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Author: search.filters.author.Hanskunatai, AnantapornSubject: search.filters.subject.Data mining

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    Item type:Publication,
    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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    Item type:Publication,
    A MULTI-LAYER HYBRID MACHINE LEARNING MODEL for AUTOMATIC SLEEP STAGE CLASSIFICATION
    (2018-12-01)
    Wongsirichot, Thakerng
    ;
    Hanskunatai, Anantaporn
    Sleep Stage Classification (SSC) is a standard process in the Polysomnography (PSG) for studying sleep patterns and events. The SSC provides sleep stage information of a patient throughout an entire sleep test. A physician uses results from SSCs to diagnose sleep disorder symptoms. However, the SSC data processing is time-consuming and requires trained sleep technicians to complete the task. Over the years, researchers attempted to find alternative methods, which are known as Automatic Sleep Stage Classification (ASSC), to perform the task faster and more efficiently. Proposed ASSC techniques usually derived from existing statistical methods and machine learning (ML) techniques. The objective of this study is to develop a new hybrid ASSC technique, Multi-Layer Hybrid Machine Learning Model (MLHM), for classifying sleep stages. The MLHM blends two baseline ML techniques, Decision Tree (DT) and Support Vector Machine (SVM). It operates on a newly developed multi-layer architecture. The multi-layer architecture consists of three layers for classifying W, R and N1, N2, N3 in different epoch lengths. Our experiment design compares MLHM and baseline ML techniques and other research works. The dataset used in this study was derived from the ISRUC-Sleep database comprising of 100 subjects. The classification performances were thoroughly reviewed using the hold-out and the 10-fold cross-validation method in both subject-specific and subject-independent classifications. The MLHM achieved a certain satisfactory classification results. It gained 0.694±0.22 of accuracy (AUC=0.822±0.31) in subject-specific classification and 0.942±0.02 of accuracy (AUC=0.920±0.17) in subject-independent classification. The pros and cons of the MLHM with the multi-layer architecture were thoroughly discussed. The effect of class imbalance was rationally discussed towards the classification results.