Earticle

Due to the low efficiency and poor accuracy of current college teaching intelligence evaluation methods, an improved method is proposed. Firstly, an improved apriori (IApriori) algorithm is utilized to filter evaluation indexes and establish a teaching quality evaluation indicator system. Secondly, considering the high complexity and low accuracy of the backpropagation neural network (BPNN), principal component analysis (PCA) is taken to reduce the input data's dimension. An improved sparrow search algorithm (ISSA) is simultaneously utilized to optimize the parameters of BPNN. Finally, a PCA-ISSA-BPNN teaching intelligence evaluation model is constructed. The experiments validated that when the number of transactions was 1,000, the IApriori only took 0.32 seconds to run. While the number of projects was 11, IApriori ran in 15.28 seconds. The evaluation accuracy of the PCA-ISSA-BPNN model reached 99.05%, the F1 value was 96.43%, the recall was 97.26%, and the AUC was 0.981. The above data show that IApriori has a higher efficiency in data mining and can more effectively screen evaluation indicators. This research method can effectively and accurately evaluate teaching quality, and has a positive impact on promoting student development, advancing teaching reform, and improving teaching quality.

Main purpose of this article is to classify reservoirs corresponding to their physical characteristics, for example, dam height, dam width, age, repair-works history. First of all, data set of 13,976 reservoirs was analyzed using k means and self organized maps. As a result of these analysis, lots of reservoirs have been classified into four clusters. Factors and their critical values to classify the reservoirs into four groups have been founded by generating a decision tree. The path rules to each group seem reasonable since their survivor function showed unique pattern.

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

The purpose of this study is to compare research trends in the information and communication technology (ICT) field between North and South Korea and analyze the differences by using data mining. Frequency analysis, clustering, and network analysis were performed using keywords from seven South Korean and two North Korean ICT academic journals published for five years (2015-2019). In the case of South Korea (S. Korea), the frequency of research on image processing and wireless communication was high at 16.7% and 16.3%, respectively. North Korea (N. Korea) had a high frequency of research, in the order of 18.2% for image processing, 16.9% for computer/Internet applications/security, and 16.4% for industrial technology. N. Korea's natural language processing (NLP) sector was 11.9%, far higher than S. Korea's 0.7 percent. Student education is a unique subject that is not clustered in S. Korea. In order to promote exchanges between the two Koreas in the ICT field, the following specific policies are proposed. Joint research will be easily possible in the image processing sector, with the highest research rate in both Koreas. Technical cooperation of medical images is required. If S. Korea's high-quality image source is provided free of charge to N. Korea, research materials can be enriched. In the field of NLP, it calls for proposing exchanges such as holding a Korean language information conference, developing a Korean computer operating system. The field of student education encourages support for remote education contents and management know-how, as well as joint research on student remote evaluation.

본 연구는 학령 아동의 건강 평가와 건강 촉진 정보 피드백에 주목했다. 문헌조사, 행동 연구, 시스템 분석 등 연구 방법을 이용하여 ‘학령기아동 건강증진 서비스 시스템’을 구축하는 설계 방안을 제시했다. 본 연구에서 구축된 ‘학령기아 동 건강증진 서비스 시스템’의 설계방안는 주로‘건강 정보 추천 시스템’과 ‘건강 위험 조기 경보 시스템’으로 나뉜다. 첫째, 건강정보 추천시스템은 건강정보에서의 정확한 추천을 담당한다. 건강정보 추천시스템은 ‘건강정보 자원모듈’, ‘건강정 보 서비스모듈’, ‘건강정보 응용모듈’ 3개 모듈로 나뉜다. 그중에서 건강정보 자원모듈은 건강정보 자원라이브러리 구축 을 책임지고 자원라이브러리의 건강정보의 권위성과 가치성을 보장한다. 건강정보 서비스모듈은 주요하게 정보가 정확 발송 서비스를 책임진다. 각각 학령아동의 정적지표 데이터와 동적지표 데이터를 수집하고 그 데이터에 근거하여 학령아 동의 건강위험성 확률을 평가한다. 건강정보 응용모듈은 주요하게 부동한 유형의 학령아동에게 정확한 건강정보 서비스 를 제공하는 것을 책임진다. 둘째, 건강 위험 조기 경보 시스템은 온라인과 오프라인에서의 건강증진 중재를 실시하는 것을 주로 담당하고 있다. 건강위험성 지표가 비교적 높은 학령아동에 초점을 맞춰 본문에서는 "가정, 학교, 커뮤니티" 3개 차원에서 건강증진 책략을 제기하였다.

