• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• The Journal of Economics, Marketing and Management
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• v.11 no.4
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• pp.31-39
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• 2023

Purpose: This study attempted to systematize a support system that can enhance teaching core competencies by establishing a scale for diagnosing teaching core competencies at University A. Research design, data and methodology : To this end, the first Delphi was conducted With six experts related to university core competency modeling research by extracting factors and designing structured questionnaires through a literature review process that collects and analyzes prior research related to domestic and foreign university teaching competency. The derived questions were diagnosed on 27 professors, and independent sample t-verification and ANOVA were conducted using SPSS 24.0 for analysis by key teaching competency factors. Result: What is the standard suitability of KMO. It was shown as 929 (KMO standard conformity value is close to 1), and Barlett's sphericity verification showed χ2=5773.295, df=1081, p<.It appeared as 001 and confirmed that it was suitable for conducting factor analysis. Conclusions: The core competencies of A University teachers were set based on the educational goals of A University, such as basic teaching competency, creative teaching competency, practical teaching competency, and communication teaching competency. This means that the concept and factors of the core competency of professors are likely to change, and in the end, continuous efforts to upgrade and apply research on core competency of professors are essential to quickly and organically respond to changes in competency required to increase the competitiveness of universities.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.53-70
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• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.60-65
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• 2013

Introducing the concept of construction safety in the design/engineering phase can improve the efficiency and effectiveness of safety management on construction sites. In this sense, further improvements for safety can be made in the design/engineering phase through the development of (1) an automated hazard identification process that is little dependent on user knowledge, (2) an automated construction schedule generation to accommodate varying hazard information over time, and (3) a visual representation of the results that is easy to understand. In this paper, we formulate an automated hazard identification framework for construction safety by extracting hazard information from related regulations to eliminate human interventions, and by utilizing a visualization technique in order to enhance users' understanding on hazard information. First, the hazard information is automatically extracted from textual safety and health regulations (i.e., Occupational Safety Health Administration (OSHA) Standards) by using natural language processing (NLP) techniques without users' interpretations. Next, scheduling and sequencing of the construction activities are automatically generated with regard to the 3D building model. Then, the extracted hazard information is integrated into the geometry data of construction elements in the industry foundation class (IFC) building model using a conformity-checking algorithm within the open source 3D computer graphics software. Preliminary results demonstrate that this approach is advantageous in that it can be used in the design/engineering phases of construction without the manual interpretation of safety experts, facilitating the designers' and engineers' proactive consideration for improving safety management.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• Journal of The Korean Association For Science Education
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• v.38 no.1
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• pp.43-55
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• 2018

In this study, we investigated high school students' analogy generating processes using the think-aloud method. Twelve high school students in Seoul participated in this study. The students were asked to generate analogies on ionic bonding and were also interviewed after their activities. Their activities and interviews were recorded and videotaped. After classifying the analogy generating processes into the three stages-encoding, exploring sources, and mapping, several process components were identified. The analyses of the results indicated that they checked the target concept given and selected one for a salient attribute among many attributes of the target concept at the stage of encoding. After selecting the salient attribute, they translated the salient attribute that is a scientific term into an everyday term, which is named as 'extracting salient similarities.' At the stage of exploring sources, they chose the sources based on salient similarities and chose the final source through circular processes, which included the process components of 'evaluating the sources' and 'discarding the sources.' At the final stage, they added the attributes to analogs and mapping them to the attributes of the target concept, which is named as 'mapping shared attributes.' There were some cases that 'mapping shared attributes' appeared after they specified the situation of analogs or assumed new situation, which is named as 'specifying the situations.' Some students recognized unshared attributes in their analogs.

• Journal of Korean Home Economics Education Association
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• v.30 no.3
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• pp.111-127
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• 2018

The purpose of this study was to analysis the home economics education curriculum from the first national curriculum to the 2015 revised curriculum using text mining techniques used in big data analysis. The subjects of the analysis were 10 curriculum texts from the first national curriculum to the 2015 revised curriculum via the National Curriculum Information Center. The major findings of this study were as follows; First, the number of data from the 4th curriculum to the 2015 revised curriculum gradually increased. Second, as a result of extracting core concept of the curriculum, there were core concept words that were changed and maintained according to the curriculum. 'Life' and 'home' were core concepts that persisted regardless of changes in the curriculum, after the 2007 revised curriculum, 'problem', 'ability', 'solution' and 'practice' were emphasized. Third, through core concept network analysis for each curriculum, the relationship between core concepts is represented by nodes and lines in each home economics curriculum. As a result, it was confirmed that the core concepts emphasized by the times are strongly connected with 'life' and 'home'. Based on these results, this study is meaningful in that it provides basic data to form the identity and the existing direction of home economics education.

• Management & Information Systems Review
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• v.37 no.4
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• pp.41-62
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• 2018

As the volume of unstructured data increases through various social media, Internet news articles, and blogs, the importance of text analysis and the studies are increasing. Since text analysis is mostly performed on a specific domain or topic, the importance of constructing and applying a domain-specific dictionary has been increased. The quality of dictionary has a direct impact on the results of the unstructured data analysis and it is much more important since it present a perspective of analysis. In the literature, most studies on text analysis has emphasized the importance of dictionaries to acquire clean and high quality results. However, unfortunately, a rigorous verification of the effects of dictionaries has not been studied, even if it is already known as the most essential factor of text analysis. In this paper, we generate three dictionaries in various ways from 39,800 news articles and analyze and verify the effect each dictionary on the accuracy of document classification by defining the concept of Intrinsic Rate. 1) A batch construction method which is building a dictionary based on the frequency of terms in the entire documents 2) A method of extracting the terms by category and integrating the terms 3) A method of extracting the features according to each category and integrating them. We compared accuracy of three artificial neural network-based document classifiers to evaluate the quality of dictionaries. As a result of the experiment, the accuracy tend to increase when the "Intrinsic Rate" is high and we found the possibility to improve accuracy of document classification by increasing the intrinsic rate of the dictionary.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Journal of the Korean Society of Costume
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• v.63 no.8
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• pp.43-57
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• 2013

The purpose of this research is to systemize the fashion digital storytelling by analyzing the communication method and its elements, and extracting the characteristics and processes of digital storytelling. Based on the previous study on the characteristics and types of storytelling the following things have been researched: 1) the process of digital storytelling in the communication process, 2) the concept and the feature of the academia of digital storytelling 3) storytelling in the document research. On the groundwork of the document research, we were able to sort out the various types, and formed a system of the features in the fashion digital storytelling cases, mainly in the four collections (Milan, Paris, London, New York) from 2000's to recent years of 2010. The types of fashion digital storytelling are episode type, narrative type, and creative type. The characteristics of each of the types are as follows. Firstly, the episode type communicates through the digital media based on the information or fact of the fashion designer brand. Secondly, narrative type communicates with the consumers using previous literature or an existing idea of the original cultural form that is rearranged in digital story expressed by the digital media. Lastly, creative type makes the designer's and consumer's susceptibility and creativity communicate through the newly made story, which expresses the unique originality of the designer. It seems that the cases and studies of using the fashion digital storytelling will increase because of its short history and lack of the case study. Fashion designer brands will show their brand image using the digital storytelling because they are able to better express originality, creativity and imagination of the fashion designer, which were factors that could not be conveyed through fashion alone.