• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.185-190
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• 2021

The article provides a literary and analytical review in the following areas of search: problems and prerequisites for changes in the field of education, innovations and innovative models in education, the use of new technologies in teaching. A proposal for a business plan and accompanying documentation for a new methodology based on blockchain technologies were developed, to assess the economic efficiency of the project. The main systems of the new model were modeled on the basis of the proposed methodology, to develop a prototype based on the project documentation.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.260-263
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• 2023

Considering the growth of blockchain technology, the research aims to transform the efficiency of recommending optimal coffee suppliers within the complex supply chain network. This transformation relies on the extraction of vital transactional data and insights from stakeholders, facilitated by the dynamic interaction between the application interface (e.g., Rest API) and the blockchain network. These extracted data are then subjected to advanced data processing techniques and harnessed through machine learning methodologies to establish a robust recommendation system. This innovative approach seeks to empower users with informed decision-making abilities, thereby enhancing operational efficiency in identifying the most suitable coffee supplier for each customer. Furthermore, the research employs data visualization techniques to illustrate intricate clustering patterns generated by the K-Means algorithm, providing a visual dimension to the study's evaluation.

• 한국멀티미디어학회논문지
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• 제22권5호
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• pp.613-625
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• 2019

The rise of the Internet of Things (IoT) devices have greatly influenced many industries and one of them is healthcare where wearable devices started to track all your daily activities for better health monitoring accuracy and even down to tracking daily food intake in some cases. With the amounts of data that are being tracked and shared between from these devices, questions were raised on how to uphold user's data privacy when data is shared between these IoT devices and third party. With the blockchain platforms started to mature since its inception, the technology can be implemented according to a variety of use case scenarios. In this paper, we present a system architecture based on the healthcare system and IoT network by leveraging on multiple blockchain networks as the medium in between that should enable users to have direct authority on data accessibility of their shared data. We provide proof of concept implementation and highlight the results from our testing to show how the efficiency and scalability of the healthcare system improved without having a significant impact on the performance of the Electronic Medical Record (EMR) that mostly affected by the previous solution since these solutions directly connected to a public blockchain network and which resulted in significant delays and high cost of operation when a large amount of data or complicated functions are involved.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.136-144
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• 2022

Construction sites are characterized by dangerous situations and environments that cause fatal accidents. Potential risk detection needs to be improved by continuously monitoring site conditions. However, the current labor-intensive inspection practice has many limitations in monitoring dangerous conditions at construction sites. Computer vision technology that can quickly analyze and collect site conditions from images has been in the spotlight as a solution. Nonetheless, inspection results obtained via computer vision are still stored and managed in centralized systems vulnerable to tampering with information by the central node. Blockchain has been used as a reliable and efficient decentralized information management system. Despite its potential, only limited research has been conducted integrating computer vision and blockchain. Therefore, to solve the current safety management problems, the authors propose a framework for construction site inspection that integrates object detection and blockchain network, enabling efficient and reliable remote inspection. Object detection is applied to enable the automatic analysis of site safety conditions. As a result, the workload of safety managers can be reduced with inspection results stored and distributed reliably through the blockchain network. In addition, errors or forgery in the inspection process can be automatically prevented and verified through a smart contract. As site safety conditions are reliably shared with project participants, project participants can remotely inspect site conditions and make safety-related decisions in trust.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.170-176
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• 2024

Since the World Health Organization (WHO) has declared COVID-19 as pandemic, many researchers have started working on developing vaccine and developing AI systems to detect COVID-19 patient using Chest X-ray images. The purpose of this work is to improve the performance of pre-trained Deep convolution neural nets (DCNNs) on Chest X-ray images dataset specially COVID-19 which is developed by collecting from different sources such as GitHub, Kaggle. To improve the performance of Deep CNNs, data augmentation is used in this study. The COVID-19 dataset collected from GitHub was containing 257 images while the other two classes normal and pneumonia were having more than 500 images each class. There were two issues whike training DCNN model on this dataset, one is unbalanced and second is the data is very less. In order to handle these both issues, we performed data augmentation such as rotation, flipping to increase and balance the dataset. After data augmentation each class contains 510 images. Results show that augmentation on Chest X-ray images helps in improving accuracy. The accuracy before and after augmentation produced by our proposed architecture is 96.8% and 98.4% respectively.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.255-266
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• 2021

