Intakosum, Sarun
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Item type:Publication, Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API(2022-07-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An efficient parallel construction of optimal independent spanning trees on hypercubes(2012-12-01); Boonjing, VeeraReliable 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 <sup>k</sup>) 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 <sup>Qk</sup>, an efficient communication network. One sequential approach utilized the recursive feature of <sup>Qk</sup> 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 <sup>Qk</sup>, based on HDLS (Hamming Distance Latin Square), single pointer jumping, which is applied for a source (r)=0 in O( <sup>k2</sup>) 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 <sup>Qk</sup>, a generalized approach, for an arbitrary root (r=0,1,2,..., or 2 <sup>k</sup>-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( <sup>k2</sup>) of the HDLS approach. We also prove that our generalized parallel k-IST construction (arbitrary r) with optimal height on <sup>Qk</sup> 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate(2022-04-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improvement Classification Approach in Tomato Leaf Disease using Modified Visual Geometry Group (VGG)-InceptionV3(2022-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Borderline over-sampling in feature space for learning algorithms in imbalanced data environments(2016-01-01); Thawonmas, RuckIn 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Qos-security metrics based on ITIL and COBIT standard for measurement web services(2012-06-27) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Plant Species Classification Using Leaf Edge Feature Combination with Morphological Transformations and SIFT Key Point(2023-03-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, High candidates generation: A new efficient method for mining share-frequent patterns(2017-11-01); Boonjing, VeeraThe 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%.