Jearanaitanakij, Kietikul

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kietikul.je@kmitl.ac.th

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Preface
(2020-01-01)
Nguyen, Ngoc Thanh
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Jearanaitanakij, Kietikul
;
Selamat, Ali
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Trawiński, Bogdan
;
Chittayasothorn, Suphamit
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Improving the running time of the nearest neighbor algorithm
(2013-12-01)
Chompupatipong, Nattakon
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Jearanaitanakij, Kietikul
Nearest neighbor algorithm is a well-known method, in pattern recognition, for classifying objects based on the nearest examples in the feature space. However, it's major drawback is the sequential search operation which calculates the distance between the probing object and the entire set of the training instances. In this paper, we propose a novel method to accelerate the searching operation in the nearest neighbor algorithm. Our method consists of two main steps; creating the reference table and searching the nearest neighbor. Reference table of the training instances is created once in the initial phase and referred periodically by the searching step. Surprisingly, this reference table can drastically reduce the searching time of the nearest neighbor algorithm on any feature space. The experimental results on five real-world datasets from the VCI repository show a remarkable improvement on the searching time while the accuracy is still preserved. © 2013 IEEE.
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Predicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns
(2019-10-01)
Jearanaitanakij, Kietikul
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Passaya, Bundit
Candlestick chart pattern is a technical tool that encapsulates the price of the asset for multiple time frames into a single price bar. The expertized trader can predict the price trend of the asset by looking at the pattern of some adjacent candlesticks. This paper proposes the architecture for predicting the short trend of the stocks by using the convolutional neural network and the candlestick patterns. The experiments are conducted with a set of candlestick pattern images collected from various stocks in the stock exchange of Thailand (SET). Each image captures six to twelve adjacent candlesticks. The experimental results indicate that the proposed method can correctly predict the short trend for most stocks with acceptable accuracy. In addition, the proposed architecture achieves better accuracy and training time than that of the well-known architecture, ResNet-18.
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Determining the orders of feature and hidden unit prunings of artificial neural networks
(2005-12-01)
Jearanaitanakij, Kietikul
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Pinngern, Ouen
There is a great deal of research undertaken for pruning away features and hidden units in order to reduce the size of Artificial Neural Networks (ANNs). However, none of these methods mentions about the relationship between the pruned unit and the number of epochs needed for retraining when the unit is pruned away from the network. In this paper, we present two heuristics for determining the pruning orders, which lead to the near smallest number of retraining epochs. The heuristics are based on the employment of the modified information gain calculated from all features in training data. Then, we test our proposed heuristics on an exclusive-or data set. The experimental results show the success of using information gain as a criterion for determining the pruning orders. © 2005 IEEE.
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An Ensemble Model of Dual Learning for Gambling and Pornographic Websites Classification
(2025-01-01)
Thianphan, Sirapat
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Jearanaitanakij, Kietikul
The rapid proliferation of pornographic and gambling websites poses significant challenges, as these platforms increasingly employ sophisticated techniques to evade detection. Traditional classification approaches that rely on a single feature often fail to achieve high detection rates due to the diverse strategies these websites use to bypass detection systems. To address this limitation, this study introduces an ensemble model for classifying pornographic and gambling websites by integrating two key features: URLs and textual content. A webscraping script was developed to extract textual data from HTML elements of 3,000 websites, evenly distributed among benign, pornographic, and gambling categories, specifically curated for Thai users. The URLs undergo preprocessing to capture their meaningful semantic properties, which reflect the characteristics of the corresponding websites. Separate classifiers were then trained on each feature before being integrated into an ensemble model for final prediction. This approach achieved an outstanding accuracy of 96.83%, significantly surpassing single-feature classifiers. Moreover, the findings demonstrate the proposed model's robustness against obfuscation techniques and anti-crawling mechanisms, underscoring its potential for effective automated detection.
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Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
(2020-10-21)
Sinhashthita, Wanarase
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Jearanaitanakij, Kietikul
Assigning proper weights to attributes in some datasets according to their importances can significantly improve the classification accuracy. Weighted attributes can support the classification methods effectively if their weights truly represent by their importances. In this research, we improve the K-Nearest Neighbors (KNN) algorithm by using Pearson correlation coefficient along with Particle Swarm Optimization (PSO) to find the optimal set of weights for attributes in the dataset. The experimental results show that the proposed method can significantly improve the classification accuracy when compared to the traditional KNN algorithm.
