Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

Disaster prediction of coal mine gas based on data mining

The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.

Efficiency assessment of coal mine safety input by data envelopment analysis

In recent years improper allocation of safety input has prevailed in coal mines in China, which resulted in the frequent accidents in coal mining operation. A comprehensive assessment of the input efficiency of coal mine safety should lead to improved efficiency in the use of funds and management resources. This helps government and enterprise managers better understand how safety inputs are used and to optimize allocation of resources. Study on coal mine’s efficiency assessment of safety input was con- ducted in this paper. A C2R model with non-Archimedean infinitesimal vector based on output is established after consideration of the input characteristics and the model properties. An assessment of an operating mine was done using a specific set of input and output criteria. It is found that the safety input was efficient in 2002 and 2005 and was weakly efficient in 2003. However, the effi- ciency was relatively low in both 2001 and 2004. The safety input resources can be optimized and adjusted by means of projection theory. Such analysis shows that, on average in 2001 and 2004, 45% of the expended funds could have been saved. Likewise, 10% of the safety management and technical staff could have been eliminated and working hours devoted to safety could have been reduced by 12%. These conditions could have given the same results.

RTDMiner:基于数据挖掘的引用计数更新缺陷检测方法

在Linux内核等大型底层系统中广泛采用引用计数来管理共享资源.引用计数需要与引用资源的对象个数保持一致,否则可能导致不恰当引用计数更新缺陷,使得资源永远无法释放或者被提前释放.为检测不恰当引用计数更新缺陷,现有静态检测方法通常需要知道哪些函数增加引用计数,哪些函数减少引用计数.而手动获取这些关于引用计数的先验知识过于费时且可能有遗漏.基于挖掘的缺陷检测方法虽然可以减少对先验知识的依赖,但难以有效检测像不恰当引用计数更新缺陷这类路径敏感的缺陷.为此,提出一个将数据挖掘技术和静态分析技术深度融合的不恰当引用计数更新缺陷检测方法RTDMiner.首先,根据引用计数的通用规律,利用数据挖掘技术从大规模代码中自动识别增加或减少引用计数的函数.然后,采用路径敏感的静态分析方法检测增加了引用计数但没有减少引用计数的缺陷路径.为了降低误报,在检测阶段再次利用数据挖掘技术来识别例外模式.在Linux内核上的实验结果表明,所提方法能够以将近90%的准确率自动识别增加或减少引用计数的函数.而且RTDMiner检测到的排行靠前的50个疑似缺陷中已经有24个被内核维护人员确认为真实缺陷.

Data organization and management of mine typical object spectral libraries

With the development of mining industry,people have obtained profits from it,but they are facing environmental damages.In order to monitor these environmental changes,a spectral library is set up for the spectrum data organization and management of mine typical objects.Most of the spectrum data come from the long-term field measuring in mining area and other spectral libraries.For the data quality control and error detection in the measuring data,an inner precision calculation method is presented and a series of interactive graphical controls are developed for the spectrum visualization and analysis.Through extracting and saving spectrum characters for the mine typical objects,realizs spectrum matching and classification for new measured spectrum samples are realized by using Euclidean distance,Aitchison distance,Pearson correlation coefficient and vector angular cosine methods.Based on the matching result,this work is able to gather dynamically physicochemical environment parameters from the library and gives an early warning for the mine environmental changes.

Alteration Information Extraction by Applying Synthesis Processing Techniques to Landsat ETM+Data: Case Study of Zhaoyuan Gold Mines, Shandong Province, China

Satellite remote sensing data are usually used to analyze the spatial distribution pattern of geological structures and generally serve as a significant means for the identification of alteration zones. Based on the Landsat Enhanced Thematic Mapper （ETM＋） data, which have better spectral resolution （8 bands） and spatial resolution （15 m in PAN band）, the synthesis processing techniques were presented to fulfill alteration information extraction： data preparation, vegetation indices and band ratios, and expert classifier-based classification. These techniques have been implemented in the MapGIS-RSP software （version 1.0）, developed by the Wuhan Zondy Cyber Technology Co., Ltd, China. In the study area application of extracting alteration information in the Zhaoyuan （招远） gold mines, Shandong （山东） Province, China, several hydorthermally altered zones （included two new sites） were found after satellite imagery interpretation coupled with field surveys. It is concluded that these synthesis processing techniques are useful approaches and are applicable to a wide range of gold-mineralized alteration information extraction.

Geophysical Study: Estimation of Deposit Depth Using Gravimetric Data and Euler Method (Jalalabad Iron Mine, Kerman Province of IRAN)

Mineral exploration is done by different methods. Geophysical and geochemical studies are two powerful tools in this field. In integrated studies, the results of each study are used to determine the location of the drilling boreholes. The purpose of this study is to use field geophysics to calculate the depth of mineral reserve. The study area is located 38 km from Zarand city called Jalalabad iron mine. In this study, gravimetric data were measured and mineral depth was calculated using the Euler method. 1314 readings have been performed in this area. The rocks of the region include volcanic and sedimentary. The source of the mineralization in the area is hydrothermal processes. After gravity measuring in the region, the data were corrected, then various methods such as anomalous map remaining in levels one and two, upward expansion, first and second-degree vertical derivatives, analytical method, and analytical signal were drawn, and finally, the depth of the deposit was estimated by Euler method. As a result, the depth of the mineral deposit was calculated to be between 20 and 30 meters on average.

Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques

In this article, the relationship between the knowledge of competitors and the development of new products in the field of capital medical equipment has been investigated. In order to identify the criteria for measuring competitors’ knowledge and developing new capital medical equipment products, marketing experts were interviewed and then a researcher-made questionnaire was compiled and distributed among the statistical sample of the research. Also, in order to achieve the goals of the research, a questionnaire among 100 members of the statistical community was selected, distributed and collected. To analyze the gathered data, the structural equation modeling (SEM) method was used in the SMART PLS 2 software to estimate the model and then the K-MEAN approach was used to cluster the capital medical equipment market based on the knowledge of actual and potential competitors. The results have shown that the knowledge of potential and actual competitors has a positive and significant effect on the development of new products in the capital medical equipment market. From the point of view of the knowledge of actual competitors, the market of “MRI”, “Ultrasound” and “SPECT” is grouped in the low knowledge cluster;“Pet MRI”, “CT Scan”, “Mammography”, “Radiography, Fluoroscopy and CRM”, “Pet CT”, “SPECT CT” and “Gamma Camera” markets are clustered in the medium knowledge. Finally, “Angiography” and “CBCT” markets are located in the knowledge cluster. From the perspective of knowledge of potential competitors, the market of “angiography”, “mammography”, “SPECT” and “SPECT CT” in the low knowledge cluster, “CT scan”, “radiography, fluoroscopy and CRM”, “pet CT”, “CBCT” markets in the medium knowledge cluster and “MRI”, “pet MRI”, “ultrasound” and “gamma camera” markets in the high knowledge cluster are located.

The safety parameters monitoring system for the coal mine based on CAN bus communication and intelligent data acquisition

In this paper,a monitoring and controlling system for the safety in production and environmental parameters of a small and medium-sized coal mine has been developed after analyzing the current domestic coal production and security conditions. The client computer can convert the analog signal about the safety in production and environmental parameters detected from the monitoring terminal into digital signal,and then,send the signal to the coal mine safety monitoring centre. This information can be analyzed,judged,and diagnosed by the monitoring-management-controlling software for helping the manager and technical workers to control the actual underground production and security situations. The system has many advantages including high reliability,better performance of real-time monitoring,faster data communicating and good practicability,and it can effectively prevent the occurrence of safety incidents in coal mines.

中国农业生态学与生态农业研究早期阶段的“燎原星火”——以1975年至2000年公开的部分资料为依据

20世纪的最后25年是中国内地的农业生态与生态农业理念推广、科研探索及人才队伍成长的早期阶段。本文基于中国知网系统、国家自然科学基金委员会大数据知识管理服务门户、中国生态学学会官网、中国科学院野外台站网,《中国生态农业学报》《农业环境科学学报》《农业资源与环境学报》《生态与农村环境学报》《农业现代化研究》《生态学杂志》等多种农业生态学与生态农业有关学术期刊网站及孔夫子旧书网,对1975—2000年期间,中国内地从事农业生态学与生态农业研究比较有影响力的学术平台(单位)和学者、公开出版的部分著作名录、国家自然科学基金生态学领域(C03)与农业生态相关的立项项目、中国知网系统内中文学术期刊发表的相关论文要素分析(发表年份、作者、所在单位、刊载期刊等)、被引次数及相关期刊创刊号特邀专论为依据的重要学术论文和学术论著题录进行了粗略的统计和分析,分析描述了早期阶段农业生态学及生态农业研究的热点及其变化过程。此外,本文还陈列和介绍了中国生态学学会农业生态专业委员会自成立至2000年期间组织和主办的十届全国农业生态学学术研讨会情况以及专业委员会的工作发展。本文为关心和支持中国农业生态学与生态农业建设的各界人士了解20世纪最后25年里中国农业生态学领域的大致概貌提供了一个粗略小结,也是对既往时代的认同、肯定和对现世同人的鼓励鞭策。

数据挖掘算法在作业车间调度问题中的应用

为了从与日俱增的车间生产数据中提取调度规则来指导生产调度任务,提出一种基于数据挖掘的调度算法。将最小化最大完工时间设置为性能指标,从作业车间的离线生产数据中建立合适的调度样本集;将建立的调度样本集按合适的比例分为训练集和测试集;用数据挖掘算法中的分类回归树(CART)从训练集中获取有效的调度知识,形成CART树状调度规则库;为了验证所得调度规则的有效性,将调度规则与遗传算法结合,设计了一种基于数据挖掘和调度规则的遗传算法作为调度算法来求解作业车间调度问题。通过对不同作业车间经典算例进行仿真与测试,验证了所提调度规则和调度算法的有效性与优越性。

