• Select Article Type
  • Abstract Supplements
  • Blood Group Review
  • Call to Arms
  • Communications
  • Hypothesis
  • In Memoriam
  • Interview
  • Introduction
  • Letter to the Editor
  • Short Report
  • abstract
  • Abstracts
  • Article
  • book-review
  • case-report
  • case-study
  • Clinical Practice
  • Commentary
  • Conference Presentation
  • conference-report
  • congress-report
  • Correction
  • critical-appraisal
  • Editorial
  • Editorial Comment
  • Erratum
  • Events
  • in-memomoriam
  • Letter
  • Letter to Editor
  • mini-review
  • minireview
  • News
  • non-scientific
  • Obituary
  • original-paper
  • original-report
  • Original Research
  • Pictorial Review
  • Position Paper
  • Practice Report
  • Preface
  • Preliminary report
  • Product Review
  • rapid-communication
  • Report
  • research-article
  • Research Communicate
  • research-paper
  • Research Report
  • Review
  • review -article
  • review-article
  • review-paper
  • Review Paper
  • Sampling Methods
  • Scientific Commentary
  • serologic-method-review
  • short-communication
  • short-report
  • Student Essay
  • Varia
  • Welome
  • Select Journal
  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls


Article | 01-September-2015


A fuzzy model for failure rate with the consideration of the effects of uncertain factors in distribution reliability evaluation is presented. The possibility and credibility distribution analyzed on the basis of sample datum are used for quantifying effects of the uncertainty done to failure rate. Mathematically, the failure rate can be obtained in the interval integration. Moreover, aiming to make the calculating quantity of system reliability evaluation simple and easy, the fuzzy clustering

H.X. Tian, W.F Wu, P. Wang, H.Z. Li

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1484–1504

Article | 08-April-2018

Improved K-means Algorithm Based on optimizing Initial Cluster Centers and Its Application

Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means

Xue Linyao, Wang Jianguo

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 2, 9–16

Article | 30-November-2018

Application of K-means Algorithm in Geological Disaster Monitoring System

the subset of data of interest. The data object segmentation variable determines the formation of clustering, which in turn affects the correct interpretation of the clustering results, and ultimately affects the stability of the clustering clusters after the new data objects are added. Before the K-means clustering related data mining, the sample data set related to the data mining clustering analysis should be extracted from the original data object set, and it is not necessary to use all the

Wang Jianguo, Xue Linyao

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 3, 16–22

No Record Found..
Page Actions