This study focuses on the issues of health assessment for school-age children and information feedback for health promotion. Using literature research, action research method, system analysis method and other research methods, the design scheme of “School-age Children Health Promotion Service System” is proposed. The design scheme of the School-age Children's Health Promotion Service System constructed in this study is mainly divided into health information recommendation system and health risk warning system. First, the health information recommendation system is responsible for the accurate recommendation of health information. The health information recommendation system is divided into three modules: “Health Information Resource Module”, “Health Information Service Module” and “Health Information Application Module”. Among them, the “Health Information Resource Module" is responsible for the construction and information screening of the health information resource database. The “Health Information Service Module” mainly collects static indicator data and dynamic indicator data of school-age children, and evaluates the health risk probability of school-age children based on the data. The “Health Information Application Module” is responsible for providing accurate health information services for different types of school-age children. Second, the health risk warning system is mainly responsible for the implementation of “online and offline health promotion interventions”. For school-age children with higher health risk indicators, this study proposes a three-dimensional health promotion program for “family, school, and community”.

본 연구는 학습자 특성을 고려한 교사의 수행평가기준 선택 및 개발을 돕기 위한 툴의 설계 및 개 발을 목적으로 한다. 본 연구에서는 데이터마이닝의 분류 및 연관규칙 탐사 기법을 적용하여 교사의 선택 경향과 학생의 특성에 따른 수행평가 유형을 분석하였으며, 이를 통하여 기존 루브릭의 활용 및 신규개발에 대한 적용 방안을 제공하였다. 학습자의 환경, 관심 및 능력을 고려한 수행평가기준의 개 발 및 활용은 데이터마이닝의 “분류”를 통한 학습자 중심의 루브릭 적용으로 가능하다. 또한 교사의 학습영역별로 축적된 루브릭 선택 성향을 연관규칙을 통해 추출하여 교수자의 루브릭 선택을 지원함 으로써 수행평가에 소요되는 노력과 시간을 경감시키는 효과가 있다. 수행평가나 루브릭 간의 연관성 과 학생의 특성 및 성취도에 따라 수행평가를 분류하는 본 프로그램은 교육행정 정보시스템(National Education Information System; NEIS)의 수행평가 요소와 연계하여 교수자의 루브릭 선택, 변경 및 생성을 지원한다.

In this study, we designed and developed a tool to help teachers select and develop effective performance assessment criteria considering characteristics of individual learners. Using this tool, we can analyze preferences of teachers and characteristics of students for each rubric by exploring the classification and association rules through data mining. Those findings can give us guidelines and insights for the development and the selection of performance assessment criteria. The classification rules found are used for the learner-centered evaluation reflecting learners' interests, capabilities, and circumstances. Association rules found are utilized for analyzing teachers' preference, which enable to reduce time and efforts for the development and selection of rubric. Also, this tool supports creation, change, and selection of teachers' rubric linked with the performance assessment of NEIS(National Education Information System).

This paper has been explored information data prediction implementation access based on data mining combination model. With data mining technology as the entry point and in combination with the analysis on information data prediction characteristics. Research on variable substitution to non-linear regression forecast model precision's influence, and seek the modeling method that can improve the forecast precision. Based on the Data mining, the transform in space and the weighted processing combined method, make full use of information that the primary data provide. Given modeling method of combination forecast model based on the Data mining. Based on Data mining’s combination forecast model’s modeling method can reduce the serious influence that the variable substitution brings and has fully used useful information in the primary data. It obviously improved the accuracy of the prediction model.

The core of service product innovation is to understand the demands of users. Self-service technology has changed the contact mode between users and the service, thus the traditional way to acquire information of users’ demands could no longer meet the requirement of self-service product innovation. The advantages of data mining technology on analyzing and forecasting information can help reveal implication relations between users and products. It can also obtain the potential and valuable information of users’ needs and increase the success rate of product innovation. This study proposed a new self-service product innovation model, and it analyzed and explored the approaches using data mining technology in the process of self-service product innovation to effectively import users’ needs and organize product function design.

In order to monitor working conditions of the hydraulic excavator correctly, the data mining technology is applied in it. Firstly, the basic theory of hydraulic excavation is studied; Secondly, the basic theory of data mining is summarized; Thirdly, the application of rough set is analyzed; then design of status monitoring system for hydraulic excavator is carried out; finally, a case study of excavator monitoring system is carried out, and results show that the data mining technology is an effective method for monitoring the condition of hydraulic excavator.

