• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.199-206
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• 2021

Rhetorical relations between two text fragments are essential information and support natural language processing applications such as Question - Answering (QA) system and automatic text summarization to produce an effective outcome. Question - Answering (QA) system facilitates users to retrieve a meaningful response. There is a demand for rhetorical relation based datasets to develop such a system to interpret and respond to user requests. There are a limited number of datasets for developing an Arabic QA system. Thus, there is a lack of an effective QA system in the Arabic language. Recent research works reveal that unsupervised learning can support the QA system to reply to users queries. In this study, researchers intend to develop a rhetorical relation based dataset for implementing unsupervised learning applications. A web crawler is developed to crawl Arabic content from the web. A discourse-annotated corpus is generated using the rhetorical structural theory. A Naïve Bayes based QA system is developed to evaluate the performance of datasets. The outcome shows that the performance of the QA system is improved with proposed dataset and able to answer user queries with an appropriate response. In addition, the results on fine-grained and coarse-grained relations reveal that the dataset is highly reliable.

• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.37-43
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• 2019

This study focused on the design and development of contents using Unplugged CS method to learn computer science knowledge. With the strengthening of software education, all universities students including elementary and junior high school students are demanding knowledge of computer science centered on the computational thinking. We have developed educational contents using unplugged computing method as a way to effectively learn the concept of computer science. First, we analyze the related research of Unplugged CS theory and investigate the educational method accordingly. And we divided the areas of computer science and selected learning concepts in each area. The contents of learning were divided into basic and intermediate classes considering the level of the learner. The subject of learning contents was selected evenly in the area of computer science and it consisted of 16 basic subjects and 16 intermediate subjects based on concepts that are important in each field. In order to confirm the validity of the Unplugged CS learning topic and the development contents for computer science, we conducted expert reviews and content validity tests. As a result of the validity test, the validity of the learning topic and area selection was found to be more than .92 for the CVI value and the validity of the 32 subject learning contents was over .935 in basic level and .925 in intermediate level for the CVI values. Based on the design contents, we developed digital contents for online learning and put them in the system. We expect that this study will contribute to the understanding of computer science for all, including elementary and junior-high school students as well as students at software - oriented universities, and contribute to the spread of software education.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• 사물인터넷융복합논문지
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• 제6권4호
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• pp.65-72
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• 2020

본 논문에서는 지속적으로 급증하고 있는 안드로이드 악성 앱을 자동적으로 판별하기 위해 머신러닝 기법을 적용하여 프라이빗 블록체인을 토대로 웹 기반 DApp 시스템을 개발하였다. 공인 실험 데이터를 대상으로 안드로이드 악성 앱 판별에 96.2587% 정확도를 제공하는 최적의 머신러닝 모델을 선정하였고, 안드로이드 악성 앱에 대한 자동 판별 결과를 Hyperledger Fabric 블록체인 시스템 내에 자동적으로 기록/관리하였다. 또한 적법한 권한이 부여된 사용자만이 블록체인 시스템을 이용할 수 있도록 웹 기반의 DApp 시스템을 개발하였다. 따라서 본 논문에서 제시한 머신러닝 기반 안드로이드 악성 앱 판별 블록체인 DApp 시스템 개발을 통해 안드로이드 모바일 앱 이용 환경에서의 보안성을 더욱 향상시킬 수 있으며, 향후에 일반적인 범용 데이터를 대상으로 머신러닝과 블록체인을 결합한 보안 서비스로 발전시킬 수 있을 것으로 기대된다.

• Genomics & Informatics
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• 제2권2호
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• pp.99-106
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• 2004

In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.

• 한국멀티미디어학회논문지
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• 제25권9호
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• pp.1367-1374
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• 2022

In this paper, we propose an automatic cosmetic foundation recommendation system that suggests a good foundation product based on the user's skin color. The proposed system receives and preprocesses user images and detects skin color with OpenCV and machine learning algorithms. The system then compares the performance of the training model using XGBoost, Gradient Boost, Random Forest, and Adaptive Boost (AdaBoost), based on 550 datasets collected as essential bestsellers in the United States. Based on the comparison results, this paper implements a recommendation system using the highest performing machine learning model. As a result of the experiment, our system can effectively recommend a suitable skin color foundation. Thus, our system model is 98% accurate. Furthermore, our system can reduce the selection trials of foundations against the user's skin color. It can also save time in selecting foundations.

• 컴퓨터교육학회논문지
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• 제5권4호
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• pp.1-8
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• 2002

인터넷기술의 발달과 네트워크 환경의 구축 및 확산은 웹을 통한 교육적 적용과 활용을 더욱 증가시켰다. 학습에 참여하는 교사와 학습자는 경쟁적, 개별적, 협동적 학습구조에 따라 교육을 진행한다. 이중 경쟁적, 개별적 학습구조가 갖고 있는 지나친 경쟁의 유발이나 학습자간 협력도의 결여라는 문제점에 대한 새로운 대안으로서 협동적 학습구조에 대한 관심이 증가되었다. 이러한 배경속에서 본 연구는 협동학습의 장점과 모형을 웹에 적용하여 기존의 웹 기반 교육사이트의 질적 향상을 기한 웹 기반 협동학습 사이트를 설계 및 구현하고, 실시된 웹 기반 협동학습에 대한 실험연구를 통하여 협동학습에 참여한 학습자들의 학업성취도와 동기-태도를 향상시키고 있음을 보인다.

• 컴퓨터교육학회논문지
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• 제9권6호
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• pp.65-78
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• 2006

유비쿼터스 사회(Ubiquitous Society)의 핵심기술인 RFID(Radio Frequency Identification)를 물류, 유통, 교통, 의료 등 다양한 분야에서 개발, 적용하는 연구가 활발하게 진행되고 있다. RFID를 이용하여 u-Campus, u-Library 등 유비쿼터스 교육환경을 마련해주는 연구들은 있으나 직접적으로 학습에 적용한 연구는 아직 미미한 실정이다. 따라서 본 연구에서는 RFID 태그와 리더의 무선통신 기술을 이용하여 학습자의 위치와 상황을 인지하고 그에 부합하는 영어 상황학습 서비스를 제공하고자 한다. RFID 시스템을 이용하기 위해 본 연구에서 제안하는 RFID 미들웨어는 기존의 범용 RFID 미들웨어와 달리 PDA 기반에서 동작하는 모바일 RFID 미들웨어로 필수 API를 중심으로 최적화하여 개발하였다.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.