Intakosum, Sarun

Preferred name

Intakosum, Sarun

Main Affiliation

School of Science

sarun.in@kmitl.ac.th

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XER: A recommendation for XML element
(2010-08-11)
Vacharaskunee, Sutheetutt
;
Intakosum, Sarun
The strong point of the XML is that it allows document owners to describe their documents in their own format (structure and element names). There might be the same information has been describe in various ways. When user wants to search some information from XML documents, user might not retrieve all related results because of the difference of elements (tag names). To retrieve all related results, user needs to have queries for all possible elements. XML Element Recommendation (XER) is an idea to make XML documents easier for searching. It receives XML document as an input and compares that input with XML documents from database. It compares word similarity and semantic of each element. For semantic similarity of each element, it checks those elements to find which element (word) should be used by using word ranking (most common words). The output is a recommendation for each element of input XML document. © 2010 IEEE.
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An efficient parallel construction of optimal independent spanning trees on hypercubes
(2012-12-01)
Werapun, Jeeraporn
;
Intakosum, Sarun
;
Boonjing, Veera
Reliable data broadcasting on parallel computers can be achieved by applying more than one independent spanning tree (IST). Using k-IST-based broadcasting from root r on an interconnection network (N=2 k) provides k-degree fault tolerance in broadcasting, while construction of optimal height k-ISTs needs more time than that of one IST. In the past, most research focused on constructing k ISTs on the hypercube Qk, an efficient communication network. One sequential approach utilized the recursive feature of Qk to construct k ISTs working on a specific root (r)=0 in O(kN) time. Another parallel approach was introduced for generating k ISTs with optimal height on Qk, based on HDLS (Hamming Distance Latin Square), single pointer jumping, which is applied for a source (r)=0 in O( k2) time for successful broadcasting in O(k). For broadcasting from r≠0, those existing approaches require a special routine to reassign new nodes' IDs for logical r=0. This paper proposes a flexible and efficient parallel construction of k ISTs with optimal height on Qk, a generalized approach, for an arbitrary root (r=0,1,2,..., or 2 k-1) in O(k) time. Our focus is to introduce the more efficient time (O(k)) of preprocessing, based on double pointer jumping over O( k2) of the HDLS approach. We also prove that our generalized parallel k-IST construction (arbitrary r) with optimal height on Qk is correctly set in efficient O(k) time. Finally, experiments were performed by simulation to investigate the fault-tolerance effect in reliable broadcasting. Experimental results showed that our efficient ISTs yielded 10%-20% fault tolerance for successful broadcasting (on N=16-1024 PEs). © 2012 Elsevier Inc. All rights reserved.
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Borderline over-sampling in feature space for learning algorithms in imbalanced data environments
(2016-01-01)
Savetratanakaree, Kittipat
;
Sookhanaphibarn, Kingkarn
;
Intakosum, Sarun
;
Thawonmas, Ruck
In this paper, we propose a new approach to over-sample new minority-class instances along the borderline using the Euclidean distance in the feature space to improve support vector machine (SVM) performance in imbalanced data environments. SVM has been an outstandingly successful classifier in a wide variety of applications where balanced class data distribution is assumed. However, SVM is ineffective when coping with imbalanced datasets whereby the majorityclass instances far outnumber the minority-class instances. Our new approach, called Borderline Over-sampling in the Feature Space, can deal with imbalanced data to effectively recognize new minority-class instances for better classification with SVM. The results of our class prediction experiments using the proposed approach demonstrate better performance than the existing SMOTE, Borderline-SMOTE and borderline over-sampling methods in terms of the g-mean and F-measure.
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XML element recommendation by semantic ranking
(2010-05-28)
Vacharaskunee, Sutheetutt
;
Intakosum, Sarun
From the strong point of XML that allows document owners to describe their documents in their own format, it is difficult to search information if those XML documents use different formats. Moreover, users might not retrieve all relevant information from differently formatted XML documents. To allow users to retrieve all relevant results, users need to have as many as queries for all possible formats. SXER (Semantic Ranking for XML Element Recommendation) is an idea to make XML documents easier for searching. It receives XML document as an input, checks all possible semantics for each element, and checks those semantic elements to find which element (word) should be used. The output is a recommendation for each element of input XML document. ©2010 IEEE.
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An approach to XML tag recommendation
(2011-01-01)
Vacharaskunee, Sutheetutt
;
Intakosum, Sarun
