• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.19-26
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• 2019

For the last two decades, a number of information systems are developed for various aims, depending on business' needs. There are a lot of organizations in the world which are using information systems in their environment, such as telecommunications organizations, universities and banks. Using information system has become crucial for most of organizations regarding with increasing the performance of work procedures and improve productivity and efficiency in general. There are many different models that have been designed and validated to explain the effect of constructs on the adoption of technologies. The aim of this research is to review the literature on information systems adoption and to analyze the different types of models which are frequently applied by researchers in their efforts to examine the factors that estimate the adoption of technologies. The research explores information systems adoption literature that focuses on development models.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.176-181
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• 2024

The desktop-level 3D printing machines makes it easier for independent designers to produce collectible models. Desktop 3D printers that use FDM (Fused Deposition Modeling) technology usually use a minimum nozzle diameter of 0.4mm. When using FDM printers to make Gunpla models, Thin slice structures are prone to slicing errors, which lead to deformation of printed objects and reduction in structural strength. This paper aims to analyze the printing model that produces errors, control a single variable among the three variables of slice layer height, slice wall thickness and filament type for comparative testing, and find a way to avoid gaps. To provide assistance for using FDM printers to build models containing thin-walled structures.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• Asia pacific journal of information systems
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• 제12권2호
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• pp.69-98
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• 2002

With the development of the Internet, electronic commerce, electronic markets, and digital economy, new business paradigm and new ways of business have been emerging and developing. The development of right and robust business models for electronic markets is a key for e-business success. This paper reviews previous studies and successful cases for e-business models. This paper presents strategic factors such as the business value and the source of revenue, products and services, business processes and technologies, and the characteristics of markets and relationship with customers and partners as a framework for developing sustainable and robust business models.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• 한국IT서비스학회지
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• 제7권2호
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• pp.59-75
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• 2008

Despite our extensive understanding on the Internet business and widely understood first-mover advantage. it is not clearly answered yet whether an internet firm can enjoy the first-mover advantage in the new environment of the Internet. This is mainly because the Internet marketplace itself has a complex combination of various business models, ranging from a simple channel-extension to a whole new business model. Based on new theoretical development on the first-mover advantage, we empirically test whether being an early mover in the Internet environment materially affects firm performance, using clickstream data from Korea where broadband Internet installation is ranked as top among OECD countries. Our results show the effectiveness of first-mover advantage on the web does not exist, regardless of its business model and competitive environment. This result expands our understandings on the e-business, not to mention of the real feature of first-mover advantage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1702-1721
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• 2019

A workflow process (or business process) management system helps to define, execute, monitor and manage workflow models deployed on a workflow-supported enterprise, and the system is compartmentalized into a modeling subsystem and an enacting subsystem, in general. The modeling subsystem's functionality is to discover and analyze workflow models via a theoretical modeling methodology like ICN, to graphically define them via a graphical representation notation like BPMN, and to systematically deploy those graphically defined models onto the enacting subsystem by transforming into their textual models represented by a standardized workflow process definition language like XPDL. Before deploying those defined workflow models, it is very important to inspect its syntactical correctness as well as its structural properness to minimize the loss of effectiveness and the depreciation of efficiency in managing the corresponding workflow models. In this paper, we are particularly interested in verifying very large-scale and massively parallel workflow models, and so we need a sophisticated analyzer to automatically analyze those specialized and complex styles of workflow models. One of the sophisticated analyzers devised in this paper is able to analyze not only the structural complexity but also the data-sequence complexity, especially. The structural complexity is based upon combinational usages of those control-structure constructs such as subprocesses, exclusive-OR, parallel-AND and iterative-LOOP primitives with preserving matched pairing and proper nesting properties, whereas the data-sequence complexity is based upon combinational usages of those relevant data repositories such as data definition sequences and data use sequences. Through the devised and implemented analyzer in this paper, we are able eventually to achieve the systematic verifications of the syntactical correctness as well as the effective validation of the structural properness on those complicate and large-scale styles of workflow models. As an experimental study, we apply the implemented analyzer to an exemplary large-scale and massively parallel workflow process model, the Large Bank Transaction Workflow Process Model, and show the structural complexity analysis results via a series of operational screens captured from the implemented analyzer.

• Journal of Information Technology Applications and Management
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• 제11권2호
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• pp.205-214
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• 2004

Internet evolves into the huge network with new nodes inserted or deleted depending on specific situations. A new model of network topology is needed in order to analyze time-varying Internet more realistically and effectively. In this study the non-monotony models are proposed which can describe topological changes of Internet such as node insertion and deletion, and can be used for predicting its future topology. Simulation is performed to analyze the topology generated by our model. Simulation results show that our proposed model conform the power law of realistic Internet better than conventional ones. The non-monotony model can be utilized for designing Internet protocols and networks with better security.