• 인터넷정보학회논문지
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• 제15권6호
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• pp.99-106
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• 2014

최근 자연어 및 정형언어 처리, 인공지능 알고리즘 등을 활용한 효율적인 의미 기반 지식모델의 생성과 분석 방법이 제시되고 있다. 이러한 의미 기반 지식모델은 효율적 의사결정트리(Decision Making Tree)와 특정 상황에 대한 체계적인 문제해결(Problem Solving) 경로 분석에 활용된다. 특히 다양한 복잡계 및 사회 연계망 분석에 있어 정적 지표 생성과 회귀 분석, 행위적 모델을 통한 추이분석, 거시예측을 지원하는 모의실험 모형의 기반이 된다. 하지만 대부분의 지식 모델은 특정 지표나 정제된 데이터를 수동적으로 모델링하여 분석에 활용한다. 본 논문에서는 텍스트 마이닝 기술을 통해 방대한 비정형 정보로부터 지식 모델을 구성하는 토픽인자와 관계 노드를 생성하고 이를 통합하는 방법과 정형적 알고리즘을 제시한다. 이를 위해 먼저, 텍스트 마이닝을 통해 도출되는 키워드 맵을 동치적 지식맵으로 변환하고 이를 의미적 지식모델로 통합하는 방법을 설명한다. 또한 키워드 맵으로부터 유의미한 토픽 맵을 투영하는 방법과 의미적 동치 모델을 유도하는 알고리즘을 제안한다.

• 통합자연과학논문집
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• 제16권4호
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• pp.123-131
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• 2023

Breast cancer continues to pose a substantial worldwide health challenge, thereby requiring the development of innovative strategies to discover new therapeutic interventions. Signal Transducer and Activator of Transcription 3 (STAT-3) has been identified as a significant factor in the development of several types of cancer, including breast cancer. This is primarily attributed to its diverse functions in promoting tumour formation and conferring resistance to therapeutic interventions. This study presents an in silico drug repositioning approach that focuses on identifying specific inhibitors of STAT-3 for the purpose of treating breast cancer. We initially examined the structural and functional attributes of STAT-3, thereby elucidating its crucial involvement in cellular signalling cascades. A comprehensive virtual screening was performed on a diverse collection of drugs that have been approved by the FDA from zinc15 database. Various computational techniques, including molecular docking, cross docking, and cDFT analysis, were utilised in order to prioritise potential candidates. This prioritisation was based on their predicted binding energies and outer molecular orbital reactivity. The findings of our study have unveiled a Dihydroergotamine and Paritaprevir that have been approved by the FDA and exhibit considerable promise as selective inhibitors of STAT-3. In conclusion, the utilisation of our in silico drug repositioning approach presents a prompt and economically efficient method for the identification of potential compounds that warrant subsequent experimental validation as selective STAT-3 inhibitors in the context of breast cancer. The present study highlights the considerable potential of employing computational strategies to expedite the drug discovery process. Moreover, it provides valuable insights into novel avenues for targeted therapeutic interventions in the context of breast cancer treatment.

• 한국정보과학회논문지:시스템및이론
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• 제36권2호
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• pp.60-67
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• 2009

단백질 상호작용 네트워크는 어떤 단백질들 간에 상호 작용 관계가 있는지를 네트워크 형태로 나타낸 것이며 단백질 상호작용을 발견하거나 분석하는 것은 생명 공학에서 중요한 연구분야이다. 본 논문에서는 방대한 단백질 상호작용 데이터를 유전자 온톨로지와 연계한 시각화를 통하여 효과적으로 직관을 얻을 수 있는 효율적인 단백질 상호작용 네트워크 분석시스템을 다룬다. 단백질 상호작용 네트워크는 데이터 양이 매우 방대하기 때문에 이를 효율적으로 분석하는 방법과 효과적인 시각화 기법이 요구된다. 본 연구에서는 이를 위하여 동적이고 상호작용 가능한 그래프와 관심 노드와 그 주변 노드를 표시하며 점진적으로 탐색할 수 있는 컨텍스트 기반 탐색 기법을 도입하였다. 이 밖에도 특화된 기능으로써 단백질 상호작용과 유전자 온톨로지 간의 빠르고 자유로운 상호참조 기능과 최소 공통 조상을 사용한 유전자 온톨로지 분석 기능 등을 지원한다. 인터페이스 측면에서는 상호참조 기능을 효과적으로 사용하게 하기 위하여 유전자 온톨로지 그래프와 단백질 상호작용의 시각화 결과를 2차원 윈도우로 나란히 보여주는 인터페이스를 디자인 하였다.

