• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3147-3153
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• 2016

Background: Many strategies are required for cervical cancer reduction e.g. provision of education cautious sexual behavior, HPV vaccination, and early detection of pre-invasive cervical lesions and invasive cancer. Basic health data for cervical cytology/ HPV DNA and associated factors are important to make an appropriate policy to fight against cervical cancer. Aims: To assess the prevalence of abnormal cervical cytology and/or HPV DNA and associated factors, including sexual behavior, among Bangkok Metropolitan women. Materials and Methods: Thai women, aged 25-to-65 years old, had lived in Bangkok for ${\geq}5$ years were invited into the study. Liquid-based cervical cytology and HPV DNA tests were performed. Personal data were collected. Main Outcomes Measures: Rates of abnormal cytology and/ or high-risk HPV (HR-HPV) and factors associated with abnormal test (s) were studied. Results: Abnormal cytology and positive HR-HPV were found in 6.3% (279/4442 women) and 6.7% (295/4428), respectively. The most common abnormal cytology was ASC-US (3.5%) while the most common HR-HPV genotype was HPV 16 (1.4%) followed by HPV 52 (1.0%), HPV 58 (0.9%), and HPV 18 and HPV 51 at equal frequency (0.7%). Both tests were abnormal in 1.6% (71/4428 women). Rates of HR-HPV detection were directly associated with severity of abnormal cytology: 5.4% among normal cytology and 13.0%, 30.8%, 40.0%, 39.5%, 56.3% and 100.0% among ASC-US, ASC-H, AGC-NOS, LSIL, HSIL, and SCC, respectively. Some 5% of women who had no HR-HPV had abnormal cytology, in which 0.3% had ${\geq}$ HSIL. Factors associated with abnormal cytology or HR-HPV were: age ${\leq}40$ years, education lower than (for cytology) or higher than bachelor for HR-HPV), history of sexual intercourse, and sexual partners ${\geq}2$. Conclusions: Rates for abnormal cytology and HR-HPV detection were 6.3% and 6.7% HR-HPV detection was directly associated with severity of abnormal cytology. Significant associated factors were age ${\leq}40$ years, lower education, history of sexual intercourse, and sexual partners ${\geq}2$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.731-739
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• 2015

In arrhythmia ECG signal, abnormal beat that has various abnormal shape depending on the generation site and conduction disorders is included and it is very important to diagnose heart disease such as arrhythmia. In this paper, we propose a PVC abnormal beat detection algorithm associated with ventricular disease. The PVC abnormal beat is characterized by distortion of the QRS complex occurs among the components of the ECG signal. Therefore it is possible to detect PVC abnormal beat according to the degree of distortion of the QRS complex. First, quantify the distortion of the QRS complex by using the potential of the R-peak, kurtosis and period. By using the mean and standard deviation, PVC abnormal beat is detected depending on the degree of distortion from the normal beat. The proposed algorithm can detect the average over 98% of the AAMI-V class type abnormal beat associated with ventricular disease in MIT-BIH arrhythmia database.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Journal of Biomedical Engineering Research
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• v.2 no.2
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• pp.83-88
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• 1981

A method of detecting abnormal spikes occuring in the EEG of subjects suffering from epilepsy is studied. The detection scheme is to take the first derivative of EEG and to determine if it exceed some threshold value. This study is focused on the digital signal processing for detecting abnormal spikes using microcomputer.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.731-737
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• 2014

This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.127-136
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• 2024

This study explores the correlation between system anomalies and large-scale logs within the Spark cluster environment. While research on anomaly detection using logs is growing, there remains a limitation in adequately leveraging logs from various components of the cluster and considering the relationship between anomalies and the system. Therefore, this paper analyzes the distribution of normal and abnormal logs and explores the potential for anomaly detection based on the occurrence of log templates. By employing Hadoop and Spark, normal and abnormal log data are generated, and through t-SNE and K-means clustering, templates of abnormal logs in anomalous situations are identified to comprehend anomalies. Ultimately, unique log templates occurring only during abnormal situations are identified, thereby presenting the potential for anomaly detection.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2482-2484
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• 2000

In this paper, we have developed an automated computer aided diagnostic (CAD) scheme by using artificial neural networks(ANN) on guantitative analysis of chest photofluorography. The first ANN performs the detection of suspicious regions in a low resolution image. This was trained specifically on the problem of detecting abnormal regions digitized chest photofluorography. The second space matching method was used to distinguish between normal and abnormal regions of interest(ROI). If the ratio of the number of abnormal ROI to the total number of all ROI in a chest image was greater than a specified threshold level, the image was classified as abnormal.