• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4011-4027
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• 2021

Cloud Computing has emerged as an extensively used technology not only in the IT sector but almost in all sectors. As the nature of the cloud is distributed and dynamic, the jeopardies present in the current implementations of virtualization, numerous security threats and attacks have been reported. Considering the potent architecture and the system complexity, it is indispensable to adopt fundamentals. This paper proposes a secure authentication and data sharing scheme for providing security to the cloud data. An efficient IPSO-KELM is proposed for detecting the malicious behaviour of the user. Initially, the proposed method starts with the authentication phase of the data sender. After authentication, the sender sends the data to the cloud, and the IPSO-KELM identifies if the received data from the sender is an attacked one or normal data i.e. the algorithm identifies if the data is received from a malicious sender or authenticated sender. If the data received from the sender is identified to be normal data, then the data is securely shared with the data receiver using SHA256-RSA algorithm. The upshot of the proposed method are scrutinized by identifying the dissimilarities with the other existing techniques to confirm that the proposed IPSO-KELM and SHA256-RSA works well for malicious user detection and secure data sharing in the cloud.

• 한국산업정보학회논문지
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• 제27권6호
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• pp.77-93
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• 2022

본 연구에서 대설의 직접, 간접적인 피해에 영향받는 요소들에 대해 지역적 특성을 반영해 위험도를 분석하는 알고리즘을 제시한다. 229개의 지역별로 대설피해의 영향을 받는 요소들을 영향변수로 선정하고 피해액과의 관계를 통해 민감도라는 개념을 도출한다. 기상 상태(적설량, 습도, 기온)와 민감도를 독립 변수로 설정하고 독립 변수의 변화에 따라 도출된 위험도를 종속변수로 설정해 머신러닝(XGBoost) 알고리즘을 이용한 대설피해 위험도 예측 모델을 개발했다.

• Geomechanics and Engineering
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• 제32권3호
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• pp.271-291
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• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.

• Steel and Composite Structures
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• 제47권6호
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.131-140
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• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.417-419
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• 2023

Hypertension is one of the leading causes of mortality worldwide. In recent years, the incidence of hypertension has increased dramatically, not only among the elderly but also among young people. In this regard, the use of machine-learning methods to diagnose the causes of hypertension has increased in recent years. In this study, we improved the prediction of hypertension detection using Mahalanobis distance-based multivariate outlier removal using the KNHANES database from the Korean national health data and the COVID-19 dataset from Kaggle. This study was divided into two modules. Initially, the data preprocessing step used merged datasets and decision-tree classifier-based feature selection. The next module applies a predictive analysis step to remove multivariate outliers using the Mahalanobis distance from the experimental dataset and makes a prediction of hypertension. In this study, we compared the accuracy of each classification model. The best results showed that the proposed MAH_RF algorithm had an accuracy of 82.66%. The proposed method can be used not only for hypertension but also for the detection of various diseases such as stroke and cardiovascular disease.

• 한국인터넷방송통신학회논문지
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• 제24권3호
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• pp.63-67
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• 2024

최근 스마트 물류, 핀테크, 엔터테인먼트 등 다양한 산업 분야의 인공지능 워크로드들이 클라우드 상에서 실행되고 있다. 본 논문은 이기종 GPU 클러스터로 구성된 다중 테넌트 클라우드 시스템에서 다양한 인공지능 워크로드가 실행될 때 발생하는 스케줄링 문제를 다룬다. 전통적인 스케줄링은 이러한 환경에서 GPU 이용률을 크게 저하시켜 시스템의 성능을 떨어뜨린다. 이러한 문제를 해결하기 위해, 본 논문에서는 유전 알고리즘 기반의 최적화 기법을 사용하는 새로운 스케줄링 접근 방식을 제안하고, 이를 프로세스 기반 이벤트 시뮬레이션 프레임워크에 구현하였다. 알리바바의 MLaaS 클러스터에서 수집한 광범위한 인공지능 작업들의 트레이스를 재현하는 실험을 통해 제안하는 스케줄링이 기존 스케줄링에 비해 GPU 이용률을 크게 개선함을 확인하였다.

• Steel and Composite Structures
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• 제50권4호
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.67-76
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• 2024

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

• 한국콘텐츠학회논문지
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• 제19권7호
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• pp.384-392
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• 2019

4차 산업혁명 시대의 폭발적인 정보의 양을 다루는 빅데이터 관련 연구는 현재 활발히 진행되고 있다. 빅데이터는 머신러닝, 즉 딥러닝의 학습데이터가 되는 광범위한 데이터로 인공지능의 발달을 촉진하는 필수 요소이다. 다양한 분야에서 빅데이터의 활용은 유의미한 결과를 가져오고 있으며, 특히 문화예술 분야에서의 활용도 주목해 볼 필요가 있다. 이에 본 논문은 영상콘텐츠를 중심으로 문화예술 산업에서 빅데이터의 활용 사례를 알아보았다. 주목한 점은 문화예술 콘텐츠의 유통뿐만 아니라 제작단계까지 빅데이터가 활용되고 있는 점이다. 특히 미국의 Netflix가 OTT사업으로 어떤 성과와 변화를 가져왔는지를 먼저 알아보고 국내의 OTT 사업체의 현황도 함께 분석하였다. 그 후 Netflix가 축적된 고객의 데이터를 통해 딥러닝 방식의 '시네매치', 즉 흥행 예측 알고리즘을 활용하여 제작/유통한 'House of Cards'의 성공 사례를 분석하였다. 그 후 문화예술 콘텐츠 전문가를 대상으로 FGI(Focus Group Interview)를 진행하였다. 이를 통해 국내 문화예술 산업에서 빅테이터의 향후 활용 전망을 기술적인 측면, 창의적인 측면, 윤리적인 측면으로 나눠서 고찰하였다.