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Compact Thai Sign Language Translation by Deep Learning
(2024-01-01)
Jearanaitanakij, Kietikul
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Choojan, Piyathida
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Thongtem, Piyada
Sign language translation is a challenging problem in natural language processing. Its principle involves machine translation from sign language images to spoken language text. Designing a good translation is not a trivial task since there are a large number of both input image pixels and output classes. We propose the deep learning model to translate static gestures of Thai sign language (TSL) to the corresponding Thai spoken words. The main objective is to design a compact model that delivers high performance so that it can be implemented on mobile devices. Several mobile convolutional neural networks (CNN) are investigated to find the best backbone architecture. We also attach additional layers to the selected CNN architecture to fine-tune its performance. The experiments on the dataset collected from twenty-four volunteers indicate excellent results; in terms of precision, recall, and f1-score, of the proposed model. The comparisons with the state-of-the-art models and the feature visualizations from convolution layers endorse its effectiveness.
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DEEP LEARNING DEVELOPMENTAL MODEL FOR EMBRYO GRADING IN THAI POPULATION
(2024-01-01)
Wiriyasirivaj, Budsaba
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Limkiatsataporn, Sawit
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Pukinghin, Apisit
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Kuekulkomain, Phatrapron
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Promrungrueng, Pornprom
In light of the growing challenges associated with infertility, an increasing number of researchers are resorting to assisted reproductive technologies such as In Vitro Fertilization (IVF). Embryo grading is a crucial step in the IVF process that requires embryologists’ expertise. However, their limited availability has led to the exploration of technological alternatives. This study aims to use deep learning for human embryo grading, with models specifically designed for the dataset in Thailand at Vajira Hospital. The process of IVF at Vajira Hospital presents its own set of challenges since its embryo classification extends beyond the Istanbul consensus. Furthermore, classes that occur infrequently are removed and classes with similarities are merged. We apply transfer learning to pretrained deep learning models like VGG19, VGG16, ResNet152, Resnet101, Xception, InceptionV3, and EfficientNet, to create a system capable of accurately classifying embryo quality scores. Experimental results from the embryo dataset collected from Vajira Hospital in Thailand demonstrate the proposed classifier’s accuracy, precision, recall, f1-score, and AUC superiority. This research contributes to the field of IVF in Thailand by potentially reducing human errors and addressing the demand for skilled embryologists.
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Hidden unit reduction of artificial neural network on English capital letter recognition
(2006-12-01)
Jearanaitanakij, Kietikul
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Pinngern, Ouen
We present an analysis on the minimum number of hidden units that is required to recognize English capital letters of the artificial neural network. The letter font that we use as a case study is the System font. In order to have the minimum number of hidden units, the number of input features has to be minimized. Firstly, we apply our heuristic for pruning unnecessary features from the data set. The small number of the remaining features leads the artificial neural network to have the small number of input units as well. The reason is a particular feature has a one-to-one mapping relationship onto the input unit. Next, the hidden units are pruned away from the network by using the hidden unit pruning heuristic. Both pruning heuristic is based on the notion of the information gain. They can efficiently prune away the unnecessary features and hidden units from the network. The experimental results show the minimum number of hidden units required to train the artificial neural network to recognize English capital letters in System font In addition, the accuracy rate of the classification produced by the artificial neural network is practically high. As a result, the final artificial neural network that we produce is fantastically compact and reliable. © 2006 IEEE.
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Determine the irrelevance of hidden unit from the validation set
(2006-12-01)
Jearanaitanakij, Kietikul
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Pinngern, Ouen
This paper proposes a method to determine the irrelevance of the hidden unit in the artificial neural network. Unlike other approaches, we calculate the sensitivity of the hidden unit from the validation set, instead of the training set. The advantage of using the validation set to calculate the sensitivity is that we never overestimate the relevance of hidden unit. In other words, we always remove the unit that has the least effect on the validation set error. As a result, the pruned neural network has the highest generalization when compared with other choices of removals. Our sensitivity is based on the activation difference of the output unit. This activation difference is the gap between the activation of output units when a particular hidden unit is present and when it is removed. We have applied our technique to two standard benchmark problems. The experimental results show that the proposed technique can correctly determine the least irrelevant hidden unit. © 2006 IEEE.

Jearanaitanakij, Kietikul

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