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.

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.

Data mining techniques在冶金领域的应用

介绍了Data mining techniques产生背景、发展情况、优化过程,以及在冶金领域的应用。

Data Mining as a Technique for Healthcare Approach

Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. With advance research in health sector, there is multitude of Data available in healthcare sector. The general problem then becomes how to use the existing information in a more useful targeted way. Data Mining therefore is the best available technique. The objective of this paper is to review and analyse some of the different Data Mining Techniques such as Application, Classification, Clustering, Regression, etc. applied in the Domain of Healthcare.

An Approach of Tropospheric Correction for VLBI Phase-Referencing using GPS Data

The dominant source of error in VLBI phase-referencing is the troposphere at observing frequencies above 5 GHz. We compare the tropospheric zenith delays derived from VLBI and GPS data at VLBA stations collocated with GPS antennas. The systematic biases and standard deviations both are at the level of sub-centimeter. Based on this agreement, we suggest a new method of tropospheric correction in phase-referencing using combined VLBI and GPS data.

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

Correcting the Contamination of Second-order Spectra:Improving HαMeasurements in Reverberation Mapping Campaigns

Long-term spectroscopic monitoring campaigns on active galactic nuclei(AGNs)provide a wealth of information about its interior structure and kinematics.However,a number of the observations suffer from the contamination of second-order spectra(SOS)which will introduce some undesirable uncertainties at the red side of the spectra.In this paper,we test the effect of SOS and propose a method to correct it in the time domain spectroscopic data using the simultaneously observed comparison stars.Based on the reverberation mapping(RM)data of NGC 5548 in2019,one of the most intensively monitored AGNs by the Lijiang 2.4 m telescope,we find that the scientific object,comparison star,and spectrophotometric standard star can jointly introduce up to~30%SOS for Grism 14.This irregular but smooth SOS significantly affects the flux density and profile of the emission line,while having little effect on the light curve.After applying our method to each spectrum,we find that the SOS can be corrected effectively.The deviation between corrected and intrinsic spectra is~2%,and the impact of SOS on time lag is very minor.This method makes it possible to obtain the HαRM measurements from archival data provided that the spectral shape of the AGN under investigation does not have a large change.

基于QAR数据的波音747-400飞机巡航报文解析研究（英文）

为充分挖掘QAR数据中所包含的大量原始监控数据,检验ACARS报文的可靠性,更好地协助航空公司进行飞机和发动机的状态监控及故障诊断,对发动机稳态报文DFD的报文结构进行了解析研究,提出了一种从QAR数据中提取满足触发条件的DFD报文的研究思路和方法,以丰富发动机性能监控的信息量。通过对B747-400飞机实际ACARS报文数据和所提取的报文数据进行计算分析,验证了该方法的可靠性,具有良好的工程应用前景。

基于数据挖掘技术探讨灸法治疗不孕症的临床应用规律

目的利用数据挖掘技术探讨灸法治疗不孕症的临床应用规律,为临床灸法治疗不孕症提供诊疗依据。方法计算机检索中文期刊全文数据库(CNKI)、万方数据库(WANFANG)、维普数据库(VIP)、中国生物医学文献数据库(CBM)、PubMed中2010年1月1日—2022年1月1日关于灸法治疗不孕症的相关文献,根据排除纳入标准筛选文献并提取其中灸法种类、灸量、治疗方法、主要证型、腧穴频次、腧穴所在部位及归经等信息,利用Excel2019、SPSS24.0、SPSS Modeler18.0对数据进行数据统计分析、聚类分析、关联规则分析以及复杂网络分析。结果文献检索与筛选共获取文献101篇。文献数据提取并数理统计分析后获得腧穴49个,总频次为374,其中关元穴频次最高。腧穴涉及十二经脉以及任脉和督脉,特定穴中交会穴腧穴数最高,胸腹部穴位最多。灸法方面,治疗方法种类较多,其中灸法与中药结合为主;灸法种类分为9类,其中以温和灸为主;治疗时间多以20~30min或2~5壮艾炷为主;治疗频率多为1次/d。聚类分析获取4组腧穴聚类,关联规则分析获取8个关联组,复杂网络分析获取腧穴复杂网络图。结论灸法治疗不孕症以温和灸、热敏灸为主,并注重施灸的灸量与频次,常用的治疗方法为灸法与中药组合,常用的施治腧穴为关元穴、神阙穴、子宫穴、三阴交穴和气海穴,其结果可以为灸法治疗不孕症临床应用提供参考依据。

The RFI Fast Mitigation Algorithm Based on Block LMS Filter

The radio telescope possesses high sensitivity and strong signal collection capabilities.While receiving celestial radiation signals,it also captures Radio Frequency Interferences(RFIs)introduced by human activities.RFI,as signals originating from sources other than the astronomical targets,significantly impacts the quality of astronomical data.This paper presents an RFI fast mitigation algorithm based on block Least Mean Square(LMS)algorithm.It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block.This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem.The algorithm is tested using baseband data from the Parkes 64 m radio telescope's pulsar observations and simulated data.The results confirm the algorithm's effectiveness,as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

