• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• International journal of thyroidology
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• v.11 no.2
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• pp.130-136
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• 2018

Background and Objectives: Intraoperative neural monitoring (IONM) of recurrent laryngeal nerve (RLN) in thyroid surgery has been employed worldwide to identify and preserve the nerve as an adjunct to visual identification. The aims of this study was to evaluate the efficacy of IONM and difficulties in the learning curve. Materials and Methods: We studied 63 patients who underwent thyroidectomy with IONM during last 2 years. The standard IONM procedure was performed using NIM 3.0 or C2 Nerve Monitoring System. Patients were divided into two chronological groups based on the success rate of IONM (33 cases in the early period and 30 cases in the late period), and the outcomes were compared between the two groups. Results: Of 63 patients, 32 underwent total thyroidectomy and 31 thyroid lobectomy. Failure of IONM occurred in 9 cases: 8 cases in the early period and 1 case in the late period. Loss of signal occurred in 8 nerves of 82 nerves at risk. The positive predictive value increased from 16.7% in the early period to 50% in the late period. The mean amplitude of the late period was higher than that of the early period (p<0.001). Conclusion: IONM in thyroid surgery is effective to preserve the RLN and to predict postoperative nerve function. However, failure of IONM and high false positive rate can occur in the learning curve, and the learning curve was about 30 cases based on the results of this study.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• Tuberculosis and Respiratory Diseases
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• v.62 no.6
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• pp.523-530
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• 2007

Background: A ThinPrep$^{(R)}$ Processor was developed to overcome the limitations of conventional cytology and is widely used to diagnose various cancers. This study compared the diagnostic efficacy of conventional cytology for lung cancer with that of the ThinPrep$^{(R)}$ cytology using the bronchial washing fluid. Methods: The bronchial washing fluid of 790 patients from Jan. 2002 to Dec. 2006, who were suspected of gaving a lung malignancy, was evaluated. Both ThinPrep$^{(R)}$ and conventional cytology were performed for all specimens. Result: Four hundred forty-six men and 344 women were enrolled in this study, and 197 of them were diagnosed with cancer from either a bronchoscopic biopsy or a percutaneous needle aspiration biopsy. ThinPrep$^{(R)}$ cytology showed a sensitivity, specificity, positive predictive value, negative predictive value and false negative error rate of 71.1%, 98.0%, 92.1%, 91.1%, 8.9%, respectively. The conventional cytology showed sensitivity, specificity, positive predictive value, nagative predictive value and false negative error rate of 57.9%, 98.0%, 90.5%, 87.5%, 12.5%, respectively. For central lesions, the sensitivity of conventional cytology and ThinPrep$^{(R)}$ were 70.1% and 82.8%, respectively. Conclusion: ThinPrep$^{(R)}$ cytology showed a higher sensitivity and lower false negative error rate than conventional cytology. This result was unaffected by the histological classification of lung cancer. Therefore, ThinPrep$^{(R)}$ cytology appears to be a useful method for increasing the detection rate of lung cancer in bronchial washing cytology test.

• Korean Journal of Veterinary Research
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• v.29 no.1
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• pp.27-35
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• 1989

To develop more specific and sensitive diagnostic methods for infectious bovine rhinotracheitis, 7-C-2 monoclonal antibody specific to polypeptides of infectious bovine rhinotracheitis virus (IBRV) was applied in indirect immunofluorescence antibody assay (IFA), indirect immunoperoxidase assay(IPA) and radial immunodiffusion enzyme assay (RIDEA). It was found that IBRV infected in MDBK cells could be detected as early as 8 hours post infection by IFA, and that IFA was more rapid and specific to identify IBRV antigen than IPA. The diagnostic efficacy of RIDEA and SN test was studied with 88 bovine sera. It was evident that RIDEA could eliminate the false positive reaction encountered in serum neutralization(SN) test, being more rapid and sensitive than the latter. Highly significant correlation coefficiency (r=0.76, p<0.01) was evaluated between the titers of sera and the diameters of RIDEA. Tracheal membranes and sera collected from 96 slaughtered cattle with lesions in respiratory organs were examined to detect IBRV antigen and antibody by IFA, RIDEA and SN test. It was presented that positive rates were 32.3% in IFA, 20.8% in RIDEA and 21.9% in SN test, and that coincidence rate between RIDEA and SN test were 100% in positive sera and 98.7% in negative sera. In conclusion, it was assumed that application of monoclonal antibody could improve the diagnostic efficacy of IBR by enhancing sensitivity and specificity of IPA, IFA and RIDEA.

• Journal of Microbiology and Biotechnology
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• v.30 no.3
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• pp.459-468
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• 2020

This study established a new polymerase spiral reaction (PSR) that combines with reverse transcription reactions for HCV detection targeting 5'UTR gene. To avoid cross-contamination of aerosols, an isothermal amplification tube (IAT), as a separate containment control, was used to judge the result. After optimizing the RT-PSR reaction system, its effectiveness and specificity were tested against 15 different virus strains which included 8 that were HCV positive and 7 as non-HCV controls. The results showed that the RT-PSR assay effectively detected all 8 HCV strains, and no false positives were found among the 7 non-HCV strains. The detection limit of our RT-PSR assay is comparable to the real-time RT-PCR, but is more sensitive than the RT-LAMP. The established RT-PSR assay was further evaluated for detection of HCV in clinical blood samples, and the resulting 80.25% detection rate demonstrated better or similar effectiveness compared to the RT-LAMP (79.63%) and real-time RT-PCR (80.25%). Overall, the results showed that the RT-PSR assay offers high specificity and sensitivity for HCV detection with great potential for screening HCV in clinical blood samples.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• BMB Reports
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• v.46 no.1
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• pp.41-46
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• 2013

Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.101-109
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• 2008

Cervix cancer is one of common gynecological cancers in the world, and staged with FIGO or TNM system. However, these clinical staging systems lack information about lymph node or distant metastases, thus imaging modalities are considered to make an appropriate therapeutic plan and enhance overall survival rate. In this context, FDG PET is recommended to pre-treatment stating and prognosis prediction, for it could noninvasively evaluate the status of lymph nodes, especially abdominal paraaortic nodes which are closely related with prognosis. Moreover, the degree of FDG uptake is correlated with prognosis. Although there is no consistent method for surveillance of cervix cancer, FDG PET seems a very important tool in detecting tumor recurrence because it is much more advantageous than conventional imaging modalities such as MRI for discerning recurrent tumor from fibrosis caused by radiation or surgery. Furthermore, FDG PET could be used to evaluate treatment response. On the other hand, recently introduced PET/CT is expected to play an ancillary role to FIGO staging by adding anatomical information, and enhance diagnostic performance of PET by decreasing false positive findings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2591-2611
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• 2020

Botnets have become one of the most significant threats to Internet-connected smartphones. A botnet is a combination of infected devices communicating through a command server under the control of botmaster for malicious purposes. Nowadays, the number and variety of botnets attacks have increased drastically, especially on the Android platform. Severe network disruptions through massive coordinated attacks result in large financial and ethical losses. The increase in the number of botnet attacks brings the challenges for detection of harmful software. This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. The prototype was implemented and used to validate the performance, accuracy, and scalability of the proposed framework by evaluating 3000 android applications. The obtained results show the proposed framework obtained 98.20% accuracy with a low 0.1140 false-positive rate.