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Author: search.filters.author.Sunhem, WisuwatSubject: search.filters.subject.machine learning

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    Interaction Behavior of Older Adults with Immersive Virtual Reality Application for Cognitive Training
    (2018-09-11)
    Intraraprasit, Monthon
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    Sunhem, Wisuwat
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    Jinjakam, Chompoonuch
    Nowadays, older adults face health problems. The main problem is decreasing our brain skills. Memory, visuospatial skill, and cognitive functions are considered to be essence of the brain skills. These capabilities can performance by applying cognitive training or brain training. Currently, virtual reality (VR) technology is applied to cognitive training application. The results of training can be analyzed after several weeks, but it is a lack of interaction behavior analysis of older adults with VR application. The behavior analysis helps to design efficient program training, in other words, we can utilize better VR application with older adults who do not familiar with current technology. Moreover, we can understand behavior of older adult via VR application for their brain ability. VR application can be designed and kept log files for interaction behavior analysis. The algorithm for preliminary analysis is machine learning. Machine learning can predict score that measures brain's ability from the dataset. We adopted 2 models in this research. The first model is to predict the scores of visual short-term memory, called VSTM-model. The second model is to predict the scores of visuospatial skill, called VS-model. We performed baseline regression and support vector regression algorithm for score prediction of behavior. Root mean squared error is selected to measure performance of the algorithm; root mean squared error of baseline regression equal to 0.3012 in VSTM-model and 1.2427 in VS-model, and root mean squared error of support vector regression equal to 0.2876 in VSTM-model and 1.0536 in VS-model.
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    A comparison between shallow and deep architecture classifiers on small dataset
    (2017-02-23)
    Pasupa, Kitsuchart
    ;
    Sunhem, Wisuwat
    Many machine learning algorithms have been introduced to solve different types of problem. Recently, many of these algorithms have been applied to deep architecture model and showed very impressive performance. In general, deep architecture model suffers from over-fitting problem when there is a small number of training data. In this paper, we attempted to remedy this problem in deep architecture with regularization techniques including overlap pooling, flipped-image augmentation and dropout, and we also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. However, a deep model augmented with a regularization technique-CNN with dropout technique-was competitive to the shallow models.
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    Hairstyle recommendation system for women
    (2016-07-22)
    Sunhem, Wisuwat
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    Pasupa, Kitsuchart
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    Jansiripitikul, Piyakorn
    A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.
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    An approach to face shape classification for hairstyle recommendation
    (2016-04-07)
    Sunhem, Wisuwat
    ;
    Pasupa, Kitsuchart
    It is important to choose a good hairstyle for women because it can enhance their beauty, personality, and confidence. One of the most important factors to consider for choosing the right hairstyle is the individuals face shape. An effective face shape classification can be used for constructing a hairstyle recommendation system. This paper presents a classification approach that divides face shapes into 5 different shapes: round, oval, oblong, square, and heart. This approach, which is based on an Active Appearance Model (AAM) and a face segmentation technique, produces a set of features that can be evaluated by several popular machine learning methods, namely, Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). Our results show that the Support Vector Machine with Radial Basis function kernel was the best algorithm that predicted accurately up to 72%.