• 한국과학교육학회지
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• 제32권3호
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• pp.486-501
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• 2012

The aims of this study are to investigate two main problems for the hypothetico-deduction method and to develop a scientific inquiry model to resolve these problems. The structure of this scientific inquiry model consists of accounts of the context of discovery and justification that the hypothetico-deduction holds as two main problems : 1) the heuristic flaw in the hypothetico-deduction method is that there is no limit to creating hypotheses to explain natural phenomena; 2) Logically, this brings into question affirming the consequent and modus tollens. The features of the model are as follows: first, the generation of hypotheses using an analogical abduction and the selection of hypotheses using consilience and simplicity; second, the expansion phase as resolution for the fallacy of affirming the consequent and the recycle phase as resolution for modus tollens involving auxiliary hypotheses. Finally, we examine the establishment process of Copernicus's Heliocentric Hypothesis and the main role of the history of science for the historical invalidity of this scientific inquiry model based on three examples of If/and/then type of explanation testing suggested by Lawson (International journal of science and Mathematics Education, 2005a, 3(1): 1-5) We claim that this hypotheticho-deduction process involving abduction approach produced favorable in scientific literacy rising for science teacher as well as students.

• The Journal of Asian Finance, Economics and Business
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• 제6권2호
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• pp.135-145
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• 2019

The paper aims to investigate the factors of brand equity affecting the purchase intention of car buyers in Ho Chi Minh City. The authors use qualitative method and quantitative research to study the matters, specifically using scales and data collected for Cronbach alpha reliability testing, analysizing the discovery factor of EFA, CFA and verifying the regression models through AMOS software with SEM linear modeling. The study proposes four factors: (1) brand awareness, (2) self-expression value, (3) perceived quality, (4) brand psychology impacting on the brand loyalty and intention to buy cars of customers. The results show that all four factors are statistically significant for positive brand loyalty and purchase willing. The results showed that brand loyalty positively affects consumers' intention to buy cars. Among the factors included in the study, the brand psychology is a new factor which developed by experts in the context of Vietnam. This is the first study in Vietnam to quantify clearly the element of "crowd psychology" affecting the interests and habits of Vietnamese consumers. This explains why Vietnamese consumers prefer brands that are familiar in the market and some new cars with nice models and colors suitable for Vietnamese psychology.

• 한국사회복지학
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• 제46권
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• pp.236-262
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• 2001

This discovery-oriented study explored 31 social workers' perceptions of discrepancies between explicit and implicit rules in their work places that are supposed to affect the quality of social work services, and identified eight categories of dilemmas: (a) confused accountability or purpose, (b) ambiguous principle, (c) improper authority, (d) confused role of social workers, (e) conflict between ideal and reality, (f) confused work ethics, (g) confused boundary of workers' rights, and (h) binds. These eight categories revealed the real philosophy and purposes of social work agencies, work ethics and values prevalent among social work agencies, agencies' orientation toward clients, and the conditions of social support from the society in large. Instead of searching for discrete variables as separately responsible for inefficient social work services, this approach probed malfunctioning implicit rules in a holistic context to see if inefficient or ineffective provision of social work services is a logical response to a much larger and deeper nexus. Insight into discrepant rules does not solely ensure the improvement of social work practice in the field, particularly if their identification is simply used as another opportunity to blame and avoid self-responsibility. However, such discrepancies between implicit and explicit rules are real enough to the staff workers and agency administrators that they may want to begin the dialogue of contradictory rules as a way of sanctioning discussion of previously forbidden topics. This study provided the ground-work for the dialogue.

• Asia pacific journal of information systems
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• 제7권1호
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• Biomolecules & Therapeutics
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• 제30권1호
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• 방사성폐기물학회지
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• 제21권4호
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• pp.571-576
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• 2023

The initial development plans for the six reactor designs, soon after the release of Generation IV International Forum (GIF) TRM in 2002, were characterized by high ambition [1]. Specifically, the sodium-cooled fast reactor (SFR) and very-high temperature reactor (VHTR) gained significant attention and were expected to reach the validation stage by the 2020s, with commercial viability projected for the 2030s. However, these projections have been unrealized because of various factors. The development of reactor designs by the GIF was supposed to be influenced by events such as the 2008 global financial crisis, 2011 Fukushima accident [2, 3], discovery of extensive shale oil reserves in the United States, and overly ambitious technological targets. Consequently, the momentum for VHTR development reduced significantly. In this context, the aims of this study were to compare and analyze the development progress of the six Gen IV reactor designs over the past 20 years, based on the GIF roadmaps published in 2002 and 2014. The primary focus was to examine the prospects for the reactor designs in relation to spent nuclear fuel burning in conjunction with small modular reactor (SMR), including molten salt reactor (MSR), which is expected to have spent nuclear fuel management potential.