• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• Journal of Construction Engineering and Project Management
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• v.5 no.4
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• Culinary science and hospitality research
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• v.18 no.4
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• pp.47-58
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• 2012

This study, targeting the students of "K" university in Busan City area, was performed to draw the groups by food-related lifestyle types and to identify the correlation between each group's attributes of selecting places to eat out and obesity index. The purpose of the study was achieved by means of the PASW Statistic 18.0(Predictive Analytics Software) which conducted frequency analysis, factor analysis, reliability analysis, t-test, ${\chi}^2$-test, non-hierarchical cluster analysis and ANOVA. It turned out that the male university students were 175.59 cm tall and weigh 69.53 kg on average. And the female university students showed their average height of 162.81 cm and weight of 53.42 kg. When examined by the body mass index(BMI), male students were composed of 1.7% of underweight, 64.6% of normal weight, 19.7% of overweight and 14.0% of obese. As for the female students, 22.9% were classified as underweight, 62.7% as normal weight, 8.5% as overweight and 5.9% as obese. The food-related lifestyle categories were divided into five factors; health seeking type, safety seeking type, mood seeking type, taste seeking type, and western food seeking type. The four attributes of selecting places to eat out included quality of food and service, price reasonableness, accessibility and atmosphere, and experience to have eaten. With regard to food-related lifestyle, the groups were named by cluster 1 [careless diet group], Cluster 2 [health oriented group], and cluster3 [careless healthcare group]. In terms of the correlation between the clusters by food-related lifestyle and their attributes of selecting places to eat out, Cluster 1 had a high mean value in experience to have eaten, Cluster 2 quality of food and service, Cluster 3 accessibility and atmosphere.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.265-274
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• 2017

This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.354-360
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• 2018

Recently, the computer aided learning system has promoted a new educational concept and education system. The purpose of this study is to construct a theoretical model for the epistemic frame and evaluation tool which is emphasized according to the 21st century. Specifically, first, this study conducted a domain analysis of epistemic frame. Second, this study developed an evaluation tool to measure epistemic frame. Finally, the evaluation tool is examined in terms of validity and reliability using factor analysis and Cronbach's alpha. As a result, the theoretical model was presented through the consultation of the Advisory Group and the evaluation tool was empirically validated. We expect that this study will provide a useful information to researchers and practitioners who want to develop a computer based learning tool for learning epistemic frame.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.5-12
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• 2021

The deterioration of facilities is an unavoidable phenomenon. For the management of aging facilities, cracks can be detected and tracked, and the condition of the facilities can be indirectly inferred. Therefore, crack detection plays a crucial role in the management of aged facilities. Conventional maintenances are conducted using the crack detection results. For example, maintenance activities to prevent further deterioration can be performed. However, currently, most crack detection relies only on human judgment, so if the area of the facility is large, cost and time are excessively used, and different judgment results may occur depending on the expert's competence, it causes reliability problems. This paper proposes a concrete crack detection framework based on machine learning to overcome these limitations. Fully automated concrete crack detection was possible through the proposed framework, which showed a high accuracy of 96%. It is expected that effective and efficient management will be possible through the proposed framework in this paper.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.784-792
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• 2018

In this study, we designed the area except the development market and the traditional market, where large scale shops were concentrated by realizing the real estate center of the alley commercial area. In addition, we have developed an area setting method for the alley area where reliability and rationality can be ensured by utilizing the actual data such as the business statistics, the survey data of the business, and the store business DB, which are managed by the local government or the state. The alley commercial areas were classified into five groups according to density. It is thought that users can distinguish the commercial areas from dense commercial areas to the commercial areas in order to utilize various commercial areas.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.129-159
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• 2019

The Purpose of this study is to develop an evaluation model for usability of research data service from the angles of evaluating usefulness of research data service itself and research data use experience-based usability. First, the various cases of evaluating usability of data services are examined and 4 rating scales and 20 measuring indicators for research data service are derived as a result of comparative analysis. In order to verify validity and reliability of the rating scale and the measuring indicators, the study conducted a survey of 164 potential research data users. KMO Bartlett Analysis was performed for validity test, and Principle Component Analysis and Verimax Rotating Method were used for component analysis on measuring indicators. The result shows that the 4 intrinsic rating scales satisfy the validity criteria of KMO Barlett; A single component was determined from component analysis, which verifies the validity of measuring indicators of the current rating scale. However, the result of 12 user experience-based measuring indicators analysis identified 2 components that are each classified as rating scale of utilization level and that of participation level. Cronbach's alpha of all 6 rating scales was 0.6 or more for the overall scale.