• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Journal of Christian Education in Korea
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• v.78
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• pp.57-85
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• 2024

Purpose of the study: In order to utilize artificial intelligence-generated AI in educational ministry, this study analyzes the concept of artificial intelligence and generative AI and the educational theological aspects of educational ministry to find ways to efficiently utilize artificial intelligence ChatGPT in educational ministry. Contents and methods of the study: The contents of this study are. First, the contents of this study were analyzed by dividing the concepts of artificial intelligence and generative AI into the concept of artificial intelligence, types of artificial intelligence, and generative language model AI ChatGPT. Second, the educational theological analysis of educational ministry was divided into the concept of educational ministry, the goals of educational ministry, the content of educational ministry, and the direction of educational ministry in the era of artificial intelligence. Third, the plan to use artificial intelligence ChatGPT in educational ministry is to provide tools for writing sermon manuscripts, preparation tools for worship and prayer, and church education, focusing on the five functions of the early church community. It was analyzed by dividing it into tools for teaching, tools for teaching materials for believers, and tools for serving and volunteering. Conclusion and Recommendation: The conclusion of this study is that, first, when writing sermon manuscripts through artificial intelligence ChatGPT, high-quality sermon manuscripts can be written through the preacher's spirituality, faith, and insight. Second, through artificial intelligence ChatGPT, you can efficiently design and plan worship services and prepare services that serve the congregation objectively through various scenarios. Third, by using artificial intelligence ChatGPT in church education, it can be used while maintaining a complementary relationship with teachers through collaboration with human and artificial intelligence teachers. Fourth, through artificial intelligence ChatGPT, we provide a program that allows members of the church community to share spiritual fellowship, a plan to meet the needs of church members and strengthen interdependence, and an attitude of actively welcoming new people and respecting diversity. It provides useful materials that can play an important role in giving, loving, serving, and growing together in the love of Christ. Lastly, through artificial intelligence ChatGPT, we are seeking ways to provide various information about volunteer activities, learning support for children and youth in the community, mentoring-related programs, and playing a leading role in forming a village community in the local community.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.1-15
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• 2020

Today's healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook 'Basic Mathematics for Artificial Intelligence' by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.3
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• pp.76-83
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• 2000

인공지능(artificial intelligence)은 컴퓨터를 보다 지능적으로 만들기 위한 추론과 학습 방법에 관해 연구하는 컴퓨터 과학의 한 분야다. 특히 기계학습(machine learning)은 지식을 자동으로 획득하기 위한 원리와 기법을 개발하는 인공지능의 한 분야로서 생물정보학의 많은 중요한 문제 해결을 위한 매우 유용한 도구가 되고 있다.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.7-17
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• 2023

Artificial Intelligence (AI) technology has already penetrated deeply into our daily lives, and we live with the convenience of it anytime, anywhere, and sometimes even without us noticing it. However, because AI is imitative intelligence based on human Intelligence, it inevitably has both good and evil sides of humans, which is why ethical principles are essential. The starting point of this study is the AI principles for companies or organizations to develop products. Since the late 2010s, studies on ethics and principles of AI have been actively published. This study focused on AI principles declared by global companies currently developing various products through AI technology. So, we surveyed the AI principles of the Global 500 companies by market capitalization at a given specific time and collected the AI principles explicitly declared by 46 of them. AI analysis technology primarily analyzed this text data, especially LDA (Latent Dirichlet Allocation) topic modeling, which belongs to Machine Learning (ML) analysis technology. Then, we conducted a Delphi technique to reach a meaningful consensus by presenting the primary analysis results. We expect to provide meaningful guidelines in AI-related government policy establishment, corporate ethics declarations, and academic research, where debates on AI ethics and principles often occur recently based on the results of our study.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.576-578
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• 2023

네트워크 트래픽 연구는 오랜 기간 지속되어 왔으며, 구현이 비교적 간단하고 높은 정확도를 가지는 기존의 분류 방식들이 오랫동안 사용되어왔다. 그러나 네트워크 기술과 암호화 기술의 발달로 기존의 분류 방식들은 더 이상 분류 결과에 대한 신뢰성을 보장할 수 없으며, 이에 따라 새로운 분류 방식의 필요성이 대두되었다. 최근 머신러닝과 딥러닝을 네트워크 트래픽 분류에 적용하는 연구가 활발히 이루어지고 있으며 획기적인 모델들이 많이 제안되었고, 그 분류 성능 또한 입증되었다. 그러나 여전히 여러 가지 극복해야 할 문제점은 남아있으며 이러한 문제점을 해결하기 위한 연구가 앞으로도 계속 진행될 것으로 보인다. 본 논문은 머신러닝과 딥러닝을 이용한 네트워크 트래픽 분류 연구 동향에 대해 살펴보고 이러한 연구들이 가지는 문제점을 짚고 넘어가며 앞으로의 네트워크 트래픽 분류 연구의 방향성에 대해 이야기 하고자 한다.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.