• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1250-1260
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• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• Journal of KIISE
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• v.44 no.11
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• pp.1231-1235
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• 2017

Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.47-60
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• 2015

In this paper, we propose a simple and useful method using a port role to visualize the network attacks. The port role defines the behavior of the port from the source and destination port number of network session. Based on the port role, the port provides the brief security features of each node as an attacker, a victim, a server, and a normal host. We have automatically classified and identified the type of node based on the port role and security features. We detected and visualized the network attacks using these features of the node by the port role. In addition, we are intended to solve the problems with existing visualization technologies which are the reflection problem caused an undirected network session and the problem caused decreasing of distinct appearance when occurs a large amount of the sessions. The proposed method monitors anomalies occurring in an entire network and displays detailed information of the attacker, victim, server, and hosts. In addition, by providing a categorized analysis of network attacks, this method can more precisely detect and distinguish them from normal sessions.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.77-84
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• 2015

The study is to investigate which features are perceived by Social Network Service(SNS) users as the most required one to further boost the usage of service, and to examine the perception of these features of SNS sites varies according to their demographic and service usage characteristics. The study also is to suggest a few of research propositions on the relationships between required features of SNS sites and characteristics of SNS users, based on statistical analyses. To accomplish these research purposes, the study defined characteristics of SNS users including demographic(gender, age) and service usage one(start time of service usage, service usage place), and required features of SNS sites(system, service, information, emotion) based on the literature review of SNS. The results show, based on the statistical analyses using survey questionnaire on Korean and Chinese SNS users, that there are differences in perception of required features of SNS sites among the respondents grouped by age, start time of service usage, service usage place. Finally, the study proposed three research propositions, based on the analysis result, that could be used in SNS related researches in the future.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.25-30
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• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.588-590
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• 1997

In this paper, a highly reliable communication network for DCS in nuclear power plant is designed. The structure and characteristics of DCS in nuclear power plant is briefly explained. The features needed for a communication network for DCS in nuclear power plant is described. According to the abovo features, the layer structure of the communication network is determined and each layer is designed in detail.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.816-818
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• 1998

In this paper, a highly reliable communication network for DCS (Distributed Control System) in nuclear power plant is designed. The structure and characteristics of DCS in nuclear power plant is briefly explained. The features needed for a communication network for DCS in nuclear power plant is described. According to the above features, a layer structure for the communication network is determined and each layer is designed in detail. Accuracy of the model was evaluated by computer simulation.