• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.173-177
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• 2006

This paper investigates the asymmetric costs of false errors to enhance the detection systems performance. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of probe detection is enhanced by considering the costs of false errors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2341-2348
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• 2009

An intrusion detection technique based on host consider system call sequence or system call arguments. These two ways are suitable when system call sequence or order and length of system call arguments are out of order. However, there are two disadvantages which a false positive rate and a false negative rate are high. In this paper we propose the T-N2SCD detection model based on Time Window in order to reduce false positive rate and false negative rate. Data for using this experiment is provided from DARPA. As experimental results, the proposed model showed that the false positive rate and the false negative rate are lowest at an interval of 1000ms than at different intervals.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Journal of Microbiology and Biotechnology
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• v.11 no.3
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• pp.491-496
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• 2001

A modification of decarboxylating agar medium as described by niven was performed to improve the detection method of biogenic amine-producing bacteria and to eliminate the false-positive. A total of 120 bacterial strains isolated from kimchi were used to evaluate different dicarboxylating agar media and for screening biogenic amines. Potential false-positives ranged from approximately 66 to 79% of the strains tested in the already well-known media. In our improved medium, none of the 120 strains showed the potential false-positives. There was a good agreement (81.7%-87.5%) between the results obtained by the improved medium and by HPLC analysis. Consequently, this medium was greatly improved in screening biogenic amine-producing bacteria and discarding false-positives. Of the 120 kimchi isolates, 14.2, 18.3, 37.5, and 0.8% were found by HPLC to be the producers of histamine, tyramine, putrescine (as a form of spermine), and cadaverine, respectively. The proportion of biogenic amine producer during kimchi fermentation increased to a maximum at an immature period and decreased thereafter.

• Journal of IKEEE
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• v.24 no.4
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.292-294
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• 2004

단백질 상호작용 검출 방법의 발달로 많은 양의 데이터가 산출되고 있고, 이러한 상호작용 데이터의 방대한 양으로 인해 통계적 방법을 이용하여 데이터를 처리함으로서 유용한 지식을 얻을 수 있다 예측한 상호작용 데이터는 첫째, 대량의 데이터를 생산해내므로, 많은 false-positive를 내포하고 있고, 둘째, 예측한 상호작용을 검증시 실험을 하는 방법 외에는 신뢰도를 측정하기가 어렵다는 문제점이 있다. 본 연구에서는 점수 할당시스템을 사용함으로서 예측한 인간 단백질 상호작용 데이터의 false-positive를 줄이고, 각각 상호작용에 점수를 부설함으로서 상호작용 데이터의 신뢰도를 검증하는 방법을 제안하고 있다.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.697-706
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• 2012

An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to common false-positive error. The performance of the test is compared with likelihood-based tests. The Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level well, and has comparable power performance with a computational advantage.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.703-706
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• 2004

나선형 CT 혈관촬영에서 획득한 영상의 분석를 통해서 폐색전증이 의심되는 부위를 자동으로 검출하는 방법으로, 연구 대상은 20명의 환자를 대상으로 분석하였으며 CT 검사 후 방사선과 의사가 정상소견을 받은 환자 5명과 폐색전증이 있는 판독소견을 가진 15명을 대상으로 비교 분석하였다. CT 검사하는 동안에 조영제를 투입하면, 폐색전증이 발생한 부위는 조영제 양과 분포가 불균등하여 명암값이 낮게 검출된다. 검출방법으로는 전처리 작업으로 폐영역만을 분할하고, 분할된 폐영역에서 혈관을 찾기 위해 모폴로지기법를 적용하여 세선화(thinning) 작업을 진행한다. 다음 공정으로는 경계선을 찾아 local watershed를 적용하여 혈관을 검출하고, 검출된 혈관내에서 원형모델을 적용하여 모폴로지(morphology)을 통해 국소 부위의 미세한 농도변화를 인지하여 색전이 발생한 영역을 자동검출하였다. 본 논문의 자동검출시스템에서는 색전증이 있는 경우에 true positive의 발생빈도는 case 당 4.5개가 검출되었다. 정상인의 경우에도 혈류의 흐름, 혈류의 분기점, 노이즈로 인한 false positive의 빈도는 case 당 2.6개가 발생하여 전체적으로 false positive는 5.2개가 검출되었다. 본 논문은 false positive의 비율이 높게 검출되었지만 폐영역 CT 검사의 컴퓨터지원진단시스템(computer aided diagnosis)의 향후 연구과제에 방향을 제시할 수 있을 것이라 사료된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.316-321
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• 2008

Advanced computer network technology enables computers to be connected in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, which makes it vulnerable to previously unidentified attack patterns and variations in attack and increases false negatives. Intrusion detection and analysis technologies are thus required. This paper investigates the asymmetric costs of false errors to enhance the performances the detection systems. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors, this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of anomaly traffic detection is enhanced by considering the costs of false errors.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1614-1615
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• 2011

It is difficult to differentially diagnose between lung cancer and benign inflammatory lung lesion due to high false positive rate on F-18 FDG-PET. We investigated whether application of artificial neural network to this diagnosis may be helpful. We reviewed the medical records and F-18 FDG PET images of 12 patients, selecting clinical and PET variables such as SUV. For selected variables and confirm, multilayer neural perceptron was applied in crossvalidation method and compared to visual interpretation. Neural network correctly classified the lung lesions in 83%, and reduced greately the false positive rate. However, false negative rate was not influenced. Application of neural network to the differential diagnosis between lung cancer and benigh inflammatory lesion may be helpful. Further studies with more patients are warranted.