• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.584-588
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• 2019

Wireless body area network (WBAN) is a networking technology that enables early detection of abnormal health conditions, real-time medical monitoring, and telemedicine support systems. The internet of things (IoT) for healthcare, which has become an issue recently, is one of the most promising areas for improving the quality of human life. It must meet the high QoS requirements of the medical communication system like any other communication system. Therefore, the bit error rate (BER) threshold was chosen to accommodate the QoS requirements of the WBAN communication system. In this paper, we calculated BER performance of WBAN channel using IR-UWB PPM modulation and analyzed link budget and system margin of WBAN according to various system parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1484-1502
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• 2023

The medical sector actively changes and implements innovative features in response to technical development and revolutions. Many of the most crucial elements in IoT-connected health services are safeguarding critical patient records from prospective attackers. As a result, BlockChain (BC) is gaining traction in the business sector owing to its large implementations. As a result, BC can efficiently handle everyday life activities as a distributed and decentralized technology. Compared to other industries, the medical sector is one of the most prominent areas where the BC network might be valuable. It generates a wide range of possibilities and probabilities in existing medical institutions. So, throughout this study, we address BC technology's widespread application and influence in modern medical systems, focusing on the critical requirements for such systems, such as trustworthiness, security, and safety. Furthermore, we built the shared ledger for blockchain-based healthcare providers for patient information, contractual between several other parties. The study's findings demonstrate the usefulness of BC technology in IoHT for keeping patient health data. The BDSA-IoHT eliminates 2.01 seconds of service delay and 1.9 seconds of processing time, enhancing efficiency by nearly 30%.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.15-20
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• 2016

This paper describes the construction of knowledge data retrieval management system based on medical image CT. The developed system is aimed to improve the efficiency of the hospital by reading the medical images using the intelligent retrieval technology and diagnosing the patient 's disease name. In this study, the medical image DICOM file of PACS is read, the image is processed, and feature values are extracted and stored in the database. We have implemented a system that retrieves similarity by comparing new CT images required for medical treatment with the feature values of other CTs stored in the database. After converting 100 CT dicom provided for academic research into JPEG files, Code Book Library was constructed using SIFT, CS-LBP and K-Mean Clustering algorithms. Through the database optimization, the similarity of the new CT image to the existing data is searched and the result is confirmed, so that it can be utilized for the diagnosis and diagnosis of the patient.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1602-1612
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• 2017

Since rapidly disseminating of Internet of Things (IoT) as the new communication paradigm, a number of studies for various applications is being carried out. Especially, interest in the smart medical system is rising. In the smart medical system, a number of medical devices are distributed in popular area such as station and medical center, and this high density of medical device distribution can cause serious performance degradation of communication, referred to as the coexistence problem. When coexistence problem occurs in smart medical system, reliable transmitting of patient's biological information may not be guaranteed and patient's life can be jeopardized. Therefore, coexistence problem in smart medical system should be resolved. In this paper, we propose a distributed coexistence mitigation scheme for IoT-based smart medical system which can dynamically avoid interference in coexistence situation and can guarantee reliable communication. To evaluate the performance of the proposed scheme, we perform extensive simulations by comparing with IEEE 802.15.4 MAC protocol which is a traditional low-power communication technology.

• Korea Journal of Hospital Management
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• v.23 no.2
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• pp.54-66
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• 2018