计算机数据挖掘技术的开发及应用探究

近年来,随着数据量的不断增大和数据来源的多样化,数据挖掘技术面临着越来越大的挑战。针对如何处理大规模数据、如何提高数据挖掘的准确性和效率、如何保证数据挖掘的安全性等问题,需要进一步深入研究。基于此,本文探讨计算机数据挖掘技术的概念和基本原理,分析计算机数据挖掘技术开发,研究计算机数据挖掘技术的应用,旨在为相关领域提供参考。

基于关联规则数据挖掘技术的图书阅读推荐系统

图书馆网站的推荐系统是帮助用户找到合适图书的重要工具。随着互联网技术的迅猛发展和图书数量的增加,通过图书馆搜索系统寻找图书变得耗时且困难。文章提出了一种专注于大学图书馆的图书阅读推荐系统,旨在满足用户在相同专业和学科领域的兴趣。结果表明,文章所开发的挖掘技术适用于在图书馆中实施推荐图书工具,其准确性值较其他技术更高。

Optical fiber positioning based on four-quadrant detector with Gaussian fitting method

With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST, also called the Guo Shou Jing Telescope) is a special reflecting Schmidt telescope. LAMOST’s special design allows both a large aperture (effective aperture of 3.6 m–4.9 m) and a wide field of view (FOV) (5°). It has an innovative active reflecting Schmidt configuration which continuously changes the mirror’s surface that adjusts during the observation process and combines thin deformable mirror active optics with segmented active optics. Its primary mirror (6.67m×6.05 m) and active Schmidt mirror (5.74m×4.40 m) are both segmented, and composed of 37 and 24 hexagonal sub-mirrors respectively. By using a parallel controllable fiber positioning technique, the focal surface of 1.75 m in diameter can accommodate 4000 optical fibers. Also, LAMOST has 16 spectrographs with 32 CCD cameras. LAMOST will be the telescope with the highest rate of spectral acquisition. As a national large scientific project, the LAMOST project was formally proposed in 1996, and approved by the Chinese government in 1997. The construction started in 2001, was completed in 2008 and passed the official acceptance in June 2009. The LAMOST pilot survey was started in October 2011 and the spectroscopic survey will launch in September 2012. Up to now, LAMOST has released more than 480 000 spectra of objects. LAMOST will make an important contribution to the study of the large-scale structure of the Universe, structure and evolution of the Galaxy, and cross-identification of multiwaveband properties in celestial objects.

Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

Clustering Approach for Analyzing the Student’s Efficiency and Performance Based on Data

The academic community is currently confronting some challenges in terms of analyzing and evaluating the progress of a student’s academic performance. In the real world, classifying the performance of the students is a scientifically challenging task. Recently, some studies apply cluster analysis for evaluating the students’ results and utilize statistical techniques to part their score in regard to student’s performance. This approach, however, is not efficient. In this study, we combine two techniques, namely, k-mean and elbow clustering algorithm to evaluate the student’s performance. Based on this combination, the results of performance will be more accurate in analyzing and evaluating the progress of the student’s performance. In this study, the methodology has been implemented to define the diverse fascinating model taking the student test scores.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

R-band host galaxy contamination of TeV γ-ray blazar Mrk 501： effects of aperture size and seeing

We simulated the R-band contribution of the host galaxy of TeV γ-ray BL Lac object Mrk 501 in different aperture sizes and seeing conditions. An intensive set of observations was acquired with the 1.02m optical telescope, managed by Yunnan Observatories, from 2010 May 15 to 18. Based on the host subtraction data usually used in the literature, the subtraction of host galaxy contamination results in significant seeing-brightness correlations. These correlations would lead to illusive large amplitude variations at short timescales, which will mask the intrinsic microvariability, thus giving rise to difficulty in detecting the intrinsic microvariability. Both aperture size and seeing condition influence the flux measurements, but the aperture size impacts the result more significantly. Based on the parameters of an elliptical galaxy provided in the literature, we simulated the host contributions of Mrk 501 in different aperture sizes and seeing conditions. Our simulation data of the host galaxy obviously weaken these significant seeing-brightness correlations for the host-subtracted brightness of Mrk 501, and can help us discover the intrinsic short timescale microvariability. The pure nuclear flux is -8.0 mJy in the R band, i.e., the AGN has a magnitude of R - 13.96 mag.

基于数据挖掘复式补泻手法“烧山火”的临床应用研究

目的基于数据挖掘探讨复式补泻手法烧山火适应病症及其选穴规律分析,为临床应用该补泻手法提供参考。方法以“烧山火”为关键词,收集中国知网、万方、维普2001-2021年烧山火手法的临床文献建立数据库。对纳入穴位进行频数统计、关联规则分析、聚类分析、复杂网络分析。结果共纳入研究文献162篇,涉及疾病84种,涉及腧穴总数132个,总取穴频次共计582次,治疗疾病前4位分别是中风及中风后遗症、周围性面瘫、腰椎间盘突出、神经根型颈椎病,取穴频次前3的腧穴是:足三里、三阴交、关元,留针时间频次前3位是30 min、不留针、20 min。结论烧山火手法对于筋骨关节之痹症、中风相关疾病疗效显著且取穴方法有规律可循,其文献分析可为临床应用该手法治疗疾病提供参考。