Kreesuradej, Worapoj

Preferred name

Alternative Name

Kreesuradej, W.

Main Affiliation

School of Information Technology

worapoj.kr@kmitl.ac.th

Now showing 1 - 10 of 12

Some of the metrics are blocked by your
consent settings
Input selection using binary particle swarm optimization
(2006-01-01)
Amonchanchaigul, Thavit
;
Kreesuradej, Worapoj
Nowadays, multi-layer feed forward networks are often used for modeling complex relationships between the data sets. And if we can choose only the important data from the training sets, it will make the networks less size and can save more time. Because we realize in this point, this paper provides procedure of feature selection to train the neural networks using binary particle swarm optimization. It also introduces the suitable function for the binary particle swarm optimization technique by changing concept in part of member value adjustment function for each particle. © 2006 IEEE.
Some of the metrics are blocked by your
consent settings
State of the art review on thai text-to-speech system
(2008-12-26)
Yimngam, Sukanya
;
Premchaisawadi, Wichian
;
Kreesuradej, Worapoj
Text-to-speech system is a system that converts the input text into speech sound. In Thai language, Thai text-to-speech system was developed in many years. There are many problems with Thai text to speech transformations such as Thai language is a Tonal language that differentiates from others languages. There are 4 main components in Thai text-to-speech synthesis system and several problems in each component. In text analysis, Thai is a language which has no punctuation marks to separate word boundaries and word ambiguity. In letter-to-sound, Thai has several sounds within one word called Homographs. In prosody generation, Thai has five tones which generate many different sounds. Finally, in speech synthesis, naturalness of speech has improved significantly. This paper presents the problems and development of recent researches in Thai text-to-speech system such as Characteristics of the Thai Tonal language, Corpus, Issues in Thai text-to-speech, Recent Thai text-tospeech applications, Future work and Conclusions. © 2008 IEEE.
Some of the metrics are blocked by your
consent settings
A new association rule-based text classifier algorithm
(2005-01-01)
Buddeewong, Supaporn
;
Kreesuradej, Worapoj
This paper proposes a new association rule-based text classifier algorithm to improve the prediction accuracy of Association Rule-based Classifier By Categories (ARC-BC) algorithm. Unlike the previous algorithms, the proposed association rule generation algorithm constructs two types of frequent itemsets. The first frequent itemsets, i.e. L<inf>k</inf>, contain all term that have no an overlap with other categories. The second frequent itemsets, i.e. OL <inf>k</inf>, contain all features that have an overlap with other categories. In addition, this paper also proposes a new join operation for the second frequent itemsets. The experimental results are shown a good performance of the proposed classifier © 2005 IEEE.
Some of the metrics are blocked by your
consent settings
Text processing simplified ARTMAP neural network
(2005-02-01)
Kreesuradej, Worapoj
;
Kunasit, Puangpaka
This paper proposes text processing simplified ARTMAP neural network. The algorithm works directly on textual information without transforming to numerical value. The input layer of the neural network can directly receive a qualitative value without mapping the qualitative value into numerical value. Then, based on simplified fuzzy ARTMAP neural network and the concept of similarity measure for symbolic objects, the proposed neural network can assigns class labels to the objects correctly.
Some of the metrics are blocked by your
consent settings
A new web search result clustering based on true common phrase label discovery
(2006-01-01)
Janruang, Jongkol
;
Kreesuradej, Worapoj
Web search results clustering are navigator for users to search results. Therefore the correct cluster label is important which has been index the set of web document. Suffix Tree Clustering (STC) is fast automatically clustering and labeling. However, STC is inadequate since they generate interrupted cluster label due to using n-gram technique. In this paper, we propose an approach for web search results clustering and labeling based on a new suffix tree data structure, a new base cluster combining algorithm with a new partial phase join operation. The algorithm for constructing the data structure is an incremental and a linear time algorithm. Thus, the proposed approach is suitable for on-the-fly the web search results clustering and labeling cluster. The proposed approach provides more readable and true common phrase of web document cluster than conventional web search result clustering. Experimental results also show that the proposed approach has better performance than that of conventional web search result clustering. © 2006 IEEE.
Some of the metrics are blocked by your
consent settings
Text Processing Adaptive Resonance Theory Neural Network
(2002-12-01)
Kreesuradej, Worapoj
;
Chantasut, Norraseth
This paper proposes a Text Processing Adaptive Resonance Theory Neural Network for document clustering. Unlike the conventional clustering algorithms, a Text Processing Adaptive Resonance Theory Neural Network works directly on textual information without transforming text data into a numerical value. The main contribution of this paper is to show how to adapt the concepts of ART1 clustering on a data set, which has a qualitative feature values. The Text Processing Adaptive Resonance Theory Neural Network utilizes of the concept of similarity measure for symbolic objects, which is different from the conventional similarity measure for objects whose feature values are numerical values. The proposed neural network assigns cluster labels to the objects.
Some of the metrics are blocked by your
consent settings
Mining dynamic databases using probability-based incremental association rule discovery algorithm
(2009-11-20)
Amornchewin, Ratchadaporn
;
Kreesuradej, Worapoj
In dynamic databases, new transactions are appended as time advances. This paper is concerned with applying an incremental association rule mining to extract interesting information from a dynamic database. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing customer preferences or new seasonal trends can be discovered in a dynamic environment. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has better performance than that of previous work. © J.UCS.
Some of the metrics are blocked by your
consent settings
Incremental association rule mining using promising frequent itemset algorithm
(2007-12-01)
Amornchewin, Ratchadaporn
;
Kreesuradej, Worapoj
Association rule discovery is an important area of data mining. In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic databases is an important problem. In this paper, promising frequent itemset algorithm, which is an incremental algorithm, is proposed to deal with this problem. The proposed algorithm uses maximum support count of 1-itemsets obtained from previous mining to estimate infrequent itemsets, called promising itemsets, of an original database that will capable of being frequent itemsets when new transactions are inserted into the original database. Thus, the algorithm can reduce a number of times to scan the original database. As a result, the algorithm has execution time faster than that of previous methods. This paper also conducts simulation experiments to show the performance of the proposed algorithm. The simulation results show that the proposed algorithm has a good performance. ©2007 IEEE.
Some of the metrics are blocked by your
consent settings
False positive item set algorithm for incremental association rule discovery
(2009-12-01)
Amornchewin, Ratchadaporn
;
Kreesuradej, Worapoj
In a dynamic database where the new transaction are inserted into the database, keeping patterns up-to-date and discovering new pattern are challenging problems of great practical importance. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic database is an important problem. In this paper, false positive itemset algorithm, which is an incremental algorithm, is proposed to deal with this problem. The proposed algorithm uses maximum support count of 1-itemsets obtained from previous mining to estimate infrequent itemsets, called false positive itemsets, of an original database. False positive itemsets will capable of being frequent itemsets when new transactions are inserted into an original database. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance.
Some of the metrics are blocked by your
consent settings
Probability-based incremental association rule discovery algorithm
(2008-11-28)
Amornchewin, Ratchadaporn
;
Kreesuradej, Worapoj
In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic databases is an important problem. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance. © 2008 IEEE.

Kreesuradej, Worapoj

Filters

Settings

Sort By

Results per page