• Clinics in Shoulder and Elbow
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• v.10 no.2
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• pp.212-219
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• 2007

Purpose: To determine the causes of the surgical treatment results in glenoid fracture by a retrospective analysis. Materials and methods: From March 1999 to February 2004, 9 patients who underwent an open reduction due to a glenoid fracture were reviewed. The modified Ideberg classification was used. There were 1, 3, 2, 1 and 2 cases of modified Ideberg type I, II, III, V, and VI, respectively. The internal fixators were a reconstruction plate, a small plate, a one-third tubular plate, a small screw, and a cannulated screw in 6, 1, 3, 3 and 1 case, respectively. The constant score and Adam's functional assessment method were used to evaluate the postoperative shoulder function. Results: The average time for fracture union was 7 weeks. The functional assessment was excellent in 4 cases, good in 3 cases, and fair in 2 cases. There were two complications related to surgery; articular screw encroachment, and inferior glenoid bone resorption without instability. Conclusion: A glenoid fracture with glenohumeral instability or displaced that was treated by open surgery showed good clinical results. Moreover, the more comminuted fracture had a lower functional score.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• Korean Journal of Veterinary Service
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• v.42 no.4
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• pp.217-225
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• 2019

To detect Single nucleotide polymorphisms (SNP) markers associated with Bovine viral diarrhea virus (BVDV) and Bovine respiratory syncytial virus (BRSV) S/P ratio in Korean Holstein dairy cattle, Genome-wide association study (GWAS) was performed using Illumina BovineSNP50 Beadchip. The number of phenotype data and genotype data were 107, and 294. respectively. Phenotype data were collected for four periods (0 week, 1 week, 4 week, 24 week) after having vaccinated (0 week no vaccinated period). A total of 36,257 SNPs was remained after quality control had been done by PLINK. The result of GWAS showed 6 SNP markers (BTB-01704243, BTB-01594395, ARS-BFGL-NGS-118070, ARS-BFGL-NGS-111365, BTA-65410-no-rs, Hapmap38331-BTA-61256) under BVDV and 4 SNP markers (ARS-BFGL-NGS-109861, Hapmap53701-rs29017064, ARS-BFGL-NGS-71055, BTA-11232-no-rs) under BRSV. And also, 10 candidate genes found through 10 SNP markers (TBX18, CEP162, PAFAH1B1, METTL16, BRCA1, RND2, POLK, ENSBTAG00000051724, ADAM18, NRG3).

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.99-108
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• 2023

This study's goal is to investigate the effects of board size, the number of annual board meetings, the profitability of the company, and the audit Committee on the disclosure of climate change in Saudi companies listed on the stock exchange. It was conducted to evaluate affecting some factors on carbon emissions by the regression model. The study uses the content analysis method. Data was collected from the annual and sustainability reports, and the platform database Refinitiv, an LSEG (London Stock Exchange Group Company) for the period 2018 to 2021. The study sample is 51 companies. The study findings showed Saudi Arabia saw its first significant overall drop in CO2 emissions with a 22.61 MtCO2 decline (3.93%) in 2018. The study revealed a positive connection between the size of the director's board, and the disclosure of carbon emissions in Saudi firms listed on the stock market. While other factors are not related to the number of director's board meetings, the audit committee, and the profitability of the company on the disclosure of carbon emissions in the Saudi companies listed on the stock exchange.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.115-128
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• 2023

Deep learning has been incorporating various optimization techniques motivated by new pragmatic optimizing algorithm advancements and their usage has a central role in Machine learning. In recent past, new avatars of various optimizers are being put into practice and their suitability and applicability has been reported on various domains. The resurgence of novelty starts from Stochastic Gradient Descent to convex and non-convex and derivative-free approaches. In the contemporary of these horizons of optimizers, choosing a best-fit or appropriate optimizer is an important consideration in deep learning theme as these working-horse engines determines the final performance predicted by the model. Moreover with increasing number of deep layers tantamount higher complexity with hyper-parameter tuning and consequently need to delve for a befitting optimizer. We empirically examine most popular and widely used optimizers on various data sets and networks-like MNIST and GAN plus others. The pragmatic comparison focuses on their similarities, differences and possibilities of their suitability for a given application. Additionally, the recent optimizer variants are highlighted with their subtlety. The article emphasizes on their critical role and pinpoints buttress options while choosing among them.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.23-42
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• 2013

Congress enacted the sex offender registration and notification act in order to prevent sexual offenses and protect public safety in the U.S.. Namely, in 2006, the Jacob Wetterling Act and Megan's Law were integrated into the Adam Walsh Child Protection and Safety Act as a comprehensive sex offender supervision and management scheme. The AWA aims to eliminate loopholes and gaps formed by inconsistent state laws and statutes as well as to provide the federal standards for sex offender registration and notification. However, the AWA contains over-inclusive sex offender registration requirements and punishments. For this reason, the implementation of the AWA may cause problems for states, sex offenders, and citizens, both as taxpayers and as beneficiaries of the AWA. Therefore, the AWA that does not differentiate between violent and non-violent offenders should be reformed to allow law enforcement officials to focus on sex offenders convicted of violent and heinous crimes. That is, the AWA should not apply to sex offenders who are not dangerous, not likely to recidivate, and who committed non-violent crimes. In addition, because the AWA requires juvenile offenders to registrate on public notification forums, it may result in a greater risk to community safety and potential risk of reoffense. Accordingly, juvenile offenders convicted of non-violent sex offenses and not likely to recidivate will be provided appropriate treatments to be rehabilitated as members of community.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.1
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• pp.36-42
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• 2016

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to $2^*SNP$ effect.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.