideration of tumor size, diagnostic rate in less than T2 was 92.3% and in greater than T3 100%. After primary treatment, evaluation for follow up check with FDG-PET (P < 0.001) 1. Agreement rate or accuracy rate is 85.4%. 2. Sensitivity is 87.5%, and specificity 82.4%. 3. Positive predictive rate is 87.5%, and negative predictive rate 82.4%. 4. In 24 recurred cases, 6 had distant metastasis, and 5 of them were diagnosed with FDG-PET, resulting in diagnostic rate of FDG-PET of 83.3%. From the above results, Positron Emission Tomography with [18F]- Fluorodeoxyglucose appears to be more sensitive and accurate for detecting the presence of oral and maxillofacial tumors, and has various clinical applications such as early diagnosis of tumor in initial and follow up check and detection of distant metastasis.