国医大师熊继柏治疗失眠的方药规律分析

目的对国医大师熊继柏教授治疗失眠的病案进行数据挖掘,分析熊继柏教授诊治失眠的用方、用药经验。方法基于中医传承辅助平台软件(V3.5),归纳和分析熊继柏教授在湖南中医药大学第一附属医院治疗失眠患者的门诊病历数据,利用该平台软件的关联规则、复杂系统熵聚类等统计方法进行数据挖掘和分析,总结药物的性味、归经、应用频次、药对组合及合方,对“证-症-方”“证-症-药”“证-方-药”进行关联,结合熊继柏教授对失眠病因病机、治则治法的认识,明确核心用药,探索其对失眠的诊治规律及遣方用药心得。结果通过总结分析,共纳入病历276份,处方中用药频次较多的有酸枣仁、龙齿、茯神、甘草,重在补虚安神;黄连、陈皮、竹茹、半夏、珍珠母、远志,重在清热化痰安神。用药性味偏甘、苦、辛,归经多为心、脾经。并归纳出虚证治疗常用合方为酸枣仁汤、枕中丹和甘麦大枣汤;因痰所致失眠治疗的主方为温胆汤及温胆汤类方(枣仁温胆汤、黄连温胆汤及十味温胆汤)。结论国医大师熊继柏治疗失眠以“补脾化痰清热,养血补肝宁心”为治疗原则,以温胆汤为基础进行加减化裁。

基于数据挖掘的痛经用药规律研究

目的:基于数据挖掘技术分析《中医方剂大辞典》中治疗痛经方剂的用药规律。方法:根据纳入与排除标准筛选出《中医方剂大辞典》中治疗痛经的方剂,对其进行方药名称规范,运用Excel、IBM SPSS Modeler 18.0、IBM SPSS Statistics 26.0等进行频数统计、关联规则分析、聚类分析及因子分析。结果:共纳入151首治疗痛经的方剂,涉及中药191味;其中出现频次≥10的中药为当归、川芎、延胡索、白芍、香附、甘草等共36味,药效以补虚药、活血化瘀药、理气药为主,药性偏温、寒、平,药味则偏辛、苦、甘,多入肝、脾、心经。选取出现频次≥25的13味高频药物进行关联规则分析,得到“当归-川芎“”当归-香附-川芎“”当归-熟地黄-白芍-川芎”等常用药对21组,并通过提升关联规则的支持度得到“当归-川芎-白芍”核心组合。进行高频药物聚类分析得出4类药物组合,C1:白芍、熟地黄、川芎、当归;C2:牡丹皮、茯苓;C3:香附、甘草;C4:桃仁、红花,因子分析得到5个公因子。结论《:中医方剂大辞典》中治疗痛经以补血活血、化瘀散结、疏肝理气为主。

Development of a Data Mining Model to Detect Cardiovascular Disease

In the previous publication on Volume 15 No 9, September 30, 2022 of IJCN, we analyzed “Data Mining as a Technique for Healthcare Approach”. In this edition, emphasis has been made on the “Development of Data Mining Model to Detect Cardiovascular Diseases (CVD)”. A Software was developed using the internationally accepted Software Engineering Methodology (SSADM), coding by OOP and packing by prototyping methodologies. Among others, this paper discusses;Cardiovascular diseases, Data Mining Algorithm, Analysis and Information flow of the Present System, Data flow and High level flow of the Proposed System, Modulating, System Design and Development, Hardware and Software Specifications, System Testing, Evaluation and Documentation.

大数据分析在中医教学评价中的应用

在大数据和数字化转型的大背景下,文章分析、阐述了目前中医教学评价的困难以及如何将大数据技术应用在中医教学评价过程中,总结了大数据在中医教学评价过程中的应用方法。首先需要更加广泛地采集数据,包括静态数据、实时数据、非结构化数据等;然后通过系统化的数据标准体系构建,利用数据处理技术,建立全面的教学评价数据仓库;最后结合教学评价业务目标,构建动态数据分析模型,持续不断地以可视化方式直观输出评价结果,真正帮助教师、学生、教学管理者提高教学评价效率,为教学改革提供支撑。

基于数据挖掘方法探讨脾胃名家李德新教授临床诊疗虚劳疾病规律

目的基于数据挖掘技术探讨李德新教授治疗虚劳疾病的用药规律。方法收集、整理李德新教授临床诊疗虚劳疾病的病例,运用频数、频率、聚类分析、关联分析等方法对病例中的证型诊断和处方用药进行统计分析和数据挖掘,归纳整理后对医案数据进行科学计算。结果共纳入治疗标准医案虚劳741例,挖掘出与疾病相对应的高频症状、高频证型、高频中药,并对中药进行聚类分析得到有效配伍,同时进行症状、证型与相关中药的关联分析。结论利用数据挖掘技术,可初步揭示李德新教授临床诊疗虚劳疾病的规律,从虚劳论治,培补虚劳对于疾病的治疗具有重要意义,有助于更好地传承李德新教授的学术思想,具有较高的临床参考和学习价值。

GatherTweet: A Python Package for Collecting Social Media Data on Online Events

Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.