• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Steel and Composite Structures
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• 제51권5호
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• 한국지역지리학회지
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• 제22권3호
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• pp.655-665
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• 2016

공동체의 의미와 역할은 지속적으로 변화해왔다. 전통적인 근린 중심의 공동체 이론은 '근접성 없는 공동체' 개념으로 위협받아 왔고, 교통과 통신, 인터넷과 SNS의 변화를 포괄하면서 공동체의 근접성은 더 이상 공동체의 전제조건이 아니게 되었다. 이 연구는 '근접성 없는 공동체'의 틀로 공동체 이론의 전개과정을 고찰하고, 사례로서 충북 괴산에 위치한 탑골만화방의 공동체성과 공간적 특성을 사회네트워크분석(SNA) 기법을 활용하여 분석하였다. 탑골만화방은 서울, 대구, 부산, 김해, 청주, 상주 등 전국에서 방문하며, 지리적으로 확산되고 있다. 탑골만화방은 또한 공공공간으로서 개방되어 있고, '목적없는 공간'이기 때문에 방문객들이 자유롭게 여러 활동을 실험하는 장으로 기능한다. 탑골만화방은 지역 내 외부의 사람들이 관계를 맺으며 공간적 사회적으로 경계를 확장해나가는 공동체적 특성을 보여준다.

• 도시과학
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• 제6권2호
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• pp.59-72
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• 2017

Studies on the calculation of accident costs include the approach on calculating damage costs covering all accidents regardless of first or secondary party and the one calculating damage costs generated by a single victim. These two approaches have a limitation of considering a subject for costs analysis as a single entity. In addition, research on estimating the interaction effects caused in the relationship between diverse traffic accident features and factors remains inadequate since most studies focused on calculating costs incurred in a single entity such as a victim, damaged building, or social organization in charge of managing car accident. This study intends to identify the expected range of old age where a specific interaction effect would remain, compare accidents between old age section and the entire age section, and discover an exogenous variable to be applied in accident drop effects in senior people and reduced benefits by calculating and testing additional accident costs in case the first party and the second party all pertain to the senior age section. By classifying the entire accidents caused by old drivers according to the types of cars, significant coefficients representing the influence that affects car accidents according to the characteristics are calculated and set them as the representative variables by selecting top variable in accordance with from low to high order. Furthermore, characteristics on five age groups such as a group of over 65 and less than 70, a group of over 70 and less than 75, a group of over 75 and less than 80, a group of over 80 and less than 85, and a group of over 85 are elicited and compared them with these preselected accident characteristics variables, thereby identifying what changing effects come out.

• Environmental Analysis Health and Toxicology
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• 제14권1_2호
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• pp.1-12
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• 1999

Recently, several new methods for the detection of genetic damages in vitro and in vivo based on molecular biological techniques were introduced according to the rapid progress in toxicology combined with cellular and molecular biology. Among these methods, mouse lymphoma thymidine kanase (tk) gene forward mutation assay, single cell gel electrophoresis (comet assay) and transgenic animal and cell line model as a target gene of lac I (Big Blue) and lac Z (Muta Mouse) gene mutation are newly introduced based on molecular toxicological approaches. The mouse lymphoma tk$\^$+/-/ gene assay (MOLY) using L5178Y tk$\^$+/-/ mouse lymphoma cell line is one of the mammalian forward mutation assays, and has many advantages and more sensitive than hprt assay. The target gene of MOLY is a heterozygous tk$\^$+/-/ gene located in 11 chromosome, so it is able to detect the wide range of genetic changes like point mutation, deletion, rearrangement, and mitotic recombination within tk gene or deletion of entire chromosome 11. The comet assay is a rapid, simple, visual and sensitive technique for measuring and analysing DNA breakages in mammalian cells, Also, transgenic animal and cell line models, which have exogenous DNA incorporated into their genome, carry recoverable shuttle vector containing reporter genes to assess endogenous effects or alteration in specific genes related to disease process, are powerful tools to study the mechanism of mutation in vivo and in vitro, respectively. Also in vivo acridine orange supravital staining micronucleus assay by using mouse peripheral reticulocytes was introduced as an alternative of bone marrow micronucleus assay. In this respect, there was an International workshop on genotoxicity procedure (IWGTP) supported by OECD and EMS (Environmental Mutagen Society) at Washington D. C. in March 25-26, 1999. The objective of IWGTP is to harmonize the testing procedures internationally, and to extend to finalization of OECD guideline, and to the agreement of new guidelines under the International Conference of Harmonization (ICH) for these methods mentioned above. Therefore, we introduce and review the principle, detailed procedure, and application of MOLY, comet assay, transgenic mutagenesis assay and supravital staining micronucleus assay.

