• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.947-971
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• 2022

The Electronic Medical Record (EMR) is a valuable source of medical data intelligence in e-health systems. The watermarking techniques have been used to authenticate the owner and protect the EMR from illegal copying. The existing distributive strategies, successfully operated to secure the EMR, are found to be inadequate. Blockchain technology, mainly, is employed by a sharing database that allows the digital crypto-currency. It rapidly leads to the magnified expectations acme. In this excitement, the use of consortium adopting the technology based on Blockchain, in the EMR structure, is found improving. This type of consortium adds an immutable share with a translucent record of the entire business and it is accomplished with responsibility, along with faith and transparency. The combination of watermarking and Blockchain technology provides a singular chance to promote a secured, trustworthy electronic documents administration to share with the e-records system. The authors, in this article, present their views on consortium Blockchain technology which is incorporated in the EMR system. The ledger, used for the distribution of the block structure, has team healthcare models based on dissimilar multiple image watermarking techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1162-1181
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• 2023

Tourism flow is not only the manifestation of tourists' special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.641-643
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• 2021

The Internet of Things (IoT) connects all markets and industries, enabling new business models for a variety of services and service providers. The Internet of Medical Things (IoMT) not only accelerates medical advances, but also enables treatment with a more human approach. In addition, it improves treatment methods and quality of precision medical care through data, enables timely treatment, and improves operational productivity of medical institutions through a simplified workflow. However, since the medical field directly affects human health and life, securing security has become an issue above all else, and is a target for hackers trying to exploit it. Therefore, in this study, IoMT technology and security threats and countermeasures in the medical field are analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2822-2843
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• 2023

All aspects of human life have become increasingly dependent on data in the last few decades. The development of several applications causes an enormous issue on data volume in current years. This information must be safeguarded and kept in safe locations. Massive volumes of data have been safely stored with cloud computing. This technology is developing rapidly because of its immense potentials. As a result, protecting data and the procedures to be handled from attackers has become a top priority in order to maintain its integrity, confidentiality, protection, and privacy. Therefore, it is important to implement the appropriate security measures in order to prevent security breaches and vulnerabilities. An improved version of Modular Encryption Standard (IMES) based on layered modelling of safety mechanisms is the major focus of this paper's research work. Key generation in IMES is done using a logistic map, which estimates the values of the input data. The performance analysis demonstrates that proposed work performs better than commonly used algorithms against cloud security in terms of higher performance and additional qualitative security features. The results prove that the proposed IMES has 0.015s of processing time, where existing models have 0.017s to 0.022s of processing time for a file size of 256KB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.77-83
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• 2022

In the era of the 4th industrial revolution, various universities' corporate field application education models are being presented. In particular, along with new teaching methods, various educational models for customized education of many companies are being studied, increasing their usability. Research on project-oriented teaching methods for competencies required in the field of business is the most developed field in recent years. In this study, we intend to propose a case-oriented curriculum model that applies the project-oriented teaching method to the requirements of these companies. In particular, we design an industry-oriented curriculum model through a companycustomized education model for blockchain and web developers, and suggest the direction of development focusing on examples of the operation process. The model through this case was designed and operated as a curriculum model suitable for the field through in-depth interviews with industries, etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.7-22
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• 2014

By the publications of RDF and OWL, the Semantic Web is confirmed as a technology through which information in the Internet can be processed by machines. The focus of the Semantic Web study after then has moved to how to provide more useful information to users for their decision making beyond simple use of the structured data in ontologies. SWRL that makes logical inference possible by rules, and SWCL that formulates constraints under the Semantic Web environment are some of many efforts toward the achievement of that goal. Constraint represents a connection or a relationship between individual data in ontology. Based on SWCL, this paper tries to extend the language by adding one more type of constraint, implication constaint, in its repertoire. When users use binary variables to represent logical relationships in mathematical models, it requires and knowledge on the solver to solve the models. The use of implication constraint ease this difficulty. Its need, definition and relevant technical description is presented by the use of the optimal common attribute selection problem in product design.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.205-213
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• 2024

This study uses a large language model (LLM) to identify Aristotle's rhetorical principles (ethos, pathos, and logos) in COVID-19 information on Naver Knowledge-iN, South Korea's leading question-and-answer community. The research analyzed the differences of these rhetorical elements in the most upvoted answers with random answers. A total of 193 answer pairs were randomly selected, with 135 pairs for training and 58 for testing. These answers were then coded in line with the rhetorical principles to refine GPT 3.5-based models. The models achieved F1 scores of .88 (ethos), .81 (pathos), and .69 (logos). Subsequent analysis of 128 new answer pairs revealed that logos, particularly factual information and logical reasoning, was more frequently used in the most upvoted answers than the random answers, whereas there were no differences in ethos and pathos between the answer groups. The results suggest that health information consumers value information including logos while ethos and pathos were not associated with consumers' preference for health information. By utilizing an LLM for the analysis of persuasive content, which has been typically conducted manually with much labor and time, this study not only demonstrates the feasibility of using an LLM for latent content but also contributes to expanding the horizon in the field of AI text extraction.

• Proceedings of the CALSEC Conference
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• 1999.07b
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• pp.587-596
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• 1999

In this paper, we introduce the design and development of a web-based workflow management system. The goal of the developed system is to manage business processes occurring in the CITIS (Contractor Integrated Technical Information Services) environment. The system is composed of three main modules: Process Designer, Workflow Engine, and Client modules. The Process Designer is a module that provides the environment for the build-time function, which generates the specifications of processes. The module presents the capability of defining nested process models, which is powerful in particular for designing complex processes. Since the other two modules are implemented using pure Java technology, the Workflow Engine can be implemented on any platform and the Client programs can be accessed via the WWW interface. This indicates that there is no need to install any client programs at the client-sides. Users who has a connection to the internet with web browsers, such as Internet Explorer and Netscape Navigator, and has a proper right of access can utilize the normal client, monitoring client, and system administration client programs. Communications between the workflow engine and the clients are implemented using the java servlet mechanism. The workflow system can serve as the underlying platform of process management tool in CALS and CITIS environments. An example scenario of using the system is presented.