scholarly journals Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hong Huang ◽  
Risheng Deng

Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. Moreover, this paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. In addition, this paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself. The research results show that the model constructed in this paper has certain practical effects and can be applied to actual competitions.

2020 ◽  
Vol 10 (7) ◽  
pp. 1660-1668
Author(s):  
Lingmei Wu ◽  
Yan Wei ◽  
Qingyun Wang ◽  
Shuanmeng Ji

With the continuous development of information construction in the medical industry, a large amount of data related to bone metastasis of prostate cancer can be found in the medical database. It includes a large number of inspection indicators, medical images, and background information such as gender, age, height, weight, and previous medical history. The content is very rich and detailed. The nuclear medicine image processing technology and data mining technology are organically combined to study the feature extraction and loading method of nuclear medicine image data, and the classification method of medical image data, thereby assisting doctors in decision-making diagnosis process and improving accuracy. These have important theoretical significance and broad application prospects. Therefore, based on the nuclear medicine imaging data, this study utilized data mining technology to analyse the nuclear medical imaging data of prostate cancer bone metastasis, and finds and summarizes the imaging features and developmental rules of prostate cancer bone metastasis. So, a BP neural network diagnosis matrix for prostate cancer bone metastasis was constructed. This is valuable and meaningful for the diagnosis, treatment and even medical research of bone metastasis of prostate cancer.


Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.


2021 ◽  
Vol 11 (3) ◽  
pp. 930-937
Author(s):  
Yubo Xie

Ultrasound medical imaging technology is one of the main methods of medical non-invasive diagnosis, and it is the focus of research in the medical field at home and abroad. Medical images have a large amount of data and contain a wealth of image feature information and rules, which need to be studied and understood. Therefore, the research of data mining technique for reading medical images has become a very important field in the interdisciplinary research of medical and computer science. The high resolution of medical images, the mass of data, and the complexity of image feature expressions make the research of data mining technology in medical images of great academic value and broad application prospects. At present, research on data mining for medical images has just started, and there are still many problems in the direct application of existing data mining methods. Researching and exploring the theoretical and practical problems of medical image data mining, such as data mining methods and algorithms suitable for medical image, which has significant and crucial value, and it is of great importance to help physicians in clinical diagnosis of medical images. This article introduces the background, definition and basic process of data mining technology, the characteristics of medical imaging data and the key techniques of medical image data mining. In view of the data mining research of human abdominal medical images is a completely new field, human abdominal imaging is the most complicated part of medical images. Solving the problem of abdominal imaging is of great value to the entire medical image. For regional medical image big data mining, we can use ultrasound images of the human abdomen. The clustering feature extraction algorithm and its implementation based on the approximate density structure of medical images proposed in this article, and innovative research results such as classification rule mining methods, are used to mine medical image data research, automatic diagnosis of clinical medical images, and early diagnosis of clinical medicine are of great significance.


Author(s):  
Terry Caelli

Most data warehousing and mining involves storing and retrieving data either in numerical or symbolic form, varying from tables of numbers to text. However, when it comes to everyday images, sounds, and music, the problem turns out to be far more complex. The major problem with image data mining is not so much image storage, per se, but rather how to automatically index, extract, and retrieve image content (content-based retrieval [CBR]). Most current image data-mining technologies encode image content by means of image feature statistics such as color histograms, edge, texture, or shape densities. Two well- known examples of CBR are IBM’s QBIC system used in the State Heritage Museum and PICASSO (Corridoni, Del Bimbo & Pala, 1999) used for the retrieval of paintings. More recently, there have been some developments in indexing and retrieving images based on the semantics, particularly in the context of multimedia, where, typically, there is a need to index voice and video (semantic-based retrieval [SBR]). Recent examples include the study by Lay and Guan (2004) on artistry-based retrieval of artworks and that of Benitez and Chang (2002) on combining semantic and perceptual information in multimedia retrieval for sporting events.


Author(s):  
Richard C. Kittler

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.


2021 ◽  
pp. 1-11
Author(s):  
Liu Narengerile ◽  
Li Di ◽  

At present, the college English testing system has become an indispensable system in many universities. However, the English test system is not highly humanized due to problems such as unreasonable framework structure. This paper combines data mining technology to build a college English test framework. The college English test system software based on data mining mainly realizes the computer program to automatically generate test papers, set the test time to automatically judge the test takers’ test results, and give out results on the spot. The test takers log in to complete the test through the test system software. The examination system software solves the functions of printing test papers, arranging invigilation classrooms, invigilating teachers, invigilating process, collecting test papers, scoring and analyzing test papers in traditional examinations. Finally, this paper analyzes the performance of this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects.


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