• Journal of Smart Tourism
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• v.2 no.3
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• pp.5-15
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• 2022

As the utilization of technology in the tourism field becomes familiar, it greatly impacts people's tourism activities. These changes could also affect the behavior of tourists during the pandemic. To investigate consumers' adaptation to the self-service technology (SST) environment during the coronavirus disease of 2019 (COVID-19) pandemic, we adopted a model of absorptive capacity as the main framework for empirical research. To track the social effects of COVID-19, consumers' behavioral intentions for four different points in time are collected. The analysis was conducted using latent growth and structural equation modeling. We set the organizational and environmental characteristics as the first step of the model, with assimilation and trust as a middle step. Intention to use a kiosk is placed at the final step as an exploit. Findings indicate that organizational characteristics and environmental characteristics positively influenced assimilation and trust, except for environmental characteristics. Consumers' assimilation in SST encourages immediate intention to use a kiosk. Consumers' trust in kiosks positively impacts both immediate and continuance intention to use a kiosk during COVID-19.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.256-274
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• 2022

The continuous information technology and telecommunication (ICT) developments inspire several Saudi Arabia citizens to transact and interact online. However, when using online platforms, several people are likely to lose their personal information to cybercriminals. In the survey, 553 Saudi Arabia citizens and 103 information technology (IT) specialists confirm the expansion of digital economy and the need for smart cities with various services, including e-commerce and solid cyber security. 96.6% of the participants believe Saudi Arabia is digitalizing its economy; yet, 33.3% of the participants believe that residents are uninformed about living and operating in smart cities. Several people (47.29%) with medium internet speed are more aware about smart cities than those with fastest internet speed (34%). Besides, online transactions via credit cards subjected 55.5% of the participants to privacy and security issues. These findings validate the essence of cyber security awareness programs among Saudi Arabia citizens and IT professionals to boost public trust and acceptance of cybersecurity frameworks.

• Steel and Composite Structures
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• v.50 no.3
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• pp.337-345
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• 2024

This research proposed a particle swarm optimization (PSO) based seismic retrofit design of moment frame structures using a steel frame assembly. Two full scale specimens of the steel frame assembly with different corner details were attached to one-story RC frames for seismic retrofit, and the lateral load resisting capacities of the retrofitted frames subjected to cyclic loads were compared with those of a bare RC frame. The open source software framework Opensees was used to develop an analytical model for validating the experimental results. The developed analytical model and the optimization scheme were applied to a case study structure for economic seismic retrofit design, and its seismic performance was assessed before and after the retrofit. The results show that the developed steel frame assembly was effective in increasing seismic load resisting capability of the structure, and the PSO algorithm could be applied as convenient optimization tool for seismic retrofit design of structures.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.335-344
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• 2024

This study explores advanced machine learning techniques for improving crop yield prediction in smart farming, utilizing multi-temporal spectral data from drone-based multispectral imagery. Conducted in garlic orchards in Andong, Gyeongbuk Province, South Korea, the research examines the effectiveness of various vegetation indices and cutting-edge models, including LSTM, CNN, Random Forest, and XGBoost. By integrating these models with the Analytic Hierarchy Process (AHP), the study systematically evaluates the factors that influence prediction accuracy. The integrated approach significantly outperforms single models, offering a more comprehensive and adaptable framework for yield prediction. This research contributes to precision agriculture by providing a robust, AI-driven methodology that enhances the sustainability and efficiency of farming practices.

• Smart Structures and Systems
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• v.13 no.4
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• pp.501-515
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• 2014

This paper presents a linear computational homogenization framework to evaluate the effective (or generalized) electromechanical coupling coefficient (EMCC) of adaptive structures with piezoelectric structural fiber (PSF) composite elements. The PSF consists of a silicon carbide (SiC) or carbon core fiber as reinforcement to a fragile piezo-ceramic shell. For the micro-scale analysis, a micromechanics model based on the variational asymptotic method for unit cell homogenization (VAMUCH) is used to evaluate the overall electromechanical properties of the PSF composites. At the macro-scale, a finite element (FE) analysis with the commercial FE code ABAQUS is performed to evaluate the effective EMCC for structures with the PSF composite patches. The EMCC is postprocessed from free-vibrations analysis under short-circuit (SC) and open-circuit (OC) electrodes of the patches. This linear two-scale computational framework may be useful for the optimal design of active structure multi-functional composites which can be used for multi-functional applications such as structural health monitoring, power harvest, vibration sensing and control, damping, and shape control through anisotropic actuation.