The one strong point of XML is that it allows document owners to create their own structure and tag names. It might be that some XML documents contain the same information but use a different structure and/or tag names that have the same meaning but in different words. It will be difficult not only to search all relevant information in one query but also to use exchanged information right away. If the sender and receiver use different formats of XML, then either the sender or receiver has to reformat his XML. The best way to make searching and/or exchanging information easier is to ensure that all XML documents are in the same format. XML Tag Recommendation (XTR) is introduced to support this idea. XTR is based on case-based reasoning framework. It collects the most used tag name as a solution for future use. When you insert a new problem to XTR, it finds the most similar case and recommends the solution from that case to the XML document owner. The XML document owner can change his document format into the recommended solution format. Thus, the XML documents are in the same format. This makes searching and/or exchanging information more powerful. © 2011 IEEE.
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Qos-security metrics based on ITIL and COBIT standard for measurement web services
(2012-06-27)
Charuenporn, Pattama
;
Intakosum, Sarun
Web Services have been widely adopted in business projects, and almost all Web Service developers agree that security factors are the principal components that must be taken into consideration. A large number of security metrics and measurements is available for specific business needs, and the best practice for different business demands is therefore needed if the quality of service security metrics (Qos-SM) is to be developed. This research proposes a new way of developing Qos-SM using Qos ontology mapping with two information system standards, COBIT and ITIL, as a result of which new Qos-SM are developed. In order to prove the correctness and precision of the metrics, the researchers have used the metrics to measure the level of security quality from Web service data sets. The experimental results, based on vector analysis, show that the same level of security quality is attained with both of the metrics developed and the metrics from previous research. This research also represents the metrics in the form of a class diagram, thus facilitating its application in the organization. © J.UCS.
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OPI model: A methodology for development metric based on outcome oriented
(2011-07-21)
Thammarak, Kaenchan
;
Intakosum, Sarun
Metric is extremely important for software development. It is a tool for measuring software in both quantitative and qualitative aspects. In addition, metric can be used for determining an achievement of the goals. There are some problems that bring difficulties to the design and development of software metrics. Among them, a number of software perspectives that metrics must handle and the way to apply metrics results to support business outcomes are considered important. This paper proposes OPI as a model for implementing software metrics based on business outcomes. There are three steps in this model, defining outcomes (O), defining perspectives (P), and defining indicators (I). A case study has been developed to assess the use of the proposed model. Forty undergraduate students, who have basic knowledge in software metric, use the model to develop metrics from the case study. The preliminary evaluation is based on usability aspect. The result has show that 85 percents of the students think that the model is easy to understand, where 8 and 7 percents say that it is moderate and hard respectively. © 2011 IEEE.
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High candidates generation: A new efficient method for mining share-frequent patterns
(2017-11-01)
Nawapornanan, Chayanan
;
Intakosum, Sarun
;
Boonjing, Veera
The share frequent patterns mining is more practical than the traditional frequent patternset mining because it can reflect useful knowledge such as total costs and profits of patterns. Mining share-frequent patterns becomes one of the most important research issue in the data mining. However, previous algorithms extract a large number of candidate and spend a lot of time to generate and test a large number of useless candidate in the mining process. This paper proposes a new efficient method for discovering share-frequent patterns. The new method reduces a number of candidates by generating candidates from only high transaction-measure-value patterns. The downward closure property of transaction-measure-value patterns assures correctness of the proposed method. Experimental results on dense and sparse datasets show that the proposed method is very efficient in terms of execution time. Also, it decreases the number of generated useless candidates in the mining process by at least 70%.
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XML document recommendation by using case based reasoning
(2010-09-01)
Vacharaskunee, Sutheetutt
;
Intakosum, Sarun
Case Based Reasoning (CBR) is the process of solving new problem based on the solutions of similar previous problems. XML is a markup language that allows document owners describe their data in their own formats. There might be the same information has been described in various ways. This makes searching those information from different formats difficult. CXDR (CBR for XML Document Recommendation) is an idea to make searching XML documents easier. CXDR applies CBR framework to collect the mostly use XML document and recommend it to its user. CBR in CXDR consists of two parts, cases and knowledge. The case is XML documents and the knowledge is an XML document recommendation. The recommendation from CXDR is the most use XML element names and structure. © 2010 IEEE.
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Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend
(2016-01-01)
Inthachot, Montri
;
Boonjing, Veera
;
Intakosum, Sarun
This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Intakosum, Sarun

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