Conferences: Recent submissions

  • Data Availability in Distributed Patients’ Record System using Virtual Primary Copy 
    Myint, Thida; Htoon, Ei Chaw; Maw, Aung Htein (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Digitization and networked computing in the healthcare sector have resulted in electronic patient records that are stored, managed and shared among different healthcare providers. Replication process enables the data ...
  • Development of Data Warehouse For Computer Universites’ Information 
    Thike, Lynn Lynn; Maw, San San (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Data warehousing is a repository of information collected from multiple data sources, stored under a unified schema, and that usually resides at a single site. Nowadays, Universities’ maintaining dta is needed to build ...
  • Camera Shopping Data Analysis using OLAP Model 
    Wai, Soe Nu; Cho, Aung (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Nowadays, many commercil products and services are available, and all of the principal database management system vendors now have offering in business areas. Readity available high-quality information is very important ...
  • XML Documents Classification using Composite SVM Kernel 
    Soe, Htet Khine (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    More and more structured or semistructured data is stored and exchange in XML format. XML mining becomes increasingly important, especially the study of classification of XML documents. As the number of XMLdocuments on the ...
  • Training Neuarl Network with Partical Swarm Optimization 
    Moe, Khin Thant Zin (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Feed-forward networks are one of the most used neural networks in various domains because of their universal approximation capability. One of the popular algorithms for training multilayer feed-forward network is ...
  • Study on Particle Swarm Optimization based Clustering 
    Moet, Moet (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such that items within a cluster are more “similar” to each other than they are to items in the other clusters. There are many ...
  • Recommender System for Online Learning Using Personalization with K-Means Clustering Algorithm 
    Aein, Hsu Pan; Win, Thandar (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. Recommder systems have proven to be valuable means for online users to cope with the information overload and ...
  • Mining the Classification Rules for Infectious Skin Diseases in Children 
    Myo, Nan Kathy; Khine, May Aye (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    The healthcare industry collects huge amount of healthcare data which, unfortunately, are not mine for discover relation and hidden information for effective decision making. Advance data mining techniques can help remedy ...
  • Mining Multilevel Association Rulesbased on Boolean Matrix for Food Sales shop 
    Yi, Nay; Zaw, Wint Thida (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    The rapid growth in data and the number of database, there is a need for discovering valuable knowledge in large database which have business data. Today, many companies which are to gain profit from their previous business ...
  • Mining Electronic Itemsets by Using EClaT Algorithm with Correlation Analysis 
    Soe, Aye Thandar; Aye, Aye (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Frequent itemsets mining is a discovery of interesting associations and correlations between itemsets in transcational and relational databases. Associaton rule mining is a popular method in the retail sales industry where ...
  • Mining Association Rules for Construction Accessories Shop 
    Thein, Lwin Po Po; Nyunt, Kyi Zar (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules describe events that tend to occur together. ...
  • Incremental Mining of Association Rules by Using Adaptive Algorithm 
    Htoo, Naw Lu Lu (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Data mining has recently attracted tremendous amount ofattention in the database research because of its wide applicability in many areas, including decision support, market strategy and financial forecast. One of the most ...
  • ICU Patients Classification System by Using CART Algorithm 
    Hlaing, Swe Swe (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Nowadays, the advancement in computing technology and the realibility of computers has led to signification changes in the way that data are collected and analyzed. This system is to predict the risk level of patients in ...
  • Heart Attack Risk Level Prediction System Using Navie Bayesian Classification 
    Soe, Mu Mu; Win, Thandar (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Data Mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision support, ...
  • Health Care System for Lung Disease Classification using Modified Gini 
    Wai, Tin Yu; Pa, Win Pa (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Data mining is the process of analyzing data from different prespectives and summarizing it into useful information. Classification has been used for predicting medical diagnosis. Human experts in medical field are frequently ...
  • Diagnosis of TB Disease by Using Decision Tree Induction 
    Naing, Soe Kalayar; Myo, Nyein Nyein (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Decision tree learning algorithm has been successfully using in data minng for training data set. In this system, the main task performed is using inductive methods to the given values of attributes of an unkown object to ...
  • Diagnosis of Diabetes by using Naive Bayesian Classifier 
    Tun, Kaythi; Myo, Nyein Nyein (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Bayseian Classifier can predict class membership probabilities, such as the probability that a given sample belongs to a particular class. They are Powerful tools for solving classification problems in a vareity of domains. ...
  • Comparison of Boosted C5 over ID3 and C4.5 Abdominal Pain Problem 
    Aye, Hnin Nwe; Pa, Win Pa (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Classification can be used as in the form of data analysis that can be used to extract models describing the important data classes. This aim of this paper is to exmaine the performance of decision tree algorithms. ...
  • Comparative Study of Decision Tree Algorithms: ID3 and CART 
    Thu, Su Myat; Pa, Win Pa (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Classification of data objcts based on a predefined knowledge of the objects is a data mining and knowledge management techniques used grouping similar data objects together. It can be defined as supervised learning ...
  • Comparative Study of C4.5 and Boosting using Decision Tree Learning Algorithm 
    Tun, Thinzar; Win, Chit Nilar (Sixth Local Conference on Parallel and Soft Computing, 2011-12-29)
    Data Mining aims to discover novel, interesting, and useful knowledge and patterns from databases. Classification is a data mining technique which addresses the problem of constructing a predication model for a ...

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