Analysis of wild fire behaviour in wild conservation area using image data mining

2015 ◽  
Author(s):  
Divya T.L. ◽  
Vijayalakshmi M.N.
Keyword(s):  
Data Mining ◽  
Image Data ◽  
Fire Behaviour ◽  
Conservation Area ◽  
Wild Fire

Algorithms for Data Mining

2008 ◽  
pp. 1301-1319
Author(s):  
Tadao Takaoka ◽  
Nigel K.L. Pope ◽  
Kevin E. Voges
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Image Data ◽  
Regression Trees ◽  
Clustering Techniques ◽  
Data Mining Algorithms ◽  
Mining Algorithms

In this chapter, we present an overview of some common data mining algorithms. Two techniques are considered in detail. The first is association rules, a fundamental approach that is one of the oldest and most widely used techniques in data mining. It is used, for example, in supermarket basket analysis to identify relationships between purchased items. The second is the maximum sub-array problem, which is an emerging area that is yet to produce a textbook description. This area is becoming important as a new tool for data mining, particularly in the analysis of image data. For both of these techniques, algorithms are presented in pseudo-code to demonstrate the logic of the approaches. We also briefly consider decision and regression trees and clustering techniques.

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
Keyword(s):  
Data Mining ◽  
Feature Recognition ◽  
Image Data ◽  
Technical Analysis ◽  
Machine Learning Algorithms ◽  
Technical Training ◽  
Image Feature ◽  
Mining Technology ◽  
Movement Characteristics ◽  
Real Time Tracking

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.

scholarly journals A Study on Coastline Extraction and Its Trend Based on Remote Sensing Image Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yun Zhang ◽  
Xueming Li ◽  
Jianli Zhang ◽  
Derui Song
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Prediction Method ◽  
Image Data ◽  
Chromatic Aberration ◽  
Grey Prediction ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Matching Algorithm ◽  
Edge Detection Method

In this paper, data mining theory is applied to carry out the field of the pretreatment of remote sensing images. These results show that it is an effective method for carrying out the pretreatment of low-precision remote sensing images by multisource image matching algorithm with SIFT operator, geometric correction on satellite images at scarce control points, and other techniques; the result of the coastline extracted by the edge detection method based on a chromatic aberration Canny operator has a height coincident with the actual measured result; we found that the coastline length of China is predicted to increase in the future by using the grey prediction method, with the total length reaching up to 19,471,983 m by 2015.

scholarly journals A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
José García ◽  
Christopher Pope ◽  
Francisco Altimiras
Keyword(s):  
Data Mining ◽  
Early Detection ◽  
Mobile Application ◽  
Image Data ◽  
Lobesia Botrana ◽  
Segmentation Algorithm ◽  
Gray Scale ◽  
Image Capture ◽  
Major Pest ◽  
Novel Method

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

The Value of Data Mining Technology in the Diagnosis of Bone Metastases from Prostate Cancer Based on Nuclear Medicine Image

2020 ◽  
Vol 10 (7) ◽  
pp. 1660-1668
Author(s):  
Lingmei Wu ◽  
Yan Wei ◽  
Qingyun Wang ◽  
Shuanmeng Ji
Keyword(s):  
Prostate Cancer ◽  
Data Mining ◽  
Nuclear Medicine ◽  
Bone Metastasis ◽  
Image Data ◽  
Background Information ◽  
Imaging Features ◽  
Imaging Data ◽  
Nuclear Medicine Imaging ◽  
Mining Technology

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.

Study on the Data Mining of Image Storage on the Hadoop Cloud Platform

2014 ◽  
Vol 543-547 ◽  
pp. 3667-3670 ◽  
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
Satellite Remote Sensing ◽  
Medical Image ◽  
Rapid Development ◽  
Image Data ◽  
Challenging Problem ◽  
Cloud Platform ◽  
Huge Number ◽  
Image Storage ◽  
Usable Information

Along with the rapid development of image acquisition and image storage, a huge number of usable image data are obtained by people, such as satellite remote sensing image data, medical image data, etc. Data mining of images is to analyze these useful images and extract the usable information from them. How to effectively store rapidly make data mining for the increasing images has become the most challenging problem faced by people. This paper focuses on data mining of the massive images with the help of the Hadoop cloud platform.

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