Jearanaitanakij, Kietikul

Preferred name

Main Affiliation

School of Engineering

kietikul.je@kmitl.ac.th

Now showing 1 - 10 of 20

Some of the metrics are blocked by your
consent settings
Improving the running time of the nearest neighbor algorithm
(2013-12-01)
Chompupatipong, Nattakon
;
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.
Some of the metrics are blocked by your
consent settings
Predicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns
(2019-10-01)
Jearanaitanakij, Kietikul
;
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.
Some of the metrics are blocked by your
consent settings
Determining the orders of feature and hidden unit prunings of artificial neural networks
(2005-12-01)
Jearanaitanakij, Kietikul
;
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.
Some of the metrics are blocked by your
consent settings
An Ensemble Model of Dual Learning for Gambling and Pornographic Websites Classification
(2025-01-01)
Thianphan, Sirapat
;
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.
Some of the metrics are blocked by your
consent settings
Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
(2020-10-21)
Sinhashthita, Wanarase
;
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.
Some of the metrics are blocked by your
consent settings
Hidden unit reduction of artificial neural network on English capital letter recognition
(2006-12-01)
Jearanaitanakij, Kietikul
;
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.
Some of the metrics are blocked by your
consent settings
Determine the irrelevance of hidden unit from the validation set
(2006-12-01)
Jearanaitanakij, Kietikul
;
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.
Some of the metrics are blocked by your
consent settings
Improving Particle Swarm Optimization by using incremental attribute learning and centroid of particle's best positions
(2017-11-03)
Srimakham, Sornnarong
;
Jearanaitanakij, Kietikul
Particle Swarm Optimization (PSO) is a powerful algorithm that can search a solution for a function which contains a large number of peaks and valleys. However, PSO might encounter a difficulty when the function gets more complex or the number of attributes (dimensions) grows larger. This paper proposes a modification of PSO by using the incremental attribute strategy along with the centroid of particle's best positions to avoid the local minima which can easily occur in a multimodal problem. The experimental results from four standard benchmarks show that the proposed method can improve PSO in terms of optimality and stability when compared with the conventional PSO and another incremental attribute-based PSO.
Some of the metrics are blocked by your
consent settings
Improving ID3 algorithm by ignoring minor instances
(2018-07-02)
Kaewrod, Nicha
;
Jearanaitanakij, Kietikul
Among various classification algorithms, ID3 is one of the most widely used and well-known tools that generates an efficient decision tree. Nevertheless, ID3 is too rigorous in generating the decision rules. As a result, the final decision tree may carry too many decision rules. Some of these decision rules may have very low number of instances which do not make significant change to the classification accuracy. The aim of this paper is to propose an approach to relax the rigorousness of the conventional ID3 algorithm by ignoring minor instances so that the resulting decision tree will have the lower number of depths yet produce promising accuracy. The proposed algorithm is examined on six datasets from UCI repository and Weka. The experimental results indicate that the proposed algorithm not only significantly reduces the maximum number of depths of the decision tree, but also retains the classification accuracy in the satisfying level. Moreover, the training time, the classification time, and the number of decision rules of the proposed algorithm are lower than those of the conventional ID3.
Some of the metrics are blocked by your
consent settings
Improving the ID3 Algorithm By Filtering Out Attributes With Values Of 0 or 1
(2022-01-01)
Chuenprasertsuk, Pinyarat
;
Jearanaitanakij, Kietikul
The iterative Dichotomiser 3 (ID3) algorithm is a classification algorithm that generates a decision tree, and it is one of the most simple and well-known tools. However, there is still room for improvement. This paper aims to reduce the running time of generating a decision tree by ignoring attributes that have an information gain value of 0 or 1 and the experiment result shows that the improved ID3 algorithm can significantly reduce the running time by more than 10 percent when compared with the traditional ID3 algorithm.

Jearanaitanakij, Kietikul

Filters

Settings

Sort By

Results per page