• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.2
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• pp.21-58
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• 2009

The Purpose of this study is working out of the scientific-theoretical and practical recommendations directed on perfection of strategic planning of development of the enterprises of mining and gold mining branch. The methodological basis of research is based on the economic theory developed by a domestic and foreign science. At processing, generalisation and a writing of materials of the master's thesis following methods were applied: - supervision, - comparison, - the analysis and synthesis, - methods of an induction and deduction, - statistical groupings, - average and relative sizes, - the system approach. Finally, the theoretical and practical importance of this research consists that results of research will allow generating a basis of statement of effective system of strategic planning of a long-term sustainable development of the gold mining enterprises reducing risk of acceptance of inefficient strategic decisions. I would like to express many thanks to the NGO "Semey- My Home" and "EastGeoResources" LLP for their help and support in providing the data collection and data analysis stages of my research from 2006.

• Geomechanics and Engineering
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• v.16 no.2
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• pp.177-185
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• 2018

The subsidence mechanism of ground surface is a complex phenomenon when multiple seam coal mining operations are carried out. Particularly, the coal mining beneath karstic formations causes a very special form of subsidence. The subsidence causes elasto-plastic deformation of the karstic layers and the collapse of cavities leads to dolinization and/or sinkhole formation. In this study, a sinkhole with a depth of 90 m and a width of 25 m formed in Gelik district within the coal-basin of Zonguldak (NW, Turkey) induced by multiple seam coal mining operations in the past has been presented as a case-history together with two-dimensional numerical simulations and InSAR monitoring. The computational results proved that the sinkhole was formed as a result of severe yielding in the close vicinity of the faults in contact with karstic formation due to multiple seam longwall mining at different levels.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2004.10a
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• pp.171-180
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• 2004

Underground mining causes surface subsidence long after the mining operation had been ceased. Surface subsidence can be in the form of saucer-shaped depression or collapsed chimneys or sinkholes. Sinkhole formations are predominant over shallow-depth room and pillar mines having weak overburden strata. In this study, occurrences of sinkholes due to mining activity are assessed based on local geological conditions and mining parameters using fuzzy reasoning techniques. All input and output parameters are represented with linguistic hedges. Numerous fuzzy rules are developed to relate sinkhole occurrences with input parameters using fuzzy relational matrix. Based on the combined fuzzy rules, possibility of sinkhole occurrences can be ascertained once the geological and mining parameters of any area are known.

• ETRI Journal
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• v.33 no.5
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.159-175
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• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.153-156
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• 2006

최근 개인 컴퓨터에 저장되는 다양한 미디어 정보에 대한 검색요구가 크게 대두되면서, 다양한 데스크톱 검색 시스템이 출현하고 있다. 그러나, 기존 데스크톱 검색 시스템은 파일명, 일부 제한된 메타데이터, 콘텐츠들에 대한 키워드 기반의 검색을 수행하기 때문에 사용자의 요구에 부합하는 결과를 정확하게 제시하는 못하는 문제점이 있다. 본 논문에서는 이와 같은 문제점을 해결하기 위해서 시맨틱웹 기술을 활용하여, 온톨로지에 기반한 메타데이터를 정의하고 이를 기반으로 메타데이터간의 의미적 연관성에 기반한 시맨틱 데스크톱 검색/관리 시스템에 대해 기술한다.

• Analytical Science and Technology
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• v.7 no.4
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• pp.477-482
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• 1994

By the instrumental neutron activation analysis using two comparators of gold and cobalt, 31 elements have been anlalyzed in anthracites collected from two main coal-fields in Korea. The average concentrations and ranges of the elements were obtained with the elemental difference between two coal-fields. The trends of rare-earth elemental distribution and vertical elemental distribution are also given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.27-36
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• 2000

The problem of discovering association rules is an emerging research area, whose goal is to extract significant patterns or interesting rules from large databases and several algorithms for mining association rules have been applied to item-oriented sales transaction databases. Data warehouses and OLAP engines are expected to be widely available. OLAP and data mining are complementary; both are important parts of exploiting data. Our study shows that data cube is an efficient structure for mining association rules. OLAP databases are expected to be a major platform for data mining in the future. In this paper, we present an efficient and effective algorithm for mining association rules using data cube. The algorithm can be applicable to enhance the power of competitiveness of business organizations by providing rapid decision support and efficient database marketing through customer segmentation.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.221-232
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• 2021

In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.124-131
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• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.