• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.749-759
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• 2020

Over the years, numerous Micro, Small, and Medium Enterprises (MSMEs) have been vigorously established across many countries. Even though the Internet of Things (IoT) has enabled companies to anchorage business returns, most Indonesian MSMEs are highly susceptible to failure and one of the main issues is the inability to manage their financials effectively. The literature on accounting points out that the success of MSMEs owing to the usage of cloud-based Accounting Information Systems (AIS) or Cloud Accounting (CA) could reduce the rate of failure by managing multiple accounting information at a low cost. Although many benefits exist, Indonesian MSMEs are not adopting these platforms in their daily business activities. This study investigates the factors that influence Indonesian MSMEs' intention to adopt CA. The study is directed by unstructured in-depth interviews with seven bestseller MSMEs where a thematic analysis technique was employed to identify them. The interview findings and prevailing literature on the influencing factors based on the TOE (technological, organizational, and environmental) framework to adopt CA in Indonesian MSMEs context are perceived benefits outweighing the cost, perceived compatibility, perceived complexity, owner-manager knowledge on accounting, organization size, competitive pressure, and informal network. The conceptual model further includes government intervention as a moderator in the model.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.695-706
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• 2018

In recent years, industrial convergence centered on ICBM (internet of things (IoT), cloud, big data, mobile) has been experiencing rapid development in various fields such as agriculture and the financial industry. In order to prepare for cyber threats, one of the biggest problems facing the convergence industry in the future, the development of the industry must proceed in tandem with a framework of information security. In this study, we analyze the details of the current industrial development policy and related information protection policies using cross impact analysis and present policy priorities through the expert questionnaire. The aim of the study was to clarify the priorities and interrelationships within information security policy as a first step in suggesting effective policy direction. As a result, all six information security policy tasks derived from this study belong to key drivers. Considering the importance of policies, policies such as improving the constitution of the security industry and strengthening of support, training of information protection talent, and investing in the information security industry need to be implemented relatively first.

• Journal of Internet of Things and Convergence
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• v.2 no.3
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• pp.1-6
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• 2016

This paper aims to present the physical computing learning model applicable in teaching the information and communication technology for technology and engineering education. This model is based on the physical computing and deals with the information creation and information transfer in one framework, thus provides students with the total understanding and practice opportunity about information and communication. The proposed learning models are classified into the client-server based model and the web based model. In the implemented learning model, the acquirement and control of information is performed by sketch on Arduino and the communication of information is performed by the Python socket on Raspberry Pi well known as an education platform. Our proposed learning model can be used for teaching students to understand the concept of Internet of Things (IoT), which provides us with world wide control and communication of information.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.63-69
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• 2020

The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.45-50
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• 2017

Micro-service Architecture is a service architecture optimized for large-scale distributed systems such as real-time realistic broadcasting systems, so that are fiercely adopted by Global leading service platform vendors such as Netflix and Twitter due to the merit of horizontal performance scalability enabling the scale-out technique. In addition, micro-service architecture makes it possible to execute image processing and real-time data analysis using an asynchronous-based processing that are difficult to handle in Web API such as REST. In this paper, an event stream processing framework applicable to asynchronous based micro services is proposed in the sense that the accountability of event processing order is not guaranteed in the events such as IoT sensor data analysis or cloud-based image editing because these are the situations where the real-time media editing generates multiple event streams and asynchronous processes in the platform.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.100-105
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• 2017

This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.13-19
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• 2021

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.