Kreesuradej, Worapoj

Preferred name

Alternative Name

Kreesuradej, W.

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School of Information Technology

worapoj.kr@kmitl.ac.th

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A Technique for Estimating Updated Frequent Itemsets in ESC-Growth Algorithm
(2019-07-01)
Kreesuradej, Worapoj
;
Thurachon, Wannasiri
In discovering association rules from a dynamic database, iteration through the frequent itemsets requires significant resources and computational time for construction of sub-trees, sub-tree traversal and generation of conditional pattern bases, and it is quite possible that no updated frequent itemsets may have been found at all, resulting in a waste of resources and computational time. We describe a technique for estimating the support count for the itemsets for the next iteration of discovery of the frequent itemsets by our ESC-Growth Algorithm. This technique reduces the need to construct a new sub-tree and next discovery step. If no frequent itemsets in the updated database have been found in the next iteration, ESC-Growth will not construct a new sub-tree and will stop discovering new frequent itemsets in that iteration, reducing the waste of resources and computational time. We measured execution time and sub-tree counts for FP-Growth, FUFP-tree, FPISC-Growth and ESCGrowth on the same synthetic dataset; we found that, at 5% minimum support threshold, ESC-Growth used only 40.3, 96.5, and 99.6% of the execution time required by FP-Growth, FUFPtree and FPISC-Growth, respectively.
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Webcam based eye gaze prediction system with automatic calibration for web browser
(2019-07-01)
Karngumpol, Niphat
;
Kreesuradej, Worapoj
Now a day, researches about Eye Gaze Prediction Systems with General Webcam was interested by many researchers and has been continuously developed because it is cheap and can be applied in many ways. Currently, a Browser based open source library for eye gaze prediction has been developed, which is easy to use and can be developed in a variety fields. However, this method is still limited because of low precision and inconveniences because users must perform calibration every time before use it. Therefore, we propose ways to develop and solve the above problems. This research has two objectives. The first is to improve the accuracy of an existing Eye Gaze Prediction System. The solution is using the Simple Moving Average to reduce the volatile of results and increase accuracy. From a results, this method gives a test scores higher than the existing method with statistically significant at 0.01, calculated as 14.96 percent increase from average score of the old method. The second objective is to propose a solution for making the system can recalibrate itself to improve accuracy in a long time without having to perform calibration by the users. We record the gaze data from system in the first 1 minute and then take the recorded data to calculate the boundary of the screen. And then, compare the calculated boundary with the real screen boundary to find error factors. This calculated error factor has been applied back to the next result to increase accuracy. From the test results, we found that over time the average test scores gradually increase. And at the last minute, the average score from this method is higher than the average score from traditional method up to 13.66 percent.
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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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The acceptance model of M-payment using smartphone devices in Thailand: A conceptual framework
(2018-01-01)
Nakwari, Parisgawin
;
Kreesuradej, Worapoj
;
Chaveesuk, Singha
In this paper, we have proposed the M-payment method using the smartphone applications, from which all electronic transactions such as purchasing, sales and the provision of other financial services can be applied. This is the investigation of the UTAUT2 extended theory that can be more appropriate for the consumers. The UTAUT2 extended theory is integrated with the service quality, which can be used to examine the acceptance potential of the M-Payment user by smartphone devices. The outcomes of this study will benefit the financial industry in their effort to initiate an extensive online-payment process in all users, especially, in Thailand.
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Discovery of incremental association rules based on a new FP-growth algorithm
(2019-02-01)
Kreesuradej, Worapoj
;
Thurachon, Wannasiri
In this paper, we propose a new FP-Growth algorithm for incremental association rule discovery. We also design a new FPISC-tree based on the FUFP-tree structure. The new FPISC-tree is more suitable for the task of incremental association rule discovery than FUFP-tree structure. The basic ideas of the proposed algorithm are to retrieve the frequent itemsets from the original database and to use their support count in the update of the new support count of the incremental database so that the original paths do not need to be reprocessed as well as to strategically use them to discover frequent itemsets from the FPISC-tree. Experimental results show that the proposed algorithm was able to reduce the number of constructed subtrees and the execution time was significantly less than those of the FP-Growth and FUFP-tree.
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A probability‑based incremental association rule discovery algorithm for record insertion and deletion
(2015-07-01)
Thusaranon, Panita
;
Kreesuradej, Worapoj
The maintenance of association rules for dynamic database is an important problem because the updates may not only invalidate some existing rules but also make other rules relevant. This paper is the extension work of probability-based incremental association rule discovery algorithm which can only handle new data insert into a dynamic database. Unlike the previous work, the proposed algorithm can efficiently handle in case of insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains the support counts of frequent itemsets and promising frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Promising frequent itemsets, which are obtained by using the principle of Bernoulli trials, can help to reduce a number of times to rescan the original database. The support counts of new candidate itemsets are approximated by using the principle of maximum possible value. The experimental results show that the execution time of the proposed algorithm is faster than that of Apriori, FUP2, EDUA, and pre-large algorithm.
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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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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
;
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.

Kreesuradej, Worapoj

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