Purpose: This study was to figure out relationships of perceived Technology Readiness Index(TRI), usefulness, acceptance intension, and the recognition of substitute employment of medical personnel on the artificial intelligence (AI) and internet of things (IoT) among main technologies. Methodology: To achieve the purpose, this study utilized structured survey tools to conduct a questionnaire survey of nursing, administrative and medical technology professionals at six university hospitals in Korea metropolitan area. A PLS(Partial Least Square) Path analysis was utilized To analyze the material. Findings: In the relation with the technology readiness and perceived usefulness, it had a positive influence to the perceived usefulness when the optimism and innovativeness were higher and the discomfort was lower. In the relation with the technology readiness and acceptance intension, it showed a positive influence when the innovativeness was higher and the discomfort was lower. In the relation with the perceived usefulness and acceptance intension, it had a positive influence to the acceptance intension when the perceived usefulness was higher. In the relation with the acceptance intension and the recognition of substitute employment, it showed a positive influence to the recognition of substitute employment when the acceptance intension was higher. Practical Implications: Judging based on the above study results and reference reviews, it confirmed that it is necessary to prepare in the level of hospital organization in the $4^{th}$ Industrial Revolution. They should increase the efficiency of human resources through the technological factors or changes of employment types for the additional demands of human resources to handle increasing medical demands or induce to secure necessary abilities which are changing at the right time by performing the $4^{th}$ Industrial Revolution related re-training continuously to develop the value of existing human resources.

• Journal of Internet of Things and Convergence
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• v.2 no.2
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• pp.15-20
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• 2016

MicroSoft Windows 10 IoT version, released in August 2015, successfully drew consumer interest by introducing the familiar Windows into the IoT market, and enabled an easier system construction of IoT web servers. Meanwhile, overdiagnosis has recently emerged as a controversy in medical society. Establishment of communication between IoT servers and medical devices will send treatment results to users and activate communication between hospitals, greatly reducing this problem. The IoT server, with its limited resources, utilizes lightweight protocols that do not generate traffic and are easy to use. This paper proposes IoT networks which will enable medical devices to easily provide ubiquitous environments to their users, through utilization of the lightweight Simple Service Discovery Protocol (SSDP) and the secure Extensible Messaging and Presence Protocol (XMPP).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.167-185
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• 2019

As most G-20 countries expect medical spending to grow rapidly over the next few decades, the burden of healthcare costs continues to grow globally due to an increase in the elderly population and chronic illnesses, and the ongoing quality improvement of health care services. However, under the rapidly changing technological environment of healthcare and IT convergence, the problem may become even bigger if not properly recognized and not properly prepared. In the context of the paradigm shift and the increasing problem of the medical field, complex responses in technical, institutional and business aspects are urgently needed. The key is to derive a business model that is appropriate for businesses that integrate IT in the medical field. With the arrival of the era of the 4th industrial revolution, new technologies such as Internet of Things have been applied to eHealthcare, and the need for new business models has emerged.In the e-healthcare of the Internet era, it became a traditional firm-based business model. However, due to the characteristics of dynamics and complexity of things Internet in the Internet of things, A business ecosystem-based approach is needed. In this paper, we present and analyze the major success factors of the ecosystem based on the 3 - layer structure of the e - healthcare business ecosystem as a result of research on e - healthcare business ecosystem based on emerging technology such as Internet of things. The three-layer business ecosystem was defined as (1) Infrastructure Layer, (2) Character Layer, and (3) Stakeholder Layer. As the key success factors for the eHealthCare business ecosystem, the following four factors are suggested: (1) introduction of the iHealthcare concept, (2) expansion of the business ecosystem, (3) business ecosystem change process innovation, and (4) business ecosystem leadership innovation.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.51-61
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• 2016

With high interest in the patient satisfaction of emergency medical services, there is a lot of effort into improving the process of Emergency Department(ED) utilizing the technology of Internet of Things(IoT). In this study, the core technologies of smart ED are examined and a decision support algorithm for medicine tasks is proposed. The proposed algorithm minimizes the decision risks such as task selection accountability, patient complaints, care delays and longer stay time. It can reduce the nurses burnout and improve the patient care with kindness and consideration. Ultimately, patient satisfaction, job satisfaction and professional identity of nurses can be increased. The comparative study was carried out by simulation in terms of the average length of patient stay in a simplified hypothetical ED system. In all the cases, the proposed algorithm was shown to perform substantially better than the other rule.