Srinilta, Chutimet

Loading...
Thumbnail Image
Preferred name
Srinilta, Chutimet
Alternative Name
Srinilta, C.
Main Affiliation
Email
chutimet.sr@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Machine learning approach in predicting post-transfusion packed cell volume in anemic dogs
    (2018-08-13)
    Srinilta, Chutimet
    ;
    Sunhem, Wisuwat
    ;
    Sangunwong, Pongsak
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Lyric-based Sentiment Polarity Classification of Thai Songs
    (2017-01-01)
    Srinilta, Chutimet
    ;
    Sunhem, Wisuwat
    ;
    Tungjitnob, Suchat
    ;
    Thasanthiah, Saruta
    ;
    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%.