Thammano, Arit

Preferred name

Thammano, Arit

Alternative Name

Thammano, A.

Thummano, Arit

Main Affiliation

School of Information Technology

arit.th@kmitl.ac.th

Now showing 1 - 8 of 8

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Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
(2025-01-01)
Pravesjit, Sakkayaphop
;
Kantawong, Krittika
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Jitkongchuen, Duangjai
;
Thammano, Arit
;
Longpradit, Panchit
This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm Optimization (SCSO) and Invasive Weed Optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and Whale Optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.
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An Improvement of Whale Optimization Algorithm with Rao Algorithm for Optimization Problems
(2023-01-01)
Pravesjit, Sakkayaphop
;
Thammano, Arit
;
Kantawong, Krittika
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Jitkongchuen, Duangjai
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Longpradit, Panchit
This paper proposes an improvement of whale optimization algorithm for optimization problems. In this study, the Rao algorithm was improved by means of procedures of spiral updating position. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), intersection mutation differential evolution (IMDE) algorithm, and whale optimization algorithm (WOA). The computation results illustrated that the proposed algorithm can produce optimal solutions for three out of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function, and the discontinuous step function.
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Modification of Sand Cat Swarm Optimization for Classification Problems
(2024-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Hunta, Sathien
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Jitkongchuen, Duangjai
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Thammano, Arit
The proposed system represents an enhanced version in search of food of Sand Cat based on Levy distribution and Firework algorithm for the image classification of grape leaf diseases. In the preprocessing step, the proposed system utilizes convolution kernels to transform images into input data within the range of (0,1). Successively, the Levy distribution and Firework algorithm are incorporated into the SCSO model as an exploration search mechanism. The study employed a grape leaf dataset sourced from the Plant Village project (www.plantvillage.org), comprising 4062 labeled images measuring 256 by 256 pixels and categorized into 4 distinct classes: healthy, Black Rot, Black Measles, and Isariopsis leaf spot, which was utilized to evaluate the efficacy of the proposed system. The experimental findings demonstrate that the proposed system outperforms the analyses of VGG16, GLCM with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and SCSO.
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Segmentation of historical lanna handwritten manuscripts
(2012-11-28)
Pravesjit, Sakkayaphop
;
Thammano, Arit
Lanna script is an archaic script not commonly used in today's world. People trying to read these archaic Lanna manuscripts have to find some form of translation help to understand what they said. Unfortunately, few people nowadays know how to read or write this language. Therefore, character recognition system must be put to use in order to translate the Lanna script to the commonly used script. The poor condition of the manuscripts and the writing style of the script make this problem very difficult to solve. The most difficult cases of the writing style problem are the touching and overlapping characters. Therefore, the first two stages of the character recognition process, which are image preprocessing and segmentation, need to be closely watched over so that the recognition accuracy is high. In this paper, two new techniques are proposed. The first proposed technique emphasizes on converting a grayscale image to a binary image. In this proposed technique, the concepts of the multithresholding method and Otsu's method are combined together. The second proposed technique emphasizes on the process of touching character segmentation. In doing this, the bounding box analysis is initially employed to segment the document image into images of isolated characters and images of touching characters. The thinning algorithm is applied to extract the skeleton of the touching characters. Next, by using the junction points as the separation points, the skeleton of the touching characters is separated into several pieces. Finally, the separated pieces of the touching characters are put back to reconstruct two isolated characters. The proposed algorithm achieves an accuracy of 86.67%. © 2012 IEEE.
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Touching character segmentation method of Archaic Lanna script
(2012-12-01)
Pravesjit, Sakkayaphop
;
Thammano, Arit
In general, character recognition consists of four stages: image preprocessing, segmentation, feature extraction, and classification. Character segmentation is one of the most important and difficult tasks in character recognition. Incorrectly segmented characters are not likely to be correctly recognized. Touching characters, which always arises when handwritten characters are being segmented, makes the task even more difficult. Therefore, this paper emphasizes the interest to the segmentation of touching and overlapping characters. This paper proposes two new techniques which are shown to dramatically improve the segmentation accuracy. The first proposed technique emphasizes on converting a greyscale image to a binary image while the second proposed technique emphasizes on the process of character segmentation itself. In the proposed character segmentation process, the bounding box analysis is initially employed to segment the document image into images of isolated characters and images of touching characters. The thinning algorithm is applied to extract the skeleton of the touching characters. Next, the skeleton of the touching characters is separated into several pieces. Finally, the separated pieces of the touching characters are put back to reconstruct two isolated characters. The proposed algorithm achieves an accuracy of 89.26%. © Springer-Verlag Berlin Heidelberg 2012.
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Segmentation of touching lanna characters
(2011-09-12)
Pravesjit, Sakkayaphop
;
Thammano, Arit
Character segmentation is an important preprocessing step for character recognition. Incorrectly segmented characters are not likely to be correctly recognized. Touching characters is one of the most difficult segmentation cases which arise when handwritten characters are being segmented. Therefore, this paper emphasizes the interest to the segmentation of touching and overlapping characters. In the proposed character segmentation process, the bounding box analysis is initially employed to segment the document image into images of isolated characters and images of touching characters. The thinning algorithm is applied to extract the skeleton of the touching characters. Next, the skeleton of the touching characters is separated into several pieces. Finally, the separated pieces of the touching characters are put back to reconstruct two isolated characters. The proposed algorithm achieves an accuracy of 75.3%.
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An Improvement on Exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems
(2024-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Kamkhad, Natdanai
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Sabaiporn, Saksit
;
Monchanuan, Jantawan
The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (www.plantvillage.org), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).
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Recognition of archaic Lanna handwritten manuscripts using a hybrid bio-inspired algorithm
(2015-03-01)
Thammano, Arit
;
Pravesjit, Sakkayaphop
This paper proposes a new character recognition system for archaic Lanna handwritten characters. The proposed system consists of two main processes: the segmentation process and the recognition process. The segmentation process decomposes the touching or overlapping characters, commonly found in Lanna manuscripts, into isolated characters. In the recognition process, the proposed system uses the self-organizing map to divide the input images into several clusters. The clonal selection algorithm of the artificial immune system is then used to build a recognition model for each cluster created by the self-organizing map. Finally, the particle swarm optimization is employed as a local search mechanism. The proposed system was evaluated and compared to several state-of-the-art approaches. The experimental results demonstrate that the proposed system is very effective in recognizing not only Lanna characters but also the handwritten numerals of the five most popular scripts.

Thammano, Arit

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