• 정보화정책
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• 제22권3호
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• pp.3-15
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• 2015

이 연구는 최근 정보통신기술의 발전에 따라 모든 분야에서 관심이 증폭되고 있는 사물인터넷(IoT)에 관한 국내 연구 동향을 살펴보았다. 2010년부터 학술지에 실린 101편의 논문을 연구주제, 연구방법, 연구학문분야를 중심으로 분석하였다. 사물인터넷에 대한 대부분의 연구주제가 기술과 산업에 치중되어 있었고, 그중에도 기술 분야의 비중이 매우 높았다. 기술 분야에서도 최근 사물인터넷의 기술을 소개하는 기술제안이 대부분이었다. 사물인터넷의 연구방법은 시험연구가 대부분이었고, 문헌고찰도 상당부분 차지하였다. 사물인터넷을 연구한 학문분야도 공학 분야가 대부분을 차지하고 있으나 일부 사회과학분야의 연구도 있었다. 사물인터넷은 산업분야 뿐만 아니라 사회문화적으로 파급효과가 크다는 점에서 볼 때, 향후 기술적인 연구뿐만 아니라 산업, 서비스, 정책과 제도 분야의 연구를 다양한 학문분야와 연구방법을 통해 진행할 필요가 있다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권sup3호
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• pp.149-153
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• 2016

Breast cancer is the most common cancer in Iran. In the recent years an upward trend has been observed in the Iranian population. Early detection by molecular approaches may reduce breast cancer morbidity and mortality. We provided consultation to 3,782 women diagnosed with early onset breast cancer during the past 15 years (1999-2014). To establish a data set for BRCA gene alterations of the Iranian families at risk, two hundred and fifty four women who met our criteria were analyzed. A total number of 46 alterations including 18 variants with unknown clinical significance (39.1%), 18 missense mutations (39.1%), 7 Indels (15.2%) and 3 large rearrangement sequences (6%) were identified. Further scanning of affected families revealed that 49% of healthy relatives harbor identical causative mutations. This is the first report of comprehensive BRCA analysis in Iranian women with early onset breast cancer. Our findings provide valuable molecular data to support physicians as well as patients for the best decision making on disease management.

• 대한수의학회지
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• 제50권1호
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• pp.43-48
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• 2010

Animal disease surveillance system, defined as the continuous investigation of a given population to detect the occurrence of disease or infection for control purposes, has been key roles to assess the health status of an animal population and, more recently, in international trade of animal and animal products with regard to risk assessment. Especially, for a system aiming to determine whether or not a disease is present in a population sensitivity of the system should be maintained high enough not to miss an infected animal. Therefore, when planning the implementation of surveillance system a number of factors that affecting surveillance sensitivity should be taken into account. Of these parameters sample size is of important, and different approaches are used to calculate sample size, usually depending on the objective of surveillance systems. The purpose of this study was to evaluate the sensitivity of the current national serological surveillance programs for four selected bovine diseases assuming a specified sampling plan, to examine factors affecting the probability of detection, and to provide sample sizes required for achieving surveillance goal of detecting at least an infection in a given population. Our results showed that, for example, detecting low level of prevalence (0.2% for bovine tuberculosis) requires selection of all animals per typical Korean cattle farm (n = 17), and thus risk-based target surveillance for high risk groups can be an alternative strategy to increase sensitivity while not increasing overall sampling efforts. The minimum sample size required for detecting at least one positive animal was sharply increased as the disease prevalence is low. More importantly, high reliability of prevalence estimation was expected with increased sampling fraction even when zero-infected animal was identified. The effect of sample size is also discussed in terms of the maximum prevalence when zero-infected animals were identified and on the probability of failure to detect an infection. We suggest that for many serological surveillance systems, diagnostic performance of the testing method, sample size, prevalence, population size, and statistical confidence need to be considered to correctly interpret results of the system.

• 한국구조물진단유지관리공학회 논문집
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• 제26권1호
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• pp.39-48
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• 2022

지진 발생 시 방송통신서비스는 현장 구조 및 효과적인 복구 작업에 직결된다. 최근에 다양한 면진장치들이 방송통신설비의 심각한 피해를 방지하기 위하여 건물 층과 방송통신설비의 바닥부 사이에 설치하는 방법이 널리 사용되고 있다. 하지만 긴 고유주기를 가진 건물은 공진현상에 따른 예상치 못한 응답증폭으로 인하여 더 큰 피해가 발생할 수도 있다. 따라서 본 연구에서는 두 개의 면진장치를 선정 후 중층, 고층건물의 해석적, 실험적 연구를 통하여 면진장치가 바닥부에 설치된 방송통신설비의 내진 안전성을 평가를 목표로 한다. 해석적 연구를 수행하여 가진 시 중층, 고층건물 최고층의 저주파수 영역대의 동적응답을 확인하였다. 또한 해석적 연구에서 확보한 층응답을 바닥부에 면진장치가 설치된 방송통신 설비를구비하여 실증 실험을 통해 내진안정성을 평가하였다.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.