• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2069-2074
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• 2014

Purpose: To appraise the frequency of cervical cytological abnormalities in a population at normal risk via analysing the archive records of cytology for the period of approximately 9,5 years, comparing them with patient demographic charecteristics, and discuss the results for women under age of 35. Materials and Methods: A total of 32,578 cases of Pap smears were retrieved and analysed from our archive included the Pap tests performed between January 2001 and April 2010 at the Early Cancer Screening, Diagnosing and Education Center by the consent of three pathologists via utilizing the Bethesda System Criteria 2001 and the results were compared with some demographical characteristics. Results: Our rate of the cervical cytological abnormality was 1.83%, with ASCUS in 1.18%, LSIL in 0.39, HSIL in 0.16%, AGUS in 0.07%, squamous cell carcinoma in 0.02%, and adenoarcinoma in 0.006%. Cytological abnormalities were detected mostly in those with higher age, lower parity, and premenopausal period whereas the smoking status was without influence. Bacterial vaginosis (5.6%) was the most frequent infectious finding (Candida albicans 2.7%; Actinomyces sp. 1.3%; and Trichomonas vaginalis 0.2%) detected on the smears. The rate of abnormal cervical cytology was 9.5% among the women aged between 30-34. Conclusions: Early detection of the cervical abnormalities by means of the regular cervical cancer screening programmes is useful to attenuate the incidence, mortality, and morbidity of cervical cancer. Our prevalence of the cytological abnormalities was much lower than the one in Western populations in general but very similar to those reported from other Islamic countries that may be explained by the conservative lifestyle and the lower prevalence of HPV in Turkey. A remarkable rate of abnormal cervical cytology of women aged 30-34 was pointed out in the present study.

• Journal of Embryo Transfer
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• v.30 no.1
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• pp.51-57
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• 2015

This study was aimed to determine the biometry of genital organs, incidence of gynecological disorders and pregnancy loss in Black Bengal goat (Capra hircus). Genitalia of 118 does were collected from local abattoirs. Biometric parameters of genital organs were measured and gross and histopathological examinations were carried out for detection of abnormalities. For gravid uterus, age of the fetus was determined by measuring crown-rump length. There was no significant difference in the length, width and weight of right and left ovaries (P>0.05). However, the number of follicles between left ($5.3{\pm}2.3$) and right ovaries ($7.4{\pm}2.7$) varied significantly (P<0.05). The mean length of right fallopian tube and uterine horn were not varied with those of left fallopian tube and uterine horn. The length of uterine body, cervix and vagina were $1.3{\pm}0.1cm$, $3.3{\pm}0.5cm$ and $6.8{\pm}1.3cm$, respectively. Overall, 29 (24.6%) genitalia had abnormalities. Fifteen genitalia (12.7%) had ovarian abnormalities including ovaro-bursal adhesions (6.8%), parovarian cyst (5.1%) and follicular cyst (0.9%). Uterine abnormalities were found in 12 genitalia (10.2%) and predominant uterine lesion was endometritis (6.8%) followed by adenomyosis (1.7%), hemorrhagic lesion on endometrial surface (0.9%) and cyst in broad ligament (0.9%). In addition, cyst in fallopian tube (0.9%) and vagina (0.9%) were recorded. The proportion of slaughtered pregnant goats was 15.3% (18/118). The pregnancy wastage was highest in the first month (50.0%) followed by second (33.3%) and third (16.7%) month. It can be concluded that ovaro-bursal adhesions, parovarian cyst and endometritis are the gynecological disorders of major concern in Black Bengal goat.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.133-146
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• 2015

Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7193-7196
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• 2013

Ovarian cancer patients need a surveillance program for the detection of tumor progression after completion of treatment. The methods generally consist of history taking, physical examination, tumor marker monitoring and imaging. However, the details of recurrence detection with each method are not well defined. To clarify this issue, ovarian cancer patients who achieved complete or partial responses and developed tumor progression at the follow up time between January 2004 and December 2010 in University Hospital Chiang Mai, Thailand, were reviewed. Clinical data, CA 125 level and imaging results at the tumor progression time were recorded and analyzed. There were 144 ovarian cancer patients meeting the inclusion criteria with the mean age of 51 years and 62.5% of them were in an advanced stage. Complete response was achieved in 89 patients (61.8%) after primary treatment. The median progression free survival and overall survival were 15.5 months and 37.5 months, respectively. Abnormal symptoms presented in 49.3% of the studied patients and 59.7% developed physical examination abnormalities. In addition, CA 125 was elevated in 89.6% while in 74.3% of tumor progression was identified by CT-scan. Short treatment time period and a high level of CA 125 were significant independent prognostic factors in these patients. In conclusion, careful history taking, physical examination and monitoring of CA 125 levels are important methods for tumor progression detection in a surveillance program for epithelial ovarian cancer patients.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5613-5618
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• 2015

Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

• Journal of Korea Association of Health Promotion
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• v.3 no.1
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• pp.110-120
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• 2005

The ultrasonography(US) is one of very important item for abdominal evaluation. Especially for the routine check of the abdomen, the US becomes the essential part of the precedure. Though the abdominal US is simple and easy, its value is very high for detection of various morphological changes of abdominal organs. The study techniques of US, common abdominal abnormalities, standardization of image interpretation, and clinical significance of the lesions, etc, were discussed.