• Tuberculosis and Respiratory Diseases
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• v.83 no.3
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• pp.211-217
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• 2020

The gold standard method for diagnosis of tuberculosis is the isolation of Mycobacterium tuberculosis through culture, but there is a probability of cross-contamination in simultaneous cultures of samples causing false-positives. This can result in delayed treatment of the underlying disease and drug side effects. In this paper, we reviewed studies on false-positive cultures of M. tuberculosis. Rate of occurrence, effective factors, and extent of false-positives were analyzed. Ways to identify and reduce the false-positives and management of them are critical for all laboratories. In most cases, false-positive is occurring in cases with only one positive culture but negative direct smear. The three most crucial factors in this regard are inappropriate technician function, contamination of reagents, and aerosol production. Thus, to reduce false-positives, good laboratory practice, as well as use of whole-genome sequencing or genotyping of all positive culture samples with a robust, extra pure method and rapid response, are essential for minimizing the rate of false-positives. Indeed, molecular approaches and epidemiological surveillance can provide a valuable tool besides culture to identify possible false positives.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.3
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• pp.163-168
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• 2015

Many IP address lookup approaches employ Bloom filters to obtain a high-speed search performance. Especially, it has been recently studied that the search performance of trie-based algorithms can be significantly improved by adding Bloom filters. In such algorithms, the number of trie accesses can be greatly reduced because Bloom filters can determine whether a node exists in a trie without actually accessing the trie. Bloom filters do not have false negatives but have false positives. False positives can lead to unnecessary trie accesses. The false positive rate must thus be reduced to enhance the performance of lookup algorithms applying Bloom filters. One important characteristic of trie-based algorithms is that all the ancestors of a node are also stored. The proposed algorithm utilizes this characteristic in reducing the false positive rate of a Bloom filter without increasing the size of the memory for the Bloom filter. When a Bloom filter produces a positive result for a node of a trie, we propose to check whether the ancestors of the node are also positives. Because Bloom filters have no false negatives, the negatives of any of the ancestors mean that the positive of the node is false. In other words, we propose to use more Bloom filter queries to reduce the false positive rate of a Bloom filter in trie-based algorithms. Simulation results show that querying one ancestor of a node can reduce the false positive rate by up to 67% with exactly the same architecture and the same memory requirement. The proposed approach can be applied to other trie-based algorithms employing Bloom filters.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.74-85
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• 2010

For the purpose of compromising hosts, attackers including infected hosts initially perform a portscan using IP addresses in order to find vulnerable hosts. Considerable research related to portscan detection has been done and many algorithms have been proposed and implemented in the network intrusion detection system (NIDS). In order to distinguish portscanners from remote hosts, most portscan detection algorithms use a fixed threshold that is manually managed by the network manager. Because the threshold is a constant, even though the network environment or the characteristics of traffic can change, many false positives and false negatives are generated by NIDS. This reduces the efficiency of NIDS and imposes a high processing burden on a network management system (NMS). In this paper, in order to address this problem, we propose an automatic portscan detection system using an fast increase slow decrease (FISD) scheme, that will automatically and adaptively set the threshold based on statistical data for traffic during prior time periods. In particular, we focus on reducing false positives rather than false negatives, while the threshold is adaptively set within a range between minimum and maximum values. We also propose a new portscan detection algorithm, rate of increase in the number of failed connection request (RINF), which is much more suitable for our system and shows better performance than other existing algorithms. In terms of the implementation, we compare our scheme with other two simple threshold estimation methods for an adaptive threshold setting scheme. Also, we compare our detection algorithm with other three existing approaches for portscan detection using a real traffic trace. In summary, we show that FISD results in less false positives than other schemes and RINF can fast and accurately detect portscanners. We also show that the proposed system, including our scheme and algorithm, provides good performance in terms of the rate of false positives.

• 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.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.550-569
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• 2019

We propose approaches for improving Bloom filter in terms of false positive probability and membership query speed. To reduce the false positive probability, we propose special type of additional Bloom filters that are used to handle false positives caused by the original Bloom filter. Implementing the proposed approach for a routing table lookup, we show that our approach reduces the routing table lookup time by up to 28% compared to the original Bloom filter by handling most false positives within the fast memory. We also introduce an approach for improving the membership query speed. Taking the hash table-like approach while storing only values, the proposed approach shows much faster membership query speed than the original Bloom filter (e.g., 34 times faster with 10 subsets). Even compared to a hash table, our approach reduces the routing table lookup time by up to 58%.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.181-186
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• 2014

The paper proposes a new method of segmentation-based feature extraction to improve performance in pedestrian upper-body detection. General pedestrian detectors that use local features are often plagued by false positives due to the locality. Color information of multi parts of the upper body is utilized in figure-ground segmentation scheme to extract an salient, "global" shape feature capable of reducing the false positives. The performance of the multi-part color segmentation-based feature is evaluated by changing color spaces and the parameters of color histogram. The experimental result from an upper-body dataset shows that the proposed feature is effective in reducing the false positives of local feature-based detectors.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.306-313
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• 2009

Top-k queries are issued to find out the highest (or lowest) readings in many sensor applications. Many top-k query processing algorithms are proposed to reduce energy consumption; FILA installs a filter at each sensor node and suppress unnecessary sensor updates; PRIM allots priorities to sensor nodes and collects the minimal number of sensor reading according to the priorities. However, if many sensor reading converge into the same range of sensor values, it leads to a problem that many false positives are occurred. In this paper, we propose a cluster-based approach to reduce them effectively. Our proposed algorithm operates in two phases: top-k query processing in the cluster level and top-k query processing in the tree level. False positives are effectively filtered out in each level. Performance evaluations show that our proposed algorithm reduces about 70% false positives and achieves about 105% better performance than the existing top-k algorithms in terms of the network lifetime.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.129-137
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• 2008

In this paper, we propose an improved Bayesian Filtering mechanism to reduce the False Positives that occurs in the existing Bayesian Filtering mechanism. In the existing Bayesian Filtering mechanism, the same Bayesian Filtering DB trained at the e-mail server is applied to each e-mail user. Also, the training method using receiving e-mails only could not provide the high quality of ham DB. Due to these problems, the existing Bayesian Filtering mechanism can produce the False Positives which misclassify the ham e-mails into the spam e-mails. In the proposed mechanism, the sending e-mails of the user are treated as the high quality of ham information, and are trained to the Bayesian ham DB automatically. In addition, by providing a different Bayesian DB to each e-mail user respectively, more efficient e-mail filtering service is possible. Our experiments show the improvement of filtering accuracy by 3.13%, compared to the existing Bayesian Filtering mechanism.

• BMB Reports
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• v.43 no.2
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• pp.110-114
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• 2010

The yeast three-hybrid system (Y3H), a powerful method for identifying RNA-binding proteins, still suffers from many false positives, due mostly to RNA-independent interactions. In this study, we attempted to efficiently identify false positives by introducing a tetracycline operator (tetO) motif into the RPR1 promoter of an RNA hybrid expression vector. We successfully developed a tight tetracycline-regulatable RPR1 promoter variant containing a single tetO motif between the transcription start site and the A-box sequence of the RPR1 promoter. Expression from this tetracycline-regulatable RPR1 promoter in the presence of tetracycline-response transcription activator (tTA) was positively controlled by doxycycline (Dox), a derivative of tetracycline. This on-off control runs opposite to the general knowledge that Dox negatively regulates tTA. This positively controlled RPR1 promoter system can therefore efficiently eliminate RNA-independent false positives commonly observed in the Y3H system by directly monitoring RNA hybrid expression.