• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.211-217
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• 2023

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.129-132
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• 2022

Super-resolution is the process of converting a low-quality image into a high-quality image. This study was conducted using ESPCN. In a super-resolution deep neural network, different quality images can be output even when receiving the same input data according to the activation function that determines the weight when passing through each node. Therefore, the purpose of this study is to find the most suitable activation function for super-resolution by applying the activation functions ReLU, ELU, and Swish and compare the quality of the output image for the same input images. The CelebaA Dataset was used as the dataset. Images were cut into a square during the pre-processing process then the image quality was lowered. The degraded image was used as the input image and the original image was used for evaluation. As a result, ELU and swish took a long time to train compared to ReLU, which is mainly used for machine learning but showed better performance.

• Phonetics and Speech Sciences
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• v.14 no.4
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• pp.35-43
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• 2022

This paper studies how to improve the identification rate of laryngeal disability speech data by convolutional neural network (CNN) and machine learning ensemble learning methods. In general, the number of laryngeal dysfunction speech data is small, so even if identifiers are constructed by statistical methods, the phenomenon caused by overfitting depending on the training method can lead to a decrease the identification rate when exposed to external data. In this work, we try to combine results derived from CNN models and machine learning models with various accuracy in a multi-voting manner to ensure improved classification efficiency compared to the original trained models. The Pusan National University Hospital (PNUH) dataset was used to train and validate algorithms. The dataset contains normal voice and voice data of benign and malignant tumors. In the experiment, an attempt was made to distinguish between normal and benign tumors and malignant tumors. As a result of the experiment, the random forest method was found to be the best ensemble method and showed an identification rate of 85%.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.1-9
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• 2023

The practical courses are considered as a model for the courses taught by the student of the Department of Physical Education at different levels of study, during which he employs his mental, physical and skill abilities to understand and master the motor skills and develop his physical abilities to be able to master them and later teach and train them, so this study was conducted with the aim of identifying the perceived mental image For the practical courses of the students of the Department of Physical Education at Umm Al-Qura University, by designing a scale for the perceived mental image of the practical courses, and identifying the percentages and the extent of their prevalence for each of the positive mental image, the nonperceived mental image, and the negative mental image of the practical courses among the students of the Department of Physical Education at Umm Al-Qura University, The researcher used the descriptive approach from the survey studies by designing a measure of the perceived mental image on a sample of (175) students, and they were chosen by the intentional method from the fourth level students who studied all the practical courses in the department, whether for the first or second semester of the academic year 2021 /2022. Data using frequencies, percentages and the test of significance of the ratio, and one of the most important results was the validity of the scale used in measuring the mental image perceived by students of the Department of Physical Education about practical courses. Realizing a positive mental image that is statistically significant about the practical courses of (53.20%) of the students of the Department of Physical Education, the sample of this study. And realizing a positive mental image that is statistically significant for students about the axes of the nature of studying practical courses, their abilities in practical performance, the method of implementing lectures, the lecturer, and their evaluation methods. The mental image of the student, and taking appropriate measures to develop the practical courses and academic programs, applying similar studies to measure the mental image of the department's graduates on the specialized tracks in the Department of Physical Education, reviewing the number of hours for some practical courses so that they are not less than two hours for all practical courses.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.155-161
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• 2023

Vertigo refers to a state in which awareness related to the location, posture, movement, etc. of a helicopter is insufficient in space. It is easy to fall into flight illusion when flying in dense fog or night flight, and even if it has a wide field of view, it can be caused by visual causes such as cloud shapes, wind conditions, conditions of ground objects, and sensory causes such as changes in air posture or gravitational acceleration. The design and program of the motion system are studied that applied a six-axis motion system to a conventional commercial flight simulator program for pilot training, depending on the specificity of helicopter flight training that requires perception and sensitivity. Using the motion-based helicopter simulator produced in this study to train pilots, it is expected to have a positive effect in prevent of vertigo, where high performance could not be confirmed in the previously used visual-based simulation training device.

• Smart Media Journal
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• v.11 no.1
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• pp.58-66
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• 2022

As Internet technology develops, SNS users are increasing. As SNS becomes popular, SNS-type crimes using the influence and anonymity of social networks are increasing day by day. In this paper, we propose a fake account classification method that applies machine learning and deep learning to statistical and image data for fake accounts classification. SNS account data used for training was collected by itself, and the collected data is based on statistical data and image data. In the case of statistical data, machine learning and multi-layer perceptron were employed to train. Furthermore in the case of image data, a convolutional neural network (CNN) was utilized. Accordingly, it was confirmed that the overall performance of account classification was significantly meaningful.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.3-47
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• 2021

Recently a multilayer spectral inversion (MLSI) model has been proposed to infer the physical parameters of plasmas in the solar chromosphere. The inversion solves a three-layer radiative transfer model using the strong absorption line profiles, H alpha and Ca II 8542 Å, taken by the Fast Imaging Solar Spectrograph (FISS). The model successfully provides the physical plasma parameters, such as source functions, Doppler velocities, and Doppler widths in the layers of the photosphere to the chromosphere. However, it is quite expensive to apply the MLSI to a huge number of line profiles. For example, the calculating time is an hour to several hours depending on the size of the scan raster. We apply deep neural network (DNN) to the inversion code to reduce the cost of calculating the physical parameters. We train the models using pairs of absorption line profiles from FISS and their 13 physical parameters (source functions, Doppler velocities, Doppler widths in the chromosphere, and the pre-determined parameters for the photosphere) calculated from the spectral inversion code for 49 scan rasters (~2,000,000 dataset) including quiet and active regions. We use fully connected dense layers for training the model. In addition, we utilize a skip connection to avoid a problem of vanishing gradients. We evaluate the model by comparing the pairs of absorption line profiles and their inverted physical parameters from other quiet and active regions. Our result shows that the deep learning model successfully reproduces physical parameter maps of a scan raster observation per second within 15% of mean absolute percentage error and the mean squared error of 0.3 to 0.003 depending on the parameters. Taking this advantage of high performance of the deep learning model, we plan to provide the physical parameter maps from the FISS observations to understand the chromospheric plasma conditions in various solar features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.549-555
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• 2023

Jacking force loss management inside the PSC Box girder of a common high-speed railway is a very important feature in girder performance, and requires detailed management during the maintenance of the girder. This study aimed to analyze the timing of re-tension prediction of PSC Box girder based on the reduction level of the packing force inside the girder and the results of the tension loss measured without the train load test. As a result of predicting the timing of re-tension according to the level of tension reduction of the PSC Box Girder, the Jacking Force Loss curve was gently analyzed before the structure reached 17 years after confirmed completion, and 17 years later, it was found that the jacking force loss curve progressed rapidly. The results confirmed that the tension of the structure decreases with the service life increase, but considerably decreases as the structure ages. Therefore, more data and research on tension loss of facilities over 20 years are much required.