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A scenario-based analysis of front-facing camera eye tracker for UX-UI survey on mobile banking app
(2020-01-01)
Sunhem, Wisuwat
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Pasupa, Kitsuchart
Recently, User Experience and User Interface (UX-UI) have become important aspects in designing an effective mobile banking application. Traditionally, developers and designers have relied on explicit feedback derived from questionnaires to gain more insights into UX-UI. With the advancement of new technology, eye-tracking device has been introduced, and the approach has been used to provide a digital footprint indicating exact gazing positions of the users when using an application. So far, many studies have acknowledged the benefits of eye movement tracking and exploited such implicit feedback, alongside the result yielded from a survey. Successful uses of this eye-tracking device would further the development of mobile banking application. In this study, we aimed to build a device-free eye tracking software module that would work efficiently on mobile phones. To achieve this goal, we employed an existing Convolutional Neural Network model in our framework and evaluated the model when it was applied to the specific domain, i.e., UX-UI research design for mobile banking apps. We investigated a GazeCapture dataset, the first large-scale dataset for eye tracking, and conducted a data wrangling technique. The results show that fine-tuning the model with our wrangled data can improve the overall eye-tracking performance. Moreover, enabling user calibration can clearly enhance the predicting performance of the model.
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A hybrid approach to building face shape classifier for hairstyle recommender system
(2019-04-15)
Pasupa, Kitsuchart
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Sunhem, Wisuwat
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Loo, Chu Kiong
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This framework enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Support Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these individual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
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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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Machine learning approach in predicting post-transfusion packed cell volume in anemic dogs
(2018-08-13)
Srinilta, Chutimet
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Sunhem, Wisuwat
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Sangunwong, Pongsak
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Chanchartree, Satthathan
Blood transfusion is commonly used to treat anemia. Blood transfusion is vital to life in many cases. Blood donation is a voluntary activity. In Thailand, blood supply for small animals are very limited. Therefore, blood must be used with extra care to save as many lives as possible. Success of whole blood transfusion where all blood components are transfused is determined by the rise of Packed Cell Volume (PCV) after transfusion. Veterinarians rely on formula to estimate the transfusion volume that can raise patient's PCV to the target. This paper attempted to use machine learning models to predict post-transfusion PCV in anemic dogs. Linear regression, XGBoost and Support Vector Regression algorithms were used in machine learning prediction models. Transfusion records from Kasetsart University Veterinary Teaching Hospital at Hua Hin were employed to assess model performance. The formula commonly used by veterinarians was performance comparison baseline. Wilcoxon signed-rank test was used to assess significant differences of the result. It was statistically confirmed with confidence interval of 90% that Support Vector Regression performed better than the baseline method on conventional input set alone and when certain red blood cell attributes were added to the conventional input set.
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A comparison between shallow and deep architecture classifiers on small dataset
(2017-02-23)
Pasupa, Kitsuchart
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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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Can Eye Movement Improve Prediction Performance on Human Emotions Toward Images Classification?
(2017-01-01)
Pasupa, Kitsuchart
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Sunhem, Wisuwat
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Loo, Chu Kiong
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Kuroki, Yoshimitsu
Recently, image sentiment analysis has become more and more attractive to many researchers due to an increasing number of applications developed to understand images e.g. image retrieval systems and social networks. Many studies aim to improve the performance of the classifier by many approaches. This work aims to predict the emotional response of a person who is exposed to images. The prediction model makes use of eye movement data captured while users are looking at images to enhance the prediction performance. An image can stimulate different emotions in different users depending on where and how their eyes move on the image. Two image datasets were used, i.e. abstract images and images with context information, by using leave-one-user-out and leave-one-image-out cross-validation techniques. It was found that eye movement data is useful and able to improve the prediction performance only in leave-one-image-out cross-validation.
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Lyric-based Sentiment Polarity Classification of Thai Songs
(2017-01-01)
Srinilta, Chutimet
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Sunhem, Wisuwat
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Tungjitnob, Suchat
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Thasanthiah, Saruta
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Vatathanavaro, Supawit
Song sentiment polarity provides outlook of a song. It can be used in automatic music recommendation system. Sentiment polarity classification based solely on lyrics is challenging. It involves understanding linguistic knowledge, song characteristics and emotional interpretation of words. Since lyric is in a form of text. Techniques used in text mining, text sentiment analysis and music mood classification are studied and used together in our proposed model. Two types of classifier are proposed—lexicon-based classifier and machine learning-based classifier. N-gram model is used in feature set generation. Features are filtered by Information Gain. Feature weighting scheme is employed. We create a sentiment lexicon from Thai song corpus. Full lyric and certain parts of lyric are chosen for datasets. We evaluate our models under various environments. The best average accuracy achieved is 68%.
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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
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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%.

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