• Smart Media Journal
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• v.4 no.3
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• pp.56-61
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• 2015

Although many successful cases regarding big data have been reported, building information systems of big data is still difficult. From the perspective of technology the builders need to understand the whole process of systems development ranging from collecting, storing, processing, and analyzing data to presenting and using information. Whereas, from the perspective of business, the builders need to understand the values of the proposed big data project and explain to top managers who have to make a decision of the risky investment. This study proposes a framework of 5W 1H that can help the builder understand things related to the development of big data information systems. In addition, big data cases from the real world have been illustrated by applying to the framework. It is expected to help builders understand and manage big data projects and lead managers to make better decisions of the investment to the development of information systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.161-161
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• 2019

Water security issues, stimulated by increasing population and changing climate, are growing and pausing major challenges for water resources managers around the world. Proper utilization, management and distribution of all available water resources is key to sustainable development for achieving water security To alleviate the water shortage, most of the current research on multi-sources combined water supplies depends on an overall generalization of regional water supply systems, which are seldom broken down into the detail required to address specific research objectives. This paper proposes the concept of optimization framework on multi water sources selection. A multi-objective water allocation model with four objective functions is introduced in this paper. Harmony search algorithm is employed to solve the applied model. The objective functions addresses the economic, environmental, and social factors that must be considered for achieving a sustainable water allocation to solve the issue of water security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2849-2864
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• 2021

The emerging of cloud data sharing can create great values, especially in multi-cloud environments. However, "data island" between different cloud service providers (CSPs) has drawn trust problem in data sharing, causing contradictions with the increasing sharing need of cloud data users. And how to ensure the data value for both data owner and data user before sharing, is another challenge limiting massive data sharing in the multi-cloud environments. To solve the problems above, we propose a Uniform Encoding and Parsing Framework (UEPF) with blockchain to support trustworthy and valuable data sharing. We design namespace-based unique identifier pair to support data description corresponding with data in multi-cloud, and build a blockchain-based data encoding protocol to manage the metadata with identifier pair in the blockchain ledger. To share data in multi-cloud, we build a data parsing protocol with smart contract to query and get the sharing cloud data efficiently. We also build identifier updating protocol to satisfy the dynamicity of data, and data check protocol to ensure the validity of data. Theoretical analysis and experiment results show that UEPF is pretty efficient.

• Smart Media Journal
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• v.13 no.3
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• pp.53-61
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• 2024

This paper presents a novel M-shaped encoder-decoder architecture for skin lesion segmentation, achieving better performance than existing approaches. The proposed architecture utilizes the left and right legs to enable multi-scale feature extraction and is further enhanced by integrating an attention module within the skip connection. The image is partitioned into four distinct patches, facilitating enhanced processing within the encoder-decoder framework. A pivotal aspect of the proposed method is to focus more on critical image features through an attention mechanism, leading to refined segmentation. Experimental results highlight the effectiveness of the proposed approach, demonstrating superior accuracy, precision, and Jaccard Index compared to existing methods

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.1-7
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• 2020

In order to implement a smart factory for manufacturing innovation, more digital twins will be developed and applied gradually. In particular, simulation and optimization of digital twins makes it possible to support critical decision-making like a predictive maintenance of the equipment for manufacturing. In terms of a user perspective, this study suggests the conceptual framework of Digital Twin Management System (DTMS) for supporting the analytical and managerial activities for Digital Twins. We integrate the methods and structure of the area like Manufacturing Engineering, Decision Support Systems, and Optimization for developing the DTMS. The framework suggested in this study shows a typical DSS which consists of dialog management system, model management system and data management system. It also includes Analytical Digital Twins and simulations & optimization module. The framework is being applied in one of the most competitive and complex industrial sector. Also this study is meaningful to suggest a new direction of research.