Most common web or application system architectures have central network. As a result, central network can be supervised and controlled in all situation. And It has the advantage of easy to manage and fast to work. However, central network have a disadvantage of weak to security and unclear. In particular, many institutions used by web system be has many problems by central network. In this paper, we proposed blokchain technology based on ethereum to resolve of problem and trading structure that arise in cental network. We propose a decentralized application based on points including cryptocurrency functions and smart contract to the advantages of blockchain with a decentralized structure. The results of the performance experiment are as follows; It has shown the advantages of reliable use and security in a variety of environments(Windows, Ubuntu, Mac).

• 스마트미디어저널
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• 제9권1호
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• pp.9-15
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• 2020

블록체인은 데이터의 투명성 및 보안성이 뛰어난 분산공유원장으로써 핵심기술인 합의 알고리즘을 통하여 참여 노드에 동일한 데이터를 순차적으로 공유할 수 있도록 한다. 이러한 블록체인 기술의 특징을 활용하고자 최근 기업 및 공공기관을 중심으로 블록체인을 적용하려는 시도가 증가하고 있다. 본 논문에서는 분산 네트워크와 같은 비동기 네트워크 환경에서 활용되는 프라이빗 블록체인의 합의 알고리즘인 PBFT를 수정하여 네트워크 통신비용 및 합의 안정성을 고려한 수정된 PBFT를 제안하였다. 수정된 PBFT는 노드 간 신뢰가 보장된 비동기 네트워크 환경의 특징을 감안하여 클라이언트의 요청 검증에 대하여 기존의 전체 참여 방식을 개선하여 2/N의 Leader(리더)를 통한 합의와 인증을 제안하였다. 해당 과정에서 발생되는 브로드캐스트 과정의 간소화를 통하여 합의를 위한 최소 노드 수 유지가 가능하였으며 네트워크 통신을 위한 효율적 비용관리가 가능하다.

• 한국빅데이터학회지
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• 제8권2호
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• pp.141-148
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• 2023

인공위성과 IoT를 연결하는 연구가 활발히 진행됨에 따라 통합된 네트워크를 구축되고, 얻어진 빅데이터들은 다양한 분야에서 활용되고 있다. 하지만 통합 네트워크 생태계는 제한된 대기 시간과 낮은 필요 전력 및 다양한 이기종 장치들의 구성 등으로 인해 심각한 보안 문제를 겪고 있다. 이를 해결하기 위해 SDN(Software Defined Networking)을 활용한 위성-IoT 네트워크를 구축하는 연구가 진행되었다. 하지만 기존 SDN에서 발생하는 보안 문제들이 여전히 존재하기 때문에 본 논문에서는 블록체인 기반 SDN 환경을 구현하여 추가적인 문제점을 해결하고자 한다. 블록체인 기반의 SDN 분산 컨트롤러를 운용하고, 블록체인 인증시스템을 통해 IoT 단말 및 노드들을 검증하도록 구현하였다. 본 논문에서는 우리가 개발한 구현의 계획을 제안하고, 향후 연구로 인공지능과의 융합과 위성-IoT 기기에서 얻을 수 있는 빅데이터들을 활용할 수 있는 방안을 제시한다.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.89-97
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• 2023

With the growth of Internet-of-Things (IoT) technologies, the number of IoT devices developers need to manage has increased exponentially. Many IoT standards have been proposed to allow those devices to communicate efficiently in day-to-day tasks. However, we lack trusted interworking entities for devices from different standards to collaborate securely. This paper proposes a blockchain platform that bridges multiple heterogeneous IoT platforms to co-exist and interwork. Specifically, we designed an interworking proxy application entity (IPE) implemented as a chaincode in Hyperledger Fabric to collect and process data coming from/to oneM2M and LWM2M architecture. The use of blockchain will guarantee network reliability and data integrity so that cross-standard communications can be audited and processed securely. Based on our evaluation, we show that the interworking between oneM2M and LWM2M through our blockchain platform is feasible. Furthermore, the proposed system can process up to 206 transactions per second with 1,000 running applications, which is about an 87% increase from the previously referenced study.