With the development of electronic commerce, agricultural products marketing also have a new development platform. In this paper, the author analyzes E-commerce consumer factors of agricultural products in agritourism based on data mining technology. The agricultural product marketing in the perspective of agritourism mainly takes the agritourism as an opportunity to carry out the sales, and E-commerce platform has greatly promoted the sales of agricultural products. Through the empirical research, we find out the influence factors of agricultural products sale as execution time, cost, credibility, reputation and other performance indicators, and credibility is one of the most important indicators. On this basis, we put forward relevant recommendations.

With the development of information technology, technology mining technology can help users to find the needed information accurately and efficiently. In this paper, the author makes factors analysis of professional growth of innovative talents based on data mining technology. Knowledge innovation is the starting point of scientific and technological innovation, and the development of innovative talents is the most important and the scarcest resource for enterprises. By analyzing the professional growth of innovative talents, we construct the evaluation index system of innovative talents. The conclusion proves that the balance between supply and demand of enterprise and personal professional growth is the important factor to promote organizational technology progress and the career development of employees.

The era of Web 2.0 has been coming, and more and more Web 2.0 application, such social networks and Wikipedia, have come up. As an industrial standard of the Web 2.0, the XML technique has also attracted more and more researchers. However, how to mine value information from massive XML documents is still in its infancy. In this paper, we study the basic problem of XML data mining-XML data mining model. We design a multi-level XML data mining model, propose a multi-level data mining method, and list some research issues in the implementation of XML data mining systems.

With expansion in the innovation, the requests of the individuals are expanding as individuals are more intrigued by the web offices with higher information rate. Despite the fact that 3G advances convey essentially higher bit rates than 2G innovations, "LTE" (3GPP Long Term Evolution) got a blast in the field of advancements with new offices like Internet applications or versatile broadband ( Voice over IP (VoIP), feature spilling, music downloading, portable TV and numerous others material) all around. It is very much necessary to extract the useful information of the user using LTE technology to understand he performance and durability of this new technology. In this paper we present a novel architecture for mining the data for extracting useful information regarding the success rates of various handoff procedures in LTE technology.

The problem of difference teaching is solved by that the data mining is used in network instructional platform of "Modern Educational Technology". This paper introduces difference teaching and data mining, analyzes learning elements and data in network instructional platform, elaborate the realization of the difference teaching by the application of data mining. Practice shows that the application of the data mining in network instructional platform of "Modern Educational Technology" which pays attention to the difference between learners, provides different resources and interactive strategy from curriculum resources recommended and assisting teachers to make decision, promotes each student's full development.

Purpose: The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs. Materials and Methods: This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health ex aminations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to Chi na Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naive Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS. Results: The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naive Bayes (0.824 and 0.831, respectively; p<0.05). Conclusion: The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system.

인간의 모든 삶에 대한 디지털적 접근을 시도한 문화기술은 이제 IT 기술을 바탕으로 한 인간의 모든 생활에서 폭 넓게 적용되어 가고 있으며 앞으로 그 범위가 점차 확대되리라 기대된다. 문화기술은 어느 특정한 분야에서 연구되기보다는 모든 학문분야에서 학제적 연계를 통하여 연구, 발전되고 있다. 본 논문에서는 특히, 디지털기술에 바탕을 둔 문화콘텐츠의 개발 및 사용에 대한 효과적인 의사결정을 지원하기 위하여 데이터 마이닝 전략을 접목하고자 한다. 데이터 마이닝 분석기법올 적용하여 사용자로부터 만족될 수 있는 문화콘텐츠의 구현방안을 제안한다.

데이터마이닝은 대량의 실제 데이터로부터 이전에 잘 알려지지는 않았지만 묵시적이고 잠재적으로 유용한 정보를 추출하는 작업으로, 본 논문은 최근 인문학 정보 자료가 전산화되고 있는 가운데 대량의 정보와 특정 체계를 갖춘 ‘조선왕조실록’ 전산자료를 분석하고 기존의 단순한 정보 검색이 아닌 데이터마이닝 기법을 적용한 상세하고 예측가능 한 정보자료 추출법을 제시한다. 먼저 텍스트화 되어 있는 컨텐츠를 형태소분석기법을 사용하여 색인어를 추출하고 집계를 낸다. 질의어와 유관한 색인어의 군집정도와 출현시점을 분석하는데, 사용된 마이닝 기법은 연관규칙분석과 클러스터링 분석기법이다. 최종 결과치는 기존의 인문학연구 결과물과 비교하여 그 정확도를 분석해 보인다.