• Journal of Animal Science and Technology
• /
• v.63 no.6
• /
• pp.1232-1246
• /
• 2021

Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.334-342
• /
• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.1
• /
• pp.129-139
• /
• 2019

Mapping urban areas using the earth observation satellites is useful for monitoring urban expansions and measuring urban developments. In this research, the different thresholds for detecting the urban areas separately from the remote-sensing index images (normalized-difference built-up index(NDBI) and urban index(UI) images) generated from the Landsat-8 image acquired in Daegu, South Korea were evaluated through the following steps: (1) the NDBI and UI images were separately generated from the given Landsat-8 image; (2) the different thresholds (-0.4, -0.2, and 0) for detecting the urban areas separately from the NDBI and UI images were evaluated; and (3) the accuracy of each detected urban area was assessed. The experiment results showed that the threshold -0.2 had the best performance for detecting the urban areas from the NDBI image, while the threshold -0.4 had the best performance for detecting the urban areas from the UI image. Some misclassification errors, however, occurred in the areas where the bare soil areas were classified into urban areas or where the high-rise apartments were classified into other areas. In the future research, a robust methodology for detecting urban areas, including the various types of urban features, with less misclassification errors will be proposed using the satellite images. In addition, research on analyzing the pattern of urban expansion will be carried out using the urban areas detected from the multi-temporal satellite images.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3583-3597
• /
• 2020

In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.

• Korean Journal of Artificial Intelligence
• /
• v.5 no.2
• /
• pp.15-25
• /
• 2017

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

• Journal of the Korean Society of Industry Convergence
• /
• v.17 no.2
• /
• pp.61-68
• /
• 2014

Constructing structures is the basic process requiring establishment of grounds. However, cracks due to sinking and distorting ground influence directly on the safety of structural health. Vibrating wire sensor measures the crack of structure by detecting the differences of wire tensions in analogue manner. In the previous production process, the tension is adjusted manually measuring the frequency of vibrating wire. Therefore, the accuracy of a sensor was depends on the skill level of labor. In this study, the automatic tension control and fixing devise is developed to enhance both accuracy and productivity. To evaluate the performance of the vibrating wire sensor, the nonlinearity of sensor is measured. The automatic tension control and fixing devise enhances the nonlinearity of the sensor from 0.398 to 0.056%. Therefore, the accuracy of the newly proposed method is successful.

• Korean Journal of Remote Sensing
• /
• v.33 no.1
• /
• pp.89-95
• /
• 2017

Ship detection in synthetic aperture radar(SAR)imagery has long been an active research topic and has many applications. In this paper,we propose an efficient method for detecting ships from SAR imagery using filtering. This method exploits ship masking using a median filter that considers maximum ship sizes and detects ships from the reference image, to which a Non-Local means (NL-means) filter is applied for speckle de-noising and a differential image created from the difference between the reference image and the median filtered image. As the pixels of the ship in the SAR imagery have sufficiently higher values than the surrounding sea, the ship detection process is composed primarily of filtering based on this characteristic. The performance test for this method is validated using KOMPSAT-5 (Korea Multi-Purpose Satellite-5) SAR imagery. According to the accuracy assessment, the overall accuracy of the region that does not include land is 76.79%, and user accuracy is 71.31%. It is demonstrated that the proposed detection method is suitable to detect ships in SAR imagery and enables us to detect ships more easily and efficiently.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.12
• /
• pp.1159-1166
• /
• 2010

Today, detecting the location of moving object has been traced as various methods in our world. In this paper, we preset the system to improve the estimation accuracy utilizing detail localization using radar sensor based on WSN and situational awareness for a calibration (context aware) database, Rail concept. A variety of existing location tracking method has a problem with receiving of data and accuracy as tracking methodology, and since these located data are the only data to be collected for location tracing, the context aware or monitering as the surrounding environment is limited. So, in this paper, we enhanced the distance aware accuracy using radar sensor utilizing the Doppler effect among the distance measuring method, estimated the location using the Triangulation algorithm. Also, since we composed the environment data(temperature, illuminancem, humidity, noise) to entry of the database, it can be utilized in location-based service according to the later action information inference and positive context decision. In order to verify the validity of the suggested method, we give a few random situation and built test bed of designed node, and over the various test we proved the utilizing the context information through route tracking of moving and data processing.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
• /
• v.25 no.1
• /
• pp.99-107
• /
• 1995

The aim of this study was to evaluate clinician's detectability in the diagnosis of bone loss in the bifurcation of mandibular molars on periapical radiographs and Digital images. Periapical radiographs were obtained of the first molars in 2 dry mandibles after preparation of bony defects corresponding to degree I, degree II and degree III buccal furcation involvements. The radiographs were randomly presented to 39 clinicians(1 oral radiologist, 4 periodontist, 34 general dentists) who were asked to determine the presence or absence of bone loss. Periapical films were digitized with a TV camera. Digital images were assessed by 15 clinicians(1 oral radiologist, 4 periodontist, 10 general dentists). I. the overall diagnostic accuracy of Digital images for detection of bone loss in the bifurcation of mandibular molars was higher than that of the periapical radiographs. 2. the largest increase in diagnostic accuracy was found between lesion grade II and III on both radiographs and Digital images(P<0.05). 3. there was no significant difference between the standard state and the controlled contrast state on Digital images. 4. the overall diagnostic accuracy of I radiologist and 4 periodontists was better than that of the general dentists for detecting bifurcation involvements.

• Journal of Information Processing Systems
• /
• v.14 no.5
• /
• pp.1167-1175
• /
• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.