Hanskunatai, Anantaporn

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Hanskunatai, Anantaporn
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Hanskunatai, Anantapom
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anantaporn.ha@kmitl.ac.th

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    A new hybrid intelligent system for fast neural network training
    (2013-01-01)
    Hanskunatai, Anantaporn
    A major drawback of artificial neural network is long training time depending on a number of training data. Thus, the contribution of this work is to present the intelligent hybrid system for faster training on neural network. The concept of the proposed method is applying DBSCAN for removing noise and outliers then selecting the represented instances to form a smaller training set for further model training. The experimental results indicate that the proposed method can dramatically reduce a size of training set while the predictive performance of the classifiers are better or almost the same as models trained with original training sets. © 2013 Springer-Verlag Berlin Heidelberg.
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    A new streaming learning for stream chunk data classification based on incremental learning and adaptive boosting algorithm
    (2018-01-01)
    Claypo, Niphat
    ;
    Hanskunatai, Anantaporn
    ;
    Jaiyen, Saichon
    Currently, stream data classification is a challenge task to discover new useful knowledge from massive and dynamic data in big data era. This paper proposes a streaming learning method based on the incremental learning using a new adaptive boosting algorithm for stream data. The proposed adaptive boosting consists of a new method for updating distribution weight and the new weight voting. This learning method concentrates on learning from sequential chunks of data stream. The distribution weight updating method uses error of previous hypothesis to update the weight. The learning method uses only one data chunk to create a new hypothesis at a time and after learning, the learned data chunk can be thrown away and can learn the new data chunk without using the previous learned data. The experimental results show that the accuracy of the proposed method is higher than other methods in all datasets.
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    A comparative investigation of PSG signal patterns to classify sleep disorders using machine learning techniques
    (2015-01-01)
    Wongsirichot, Thakerng
    ;
    Hanskunatai, Anantaporn
    Patients with Non-Communicable Diseases (NCDs) are increasing around the globe. Possible causes of the NCDs are continuously being investigated. One of them is a sleep disorder. In order to detect specific sleep disorders, the Polysomnography (PSG), is necessary. However, due to the lack of the PSG in many hospitals, researchers attempt to discover alternative approaches. This article demonstrates comparisons of sleep disorder classifications using machine learning techniques. Three main machine learning techniques have been compared including Classification And Regression Tree (CART), k-Mean Clustering (KMC) and Support Vector Machine (SVM). The SVM achieves the best classification results in NREM-1 and NREM-2. The CART performs superior in NREM-3 and REM. Implications in terms of medical diagnosis, there are two main selected features, SaO2 and Pulse, based on the CART in all of the sleep stages. The features may be pieces of evidences to predict various types of sleep disorders.
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    The ensemble of Naïve Bayes classifiers for hotel searching
    (2014-01-01)
    Srisuan, Janasthar
    ;
    Hanskunatai, Anantaporn
    The objective of the paper is to present a new ensemble of Naïve Bayes classifiers model for an application of hotel searching. The dataset were collected from 293 reviews of 15 hotels in Phuket. The main idea of the proposed model is to combine two models of Naïve Bayes classifiers with different feature selection techniques. The output of the searching model is a list of hotel names ranking by hotel probability related to user keywords. The searching performance of the ensemble model was compared with two classical searching methods: Boolean searching and Boyer-Moore searching. The results show that the ensemble of Naïve Bayes classifiers model provides the highest average rank-accuracy. In addition, the proposed model also takes the fastest time in searching when compared with the other techniques.
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    Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models
    (2023-01-01)
    Thananukhun, Kit
    ;
    Jaiyen, Saichon
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Hanskunatai, Anantaporn
    Question-Answering (QA) models are part of Natural Language Processing (NLP) field used for ensuring questions match the answers appropriately. QA consists of several steps, one of which is called Question Classification, which is to classify the context of communication. In this step, it categorizes group of questions based on what users need to know in order to combine answers within the same category and respond accurately. It helps saving us time to search for answers as well. In this paper, we present a question classification model for Thai Conversational Chatbot using Artificial Neural Network and Multilingual Bidirectional Encoder Representations from Transformer (BERT) models using BERT-base multilingual cased combined with Multilayer Perceptron (MLP). The method yields the highest accuracy of 92.57%, compared to the BERT-base multilingual cased combined with other classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Decision Trees (DTs) with the accuracy scores of 88.57%, 80.00%, 78.57% and 60.29%, respectively. In addition, we also compare the performance of our proposed BERT model with another well-known Thai word embedding model, called Thai2Vec, which also combines with other classification models including MLP, SVM, NB, KNN and DTs, and their results of accuracies are: 85.71%, 85.71%, 75.71%, 75.71% and 58.86%, respectively. From the experiments, the BERT model combined with MLP can achieve the highest performance in term of accuracy among other methods.
