Kreesuradej, Worapoj

Preferred name

Alternative Name

Kreesuradej, W.

Main Affiliation

School of Information Technology

worapoj.kr@kmitl.ac.th

Now showing 1 - 10 of 28

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Prosody analysis of Thai emotion utterances
(2011-07-01)
Yimngam, Sukanya
;
Premchaisawadi, Wichian
;
Kreesuradej, Worapoj
Emotion speech synthesis is the most important process to generate the naturalness of utterances in text-to-speech system. The interjection utterances in Thai language are employed in express a number of emotions. This paper presents a study of the prosody parameters of the interjection utterances clipped from Thai utterances in the movies. The Thai emotional utterances from various movies have been analyzed and classified into 8 emotional types consisting of neutral, anger, happiness, sadness, fear, pleasant, unpleasant and surprise. The classification of prosodic features is based on fundamental frequency (F0), intensity and duration. This paper compares the prosodic features in the Thai language and other languages including English, Italian, French, Spanish and Arabic. The comparison results show that there are significant differences of prosodic features for each emotion in each language. Therefore, the quality of a text-to-speech system is based on the prosodic analysis of each language. © 2011 Springer-Verlag.
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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.
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Incremental Association Rule Mining with a Fast Incremental Updating Frequent Pattern Growth Algorithm
(2021-01-01)
Thurachon, Wannasiri
;
Kreesuradej, Worapoj
One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental associationrule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incrementalmining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.
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An enhanced incremental association rule discovery with a lower minimum support
(2016-12-01)
Ariya, Araya
;
Kreesuradej, Worapoj
In the real world of data, a new set of data has been being inserted into the existing database. Thus, the rule maintenance of association rule discovery in large databases is an important problem. Every time the new data set is appended to an original database, the old rule may probably be valid or invalid. This paper proposed the approach to calculate the lower minimum support for collecting the expected frequent itemsets. The concept idea is applying the normal approximation to the binomial theory. This proposed idea can reduce a process of calculating probability value for all itemsets that are unnecessary. In addition, the confidence interval is also applied to ensure that the collection of expected frequent itemsets is properly kept.
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Frequent itemsets mining using random walks for record insertion and deletion
(2017-02-23)
Thusaranon, Panita
;
Kreesuradej, Worapoj
In Association rules mining, the task of finding frequent itemsets in dynamic database is very important because the updates may not only invalidate some existing rules but also make other rules relevant. In this paper, we propose a new algorithm to maintain frequent itemsets of a dynamic database in the case of record insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains not only the support counts of frequent itemsets but also the support counts of prospective frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Prospective frequent itemsets, which are obtained by using the principle of Random Walks, can help to reduce a number of times to rescan the original database.
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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.
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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.
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Probability-based incremental association rule discovery using the normal approximation
(2013-01-01)
Ariya, Araya
;
Kreesuradej, Worapoj
An incremental association rules mining is one of an association rule mining research topics which finds the relation between set of item in dynamic databases. As data grows up rapidly, the co-occurrence itemset which discovered in the previous mining may be changed and the association rule will be change consequently. Incremental association rule mining research attempts to maintain that rules. Probability-based algorithm, one of an incremental algorithm, applied the principle of Bernoulli trial to predict expected frequent itemsets for reducing collected border itemsets and a number of times to rescan the original database. However, the numerical problem will occur when the algorithm deals with a large database. To manipulate with this problem, the improved probability-based incremental association rule discovery using normal approximation to estimate the probability of occurrence of expected frequent itemset is introduced in this paper. In addition, the confidence interval is applied to ensure that the collecting of expected frequent itemsets is properly kept. © 2013 IEEE.
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The framework of government cloud computing adoption with TAM in Thailand
(2019-08-23)
Buavirat, Warune
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Kreesuradej, Worapoj
;
Chaveesuk, Singha
Cloud technology/ Cloud Computing had gained significance and popularity in the recent years with the increase in the internet reliability, even increasing access speed and the need for large storage requirements by various users. The cloud technology has been widely used in the various business industries for many years such as data storage, analytics, data management, connecting network, distributed working etc. Cloud computing offer its series of benefits to many business industries, organizations including at government level. The implication of Cloud technology in the government will help to get information in real-time, improve work process with higher efficiency and effectiveness. E-government also known as G-Cloud is the kind of cloud system, which provides user interface to the Government systems for its citizens. The influx of innovation had also brought up many disruptions to many businesses in the industry. Organizations have been involved in the research to overcome such challenges and aims to extend adoption and technology fit with better clarity while overcoming the disruptions that have been faced by its citizens from the government departments and why it’s necessary for adopting this technology innovation by their particular organization. This study based on technology acceptance model 3 and task-technology fit model.
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Incremental multilevel association rule mining of a dynamic database under a change of a minimum support threshold
(2016-01-01)
Pumjun, Nophadon
;
Kreesuradej, Worapoj
The proposed algorithm can efficiently deal with multilevel association rules mining of a dynamic database and a current support threshold can be different from the previous task. The experimental results show that the proposed algorithm has better performance than ML-T2 algorithm.

Kreesuradej, Worapoj

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