Srinilta, Chutimet

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Srinilta, Chutimet
Alternative Name
Srinilta, C.
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chutimet.sr@kmitl.ac.th

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    Municipal solid waste segregation with CNN
    (2019-07-01)
    Srinilta, Chutimet
    ;
    Kanharattanachai, Sivakorn
    Pollution from municipal solid waste has been a problem in Thailand for a long time. People generate waste in every minute. Ineffective waste segregation does increase difficulties in solid waste management. The Pollution Control Department of Thailand provides segregation guideline for municipal solid waste. Household wastes should be separated into four types-general waste, compostable waste, recyclable waste and hazardous waste. This paper explored performance of CNN-based waste-type classifiers (VGG-16, ResNet-50, MobileNet V2 and DenseNet-121) in classifying waste types of 9,200 municipal solid waste images. Waste type can be identified directly from waste-type classifier or derived from waste-item class. Derived classifiers outperformed their corresponding direct classifiers in the experiment. The highest waste-type classification accuracy was 94.86% from the derived ResNet-50 classifier.
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    PP caching: Proxy caching mechanism for YouTube videos in campus network
    (2011-07-26)
    Suranuntakul, Varangpa
    ;
    Srinilta, Chutimet
    Nowadays, YouTube has become a very popular website offering video sharing service. YouTube accounts for a significant portion of Internet traffic in global and local networks. Many techniques have been implemented to deliver videos smoothly to every user. Streaming proxy servers are put into action to reduce user-perceived latency as well as network resource utilization. "What is viewed next" can be guessed from relationships of YouTube videos. This paper proposes PP caching mechanism that benefits from prefix caching and prefetching technique. Certain parts of selected videos are prefetched and kept at the proxy cache. The experimental results show that startup delay decreases and cache hit rate increases when the proposed mechanism is tested against actual YouTube traces from two campuses.
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    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.
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    Statistical downscaling for rainfall and temperature prediction in Thailand
    (2011-07-26)
    Aksornsingchai, Pawanrat
    ;
    Srinilta, Chutimet
    This paper studies three statistical downscaling methods to predict temperature and rainfall at 45 weather stations in Thailand. Methods under consideration are multiple linear regressions (MLR), support vector machine with polynomial kernel (SVM-POL), and support vector machine with Radial Basis Function kernel (SVM-RBF). Large-scale data are from Geophysical Fluid Dynamics Laboratory (GFDL). Five predictor variables are chosen: (1) temperature, (2) pressure, (3) precipitation, (4) evaporator, and (5) net short wave. Accuracy is assessed by 10-fold cross-validation in terms of root-mean-squared error (RMSE) and correlation coefficient (R). SVM-RBF is the most accurate model. Prediction accuracy of monthly average rainfall and temperature is satisfying in most part of the country. Lastly, downscaling models can project long term trends of monthly average rainfall and temperature.
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    Dynamic load balancing of short videos in heterogeneous storage environment
    (2010-12-01)
    Jaiphakdee, W.
    ;
    Srinilta, C.
    Performance of storage system has great impact on overall performance of I/O intensive systems. Data distribution across storage devices affects storage performance directly. Data migration can degrade system performance. This paper focuses on data placement issue in heterogeneous tiers of storage devices storing YouTube videos. In addition to storage capability and current workload, our data placement algorithm also takes into account future workload. Future workload is estimated from video's characteristics. Workload in all storage tiers is dynamically adjusted to achieve balance at all times. Four data placement algorithms were used to distribute 16,314 videos across 3-tier storage system in the experiment. The proposed algorithm resulted in a more balance workload distribution compared to round robin and random algorithms.
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    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%.