• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.441-453
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• 2021

This study seeks to examine the effect of attitude, subjective norm, and perceived behavioral control on the purchase behavior of students enrolled in a private higher education establishment in Tangerang, Indonesia. This is a quantitative study and it employs samples by simple random sampling of 410 university students. The returned and valid questionnaire results totaled 261 samples. Data processing used the SEM method with SmartPLS 3.0 software. The findings of this study reveal that attitude, subjective norm, and perceived behavioral control have a significant effect on purchase intention. Meanwhile, purchase intention has a significant effect on working students' purchase behavior, and halal awareness had a moderating effect of purchase intention on purchase behavior. Purchasing interest has a positive effect on purchasing behavior, and this study proves that halal awareness is able to moderate the effect of purchase intention on purchasing behavior toward halal food products. The higher the awareness of halal products, the greater the relationship between buying interest and buying behavior of halal food. The results of this study also show the importance of paying attention to halal awareness factor in the form of increasing the relationship between buying interest and buying behavior of halal food products.

• International Journal of Advanced Culture Technology
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• 제9권2호
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• pp.86-90
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• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.

• 대한원격탐사학회지
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• 제37권1호
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• pp.13-22
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• 2021

The ML 5.8 earthquake shocked Gyeongju, Korea, at 11:32:55 UTC on September 12, 2016. One year later, on the afternoon of November 15, 2017, the ML 5.4 earthquake occurred in Pohang, South Korea. The earthquakes injured many residents, damaged buildings, and affected the economy of Gyeongju and Pohang. The damage proxy maps (DPMs) were generated from Sentinel-1 synthetic aperture radar (SAR) imagery by comparing pre- and co-events interferometric coherences to identify anomalous changes that indicate damaged by the earthquakes. DPMs manage to detect coherence loss in residential and commercial areas in both Gyeongju and Pohang earthquakes. We found that our results show a good correlation with the Korea Meteorological Administration (KMA) report with Modified Mercalli Intensity (MMI) scale values of more than VII (seven). The color scale of Sentinel-1 DPMs indicates an increasingly significant change in the area covered by the pixel, delineating collapsed walls and roofs from the official report. The resulting maps can be used to assess the distribution of seismic damage after the Gyeongju and Pohang earthquakes and can also be used as inventory data of damaged buildings to map seismic vulnerability using machine learning in Gyeongju or Pohang.

• 대한원격탐사학회지
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• 제37권1호
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• pp.57-67
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• 2021

An earthquake occurred on 17 November 2017 in Pohang, South Korea with a strength of 5.4 Mw. This is the second strongest earthquake recorded by local authorities since the equipment was first installed. In order to improve understanding of earthquakes and surface deformation, many studies have been conducted according to these phenomena. In this research, we will estimate the surface deformation using the Okada model equation. The SAR images of three satellites with different wavelengths (ALOS-2, Cosmo SkyMed and Sentinel-1) were used to produce the interferogram pairs. The interferogram is used as a reference for surface deformation changes by using Okada to determine the source of surface deformation that occurs during an earthquake. The Non-linear optimization (Levemberg-Marquadrt algorithm) and Monte Carlo restart was applied to optimize the fault parameter on modeling process. Based on the modeling results of each satellite data, the fault geometry is ~6 km length, ~2 km width and ~5 km depth. The root mean square error values in the surface deformation model results for Sentinel, CSK and ALOS are 0.37 cm, 0.79 cm and 1.47 cm, respectively. Furthermore, the results of this modeling can be used as learning material in understanding about seismic activity to minimize the impacts that arise in the future.

• Smart Structures and Systems
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• 제26권6호
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 대한원격탐사학회지
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• 제37권1호
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• pp.139-151
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• 2021

By the end of December 2020, an earthquake with Mw about 6.4 hit Sisak-Moslavina County, Croatia. The town of Petrinja was the most affected region with major power outage and many buildings collapsed. The damage also affected neighbor countries such as Bosnia and Herzegovina and Slovenia. As a light of this devastating event, a deformation map due to this earthquake could be generated by using remote sensing imagery from Sentinel-1 SAR data. InSAR could be used as deformation map but still affected with noise factor that could problematize the exact deformation value for further research. Thus in this study, 17 SAR data from Sentinel-1 satellite is used in order to generate the multi-temporal interferometry utilize Stanford Method for Persistent Scatterers (StaMPS). Mean deformation map that has been compensated from error factors such as atmospheric, topographic, temporal, and baseline errors are generated. Okada model then applied to the mean deformation result to generate the modeled earthquake, resulting the deformation is mostly dominated by strike-slip with 3 meter deformation as right lateral strike-slip. The Okada sources are having 11.63 km in length, 2.45 km in width, and 5.46 km in depth with the dip angle are about 84.47° and strike angle are about 142.88° from the north direction. The results from this modeling can be used as learning material to understand the seismic activity in the latest 2020 Petrinja, Croatia Earthquake.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.155-165
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• 2021

With the continuous development and progress of science and technology, the field of mixed reality applications has involved scientific research, medicine, entertainment, education, information dissemination, and people's daily lives; This paper will focus on the application of mixed reality in the field of education and teaching, relying on the characteristics of mixed reality technology. This article will use the combination of mixed reality and smart glasses to fully consider the characteristics of photography darkroom teaching, design and produce a mixed reality photography darkroom teaching software, and explore the feasibility of the application of mixed reality in teaching. This paper will use literature research methods, practical research methods, and survey methods as the main methods of research topics to determine the importance, feasibility, and necessity of the relevant theories studied in this paper. The application of mixed reality technology in photography darkroom teaching can not only solve many problems in the existing darkroom teaching methods, but also develop and expand students' Independent learning ability. It is concluded from this that mixed reality teaching software has the feasibility of sustainable development, which brings particular application value to the development and research of other education and art discipline software.

• Journal of Information Processing Systems
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• 제16권6호
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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.

• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.133-137
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• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.