scholarly journals Construction of an Intelligent Processing Platform for Equestrian Event Information Based on Data Fusion and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhong Wu ◽  
Chuan Zhou
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Data Fusion ◽  
Recall Rate ◽  
Development Environment ◽  
Intelligent Processing ◽  
Equestrian Sports ◽  
Event Information ◽  
Processing Platform ◽  
Precision Rate

In the past two years, equestrian sports have become more and more popular with the public. Due to the comprehensive development of equestrian preparations for the 2020 Olympic Games in China, the equestrian sports industry presents an unprecedented favorable development environment in China. This article is aimed at studying the construction of an equestrian event information intelligent processing platform based on data fusion and data mining. This article introduces the relevant theoretical knowledge of data mining and data fusion, including the description of the concept of data mining, the common analysis methods and algorithms of data mining, the basic concepts of data fusion, and the functional structure of data fusion. It discusses various algorithms in cluster analysis and focuses on the analysis of distance measurement and similarity coefficient in cluster analysis. In the experimental part, in order to intelligently process and acquire information, an information intelligent processing platform is constructed based on data fusion and data mining technology. The experimental results of this paper show that the precision rate, recall rate, and F -score of the platform under closed test are much higher than those under open test, and the precision rate is increased by about 7.26%.

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

Research on Key Technology of Data Mining for Volleyball Game Based on Service System

2014 ◽  
Vol 543-547 ◽  
pp. 4698-4701
Author(s):  
Juan Wang
Keyword(s):  
Data Mining ◽  
High Precision ◽  
Service System ◽  
Processing System ◽  
Machining Process ◽  
Precision Machining ◽  
Intelligent Processing ◽  
Mining Model ◽  
Processing Cycle ◽  
Key Parameter

During the processing of aircraft and other high precision machinery workpieces, if using the traditional machining methods, it will consume a amount of machining costs, and the mechanical processing cycle is long. In this context, this paper designs a kind of robot intelligent processing system with high precision machinery. And it has realized the intelligent online control on the machining process by using the high precision machining intelligent online monitoring technology and the numerical simulation prediction technology. Finally, this system is introduced into the process of data mining for volleyball game, and designs the partial differential variational data mining model, which has realized the key parameter data mining of volleyball games service system, and has provided reliable parameters and technical support for the training of volleyball players.

scholarly journals Exploring high reliable substructures in auto-reconstructions of a neuron

2021 ◽  
Author(s):  
Yishan He ◽  
Jiajin Huang ◽  
Gaowei Wu ◽  
Jian Yang
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Neuron Tracing ◽  
Digital Reconstruction ◽  
High Recall Rate ◽  
Multiple Species ◽  
Multiple Reference ◽  
Precision Rate

Abstract The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron’s reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

A Data Distribution View of Clustering Algorithms

2011 ◽  
pp. 374-381 ◽  
Author(s):  
Junjie Wu ◽  
Jian Chen ◽  
Hui Xiong
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Point Of View ◽  
Group Method ◽  
Data Sets ◽  
Distribution Point

Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing the objects into groups (clusters), such that objects in a cluster are more similar to each other than objects in other clusters. Cluster analysis has long played an important role in a wide variety of fields, such as psychology, bioinformatics, pattern recognition, information retrieval, machine learning, and data mining. Many clustering algorithms, such as K-means and Unweighted Pair Group Method with Arithmetic Mean (UPGMA), have been wellestablished. A recent research focus on clustering analysis is to understand the strength and weakness of various clustering algorithms with respect to data factors. Indeed, people have identified some data characteristics that may strongly affect clustering analysis including high dimensionality and sparseness, the large size, noise, types of attributes and data sets, and scales of attributes (Tan, Steinbach, & Kumar, 2005). However, further investigation is expected to reveal whether and how the data distributions can have the impact on the performance of clustering algorithms. Along this line, we study clustering algorithms by answering three questions: 1. What are the systematic differences between the distributions of the resultant clusters by different clustering algorithms? 2. How can the distribution of the “true” cluster sizes make impact on the performances of clustering algorithms? 3. How to choose an appropriate clustering algorithm in practice? The answers to these questions can guide us for the better understanding and the use of clustering methods. This is noteworthy, since 1) in theory, people seldom realized that there are strong relationships between the clustering algorithms and the cluster size distributions, and 2) in practice, how to choose an appropriate clustering algorithm is still a challenging task, especially after an algorithm boom in data mining area. This chapter thus tries to fill this void initially. To this end, we carefully select two widely used categories of clustering algorithms, i.e., K-means and Agglomerative Hierarchical Clustering (AHC), as the representative algorithms for illustration. In the chapter, we first show that K-means tends to generate the clusters with a relatively uniform distribution on the cluster sizes. Then we demonstrate that UPGMA, one of the robust AHC methods, acts in an opposite way to K-means; that is, UPGMA tends to generate the clusters with high variation on the cluster sizes. Indeed, the experimental results indicate that the variations of the resultant cluster sizes by K-means and UPGMA, measured by the Coefficient of Variation (CV), are in the specific intervals, say [0.3, 1.0] and [1.0, 2.5] respectively. Finally, we put together K-means and UPGMA for a further comparison, and propose some rules for the better choice of the clustering schemes from the data distribution point of view.

scholarly journals Method for Retrieving Digital Agricultural Text Information Based on Local Matching

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1103
Author(s):  
Yue Song ◽  
Minjuan Wang ◽  
Wanlin Gao
Keyword(s):  
Recall Rate ◽  
Retrieval Method ◽  
Retrieval Time ◽  
Search Results ◽  
Retrieval Efficiency ◽  
Query Tree ◽  
Text Information ◽  
Relationship Of ◽  
The Relationship ◽  
Precision Rate

In order to improve the retrieval results of digital agricultural text information and improve the efficiency of retrieval, the method for searching digital agricultural text information based on local matching is proposed. The agricultural text tree and the query tree are constructed to generate the relationship of ancestor–descendant in the query and map it to the agricultural text. According to the retrieval method of the local matching, the vector retrieval method is used to calculate the digital agricultural text and submit the similarity between the queries. The similarity is sorted from large to small so that the agricultural text tree can output digital agricultural text information in turn. In the case of adding interference information, the recall rate and precision rate of the proposed method are above 99.5%; the average retrieval time is between 4s and 6s, and the average retrieval efficiency is above 99%. The proposed method is more efficient in information retrieval and can obtain comprehensive and accurate search results, which can be used for the rapid retrieval of digital agricultural text information.

A Study in the Technology Implementation of the Network Massive Information Processing Platform

2013 ◽  
Vol 347-350 ◽  
pp. 2993-2997
Author(s):  
Yue Li ◽  
Ran Liu
Keyword(s):  
Data Mining ◽  
Information Processing ◽  
Technology Implementation ◽  
High Performance ◽  
High Efficiency ◽  
Information Storage ◽  
The Internet ◽  
The One ◽  
Processing Platform ◽  
Massive Information

With the popularity and development of the network, the support of the high-performance computer technology becomes increasingly important as the huge information storage and the convenience of Information retrieval function of the internet that attracts more and more people join the netizens team. Therefore, I proposed an Information Processing Platform based on the high performance data mining in order to improve the Internet mass information intelligence parallel processing functions and the integrated development of the systems information storage, management, integration, intelligence processing, data mining and utilization. The propose of this system is to provide certain references and guidance for the technology implementation and realization of the high performance and high efficiency network massive Information Processing Platform as on the one hand, I have analyzed the key technology of the implementation of the platform, on the other hand briefly introduced the implementation of the RDIDC.

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