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    Auto-Tuning of parameters in hybrid sampling method for class imbalance problem
    (2017-02-21)
    Sanguanmak, Yotsathon
    ;
    Hanskunatai, Anantaporn
    The class imbalance is a major problem in machine learning. This problem affects the performance of a model prediction. The DBSM algorithm, a hybrid-sampling technique, was developed to deal with the class imbalance for two-class classification problem. Although the DBSM algorithm is the effective solution, there are too many parameters for tuning in the algorithm. Thus, this paper proposes an automatic parameter tuning for the DBSM algorithm by using a genetic algorithm (GA), called GADBSM. The experimental results of GADBSM are compared with the DBSM algorithm. The results show that the GADBSM can enhance the classification performance of the DBSM algorithm. Moreover, the GADBSM provides the best in F-measure and AUC in all datasets.
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    Multi-Label Classification of Foreign Tourists' Opinions on Thailand Tourism Development
    (2024-10-03)
    Suramanka, Lalita
    ;
    Hanskunatai, Anantaporn
    The enhancement of tourism quality in Thailand through the understanding and utilization of foreign tourists' opinions presents challenges due to the extensive volume of data involved. This research proposes a two-fold approach to address this issue: (1) the development of an opinion classification model, and (2) the analysis of tourists' opinions through a dashboard. A dataset, compiled by the Tourism Authority of Thailand (TAT) and consisting of opinions from foreign tourists regarding areas for improvement in Thai tourism, was utilized. A total of 2,249 comments were collected. Experimental results demonstrate that the use of data augmentation, feature selection, Multi-label transformation using Classifier Chains, and the Random Forest classification model on the training dataset yields promising results with an accuracy rate of 80%, precision of 90%, recall of 81%, F1-score of 85%, and Hamming loss of 0.04. Analysis from the dashboard revealed the top three key areas for improvement: communication/language, traffic/public transportation, and cleanliness/hygiene.
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    A New Hybrid Sampling Approach for Classification of Imbalanced Datasets
    (2018-09-11)
    Hanskunatai, Anantaporn
    Nowadays it is an era of data driven. Many organizations around the world including bank, industry, commercial, and medical intend to extract knowledge from a huge of data. But in the real-word datasets, most of them occur class imbalance problems. This paper presents a new algorithm to handle an imbalanced classification. The proposed technique is a hybrid sampling approach which is the combination of a well know oversampling algorithm called SMOTE and the undersampling technique by removing the ambiguous instances from the majority class instances. The experimental results show that the new hybrid sampling method yields the better predictive performance in term of F-measure when compare with other sampling techniques. In addition, it can improve f-measure up to 59.73% and 412.26% when compare with the original dataset based on decision tree learning and naïve bayes classifiers respectively.
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    Tourist Destination Recommendation System based on Machine Learning
    (2024-10-03)
    Kongpeng, Sumitra
    ;
    Hanskunatai, Anantaporn
    Thailand has a wide variety of tourist attractions, making it difficult for tourist to choose where to go on vacation. The tourist destination recommendation system is a challenge for creating a system to help recommend tourist destinations that are appropriate for personal. Therefore, the principal aims of this research encompass two distinct objectives: firstly, to create a recommendation system for tourist destinations in Thailand by applying machine learning algorithms; and secondly, to analyze factors influencing tourists' choices of destinations. The dataset was gathered from an online survey conducted via Google Forms, comprising responses from 429 tourists in Thailand. In the experiments, three different types of feature selection methods were applied in a data preprocessing step. In the modeling process, four machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbors (k-NN), and Multi-Layer Perceptron (MLP), were used to construct the model and compare the predictive performance of the recommendation system based on hit rate and NDCG. The experimental results showed that suggesting tourist destinations in the Central region was the most effective, with the highest hit rate and NDCG compared to other regions. The average hit rate and NDCG for the five regions were 0.8 and 0.59, respectively. In addition, there has been an analysis of key factors influencing destination selection, such as activity, travel month, travel budget, and the age of tourists, to understand their impact on travel choices in each region of Thailand.
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    Thai Voice Recognition for Controlling Electrical appliances Using Long Short-Term Memory
    (2020-04-01)
    Saheaw, Wuttichai
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    Human speech possesses characteristics in each of the word that can be recognized and learned by computers. In this research, It is being proposed the use of the Deep Learning Model to predict speech turn-on and turn-off various electrical appliances, by using the sound conversion method that has been through the process to get the value of sound waves and applied toward training process in different ways. As the sound has more than 1 syllable and having characteristics of similar words that might difficult to predict. This research is based on Convolutional Neural Network (CNN) for comparison with the use of Long Short-Term Memory (LSTM), which is part of the Recurrent Neural Network (RNN) and Thai language Speech Dataset turn-on and turn-off by the 7 types of electrical appliances, the process of reducing noise and silence of the front and back of the audio files by 14 classes in total. The experimental results signify that the proposed Long Short-Term Memory can achieve the best accuracy.