• Annual Conference of KIPS
• /
• 2019.05a
• /
• pp.419-422
• /
• 2019

The recent increase in single-person households and family income has led to an increase in the number of pet owners. However, due to the owners' difficulty to communicate with them for 24 hours, pets, and especially dogs, tend to display unwanted behavior that can be harmful to themselves and their environment when left alone. Therefore, detecting those behaviors when the owner is absent is necessary to suppress them and prevent any damage. In this paper, we propose a camera-based system that detects a set of normal and unwanted behaviors using deep learning algorithms to monitor dogs when left alone at home. The frames collected from the camera are arranged into sequences of RGB frames and their corresponding optical flow sequences, and then features are extracted from each data flow using pre-trained VGG-16 models. The extracted features from each sequence are concatenated and input to a bi-directional LSTM network that classifies the dog action into one of the targeted classes. The experimental results show that our method achieves a good performance exceeding 0.9 in precision, recall and f-1 score.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3668-3684
• /
• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• The Journal of Korea Robotics Society
• /
• v.16 no.3
• /
• pp.260-269
• /
• 2021

To verify performance or conduct experiments using actual robots, a lot of costs are needed such as robot hardware, experimental space, and time. Therefore, a simulation environment is an essential tool in robotics research. In this paper, we develop the HUMIC simulator using ROS and Gazebo. HUMIC is a humanoid robot, which is developed by HCIR Lab., for human-robot interaction and an upper body of HUMIC is similar to humans with a head, body, waist, arms, and hands. The Gazebo is an open-source three-dimensional robot simulator that provides the ability to simulate robots accurately and efficiently along with simulated indoor and outdoor environments. We develop a GUI for users to easily simulate and manipulate the HUMIC simulator. Moreover, we open the developed HUMIC simulator and GUI for other robotics researchers to use. We test the developed HUMIC simulator for object detection and reinforcement learning-based navigation tasks successfully. As a further study, we plan to develop robot behavior intelligence based on reinforcement learning algorithms using the developed simulator, and then apply it to the real robot.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3723-3737
• /
• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.29 no.1
• /
• pp.31-60
• /
• 2023

Purpose: This study explored the influencing factors on suicide-related behaviors (ideation, plans, and attempts) focusing on mental health problems (anxiety, depression, and loneliness) among Korean adolescents. Methods: A secondary analysis was conducted with data from the 16th Korean Youth Health Behavior Survey collected from in 2020 by the Korea Centers for Disease Control and Prevention. Chi-square tests and multivariate logistic regression analyses were performed. Results: After the adjustment of demographic characteristics and health risk behaviors, the influences of mental health problems on suicidal ideation, plans, and attempts showed the anxiety odds ratio (OR) for severe anxiety vs. minimal (OR 4.65, 4.67, and 3.75), depression (OR 4.27, 3.69, and 4.49), loneliness (OR 2.18, 1.96, and 1.96). Health risk behaviors (violence experience, drug use, stress, smoking, and drinking alcohol) and demographic variables (gender, school record, and socioeconomic status) were also significantly associated with suicide-related behaviors. Conclusion: Anxiety, depression, and loneliness were strong predictors of suicide-related behaviors. Early detection of suicide risks through screening for comprehensive mental health problems was recommended. Suicide prevention that considers the risk factors, including mental health problems and other risk factors, needs to be developed and implemented to reduce suicide risks among adolescents.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.69-78
• /
• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• Journal of Technologic Dentistry
• /
• v.45 no.1
• /
• pp.14-20
• /
• 2023

Purpose: This study was conducted to provide basic data for a health promotion program by analyzing the effects of high school students' mental health-related factors on oral symptom experiences. Methods: This study included 24,833 high school students who participated in the screening and health survey in the "17th (2021) Adolescent Health Behavior Survey." SPSS software (SPSS Statistics ver. 21.0; IBM) was used for statistical analysis. Multiple sample logistic regression analysis was performed. The significance level was set to 0.05. Results: The result of the analysis on the effect of mental health revealed that oral symptom experience was low in students without depression and suicidal thoughts. Oral symptom experience was high in students with stress perception. Additionally, the experience of oral symptoms was low when there was sufficient subjective sleep. Conclusion: Therefore, it is necessary to develop a customized oral health education program for early detection of oral symptoms and oral health promotion in high school students. Furthermore, it suggests the need for strategies and continuous oral health guidance to practice proper oral health habits to maintain healthy oral conditions.

• Korean Journal of Materials Research
• /
• v.33 no.5
• /
• pp.195-204
• /
• 2023

Micro-electronic gas sensor devices were developed for the detection of carbon monoxide (CO), nitrogen oxides (NO_x), ammonia (NH₃), and formaldehyde (HCHO), as well as binary mixed-gas systems. Four gas sensing materials for different target gases, Pd-SnO₂ for CO, In₂O₃ for NO_x, Ru-WO₃ for NH₃, and SnO₂-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were then fabricated using a micro sensor platform. The gas sensing behavior and sensor response to the gas mixture were examined for six mixed gas systems using the experimental data in MEMS gas sensor arrays in sole gases and their mixtures. The gas sensing behavior with the mixed gas system suggests that specific adsorption and selective activation of the adsorption sites might occur in gas mixtures, and allow selectivity for the adsorption of a particular gas. The careful pattern recognition of sensing data obtained by the sensor array made it possible to distinguish a gas species from a gas mixture and to measure its concentration.

• Journal of Preventive Medicine and Public Health
• /
• v.55 no.2
• /
• pp.173-181
• /
• 2022

Objectives: Anxiety disorder is among the most prevalent mental illnesses among adolescents. Early detection and proper treatment are important for preventing sequelae such as suicide and substance use disorder. Studies have suggested that sleep duration is associated with anxiety disorder in adolescents. In the present study, we investigated the association between sleep quality and anxiety in a nationally representative sample of Korean adolescents. Methods: This cross-sectional study was conducted using data from the 2020 Korea Youth Risk Behavior Web-based Survey. The Generalized Anxiety Disorder-7 questionnaire was used to evaluate anxiety. The chi-square test was used to investigate and compare the general characteristics of the study population, and multiple logistic regression analysis was used to analyze the relationship between sleep quality and anxiety. Results: In both sexes, anxiety was highly prevalent in participants with poor sleep quality (adjusted odds ratio [aOR], 1.56; 95% confidence interval [CI], 1.43 to 1.71 in boys; aOR, 1.30; 95% CI, 1.19 to 1.42 in girls). Regardless of sleep duration, participants with poor sleep quality showed a high aOR for anxiety. Conclusions: This study identified a consistent relationship between sleep quality and anxiety in Korean adolescents regardless of sleep duration.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.6
• /
• pp.1657-1673
• /
• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.