Phankokkruad, Manop
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Phankokkruad, Manop
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manop.ph@kmitl.ac.th
39 results
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Item type:Publication, Mechanical, Thermal and Morphological Properties of Poly(Lactic Acid) and Poly(Butylene Adipate-co-Terephthalate) Blends with Organoclay(2022-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of file duplication by hierarchical clustering based on similarity relations(2018-06-21)This paper have proposed the classification of the duplicate file by measuring the similarity score between the couple of files. This work examined the distance between the pairwise of files by the Smith-Waterman algorithm. In addition, the make use of the Euclidean distance matrix could identify the relativity between the persons who often copies the files each other. Since the regularity of the duplication happens, this work could classify the proximity to the persons, and a group of person who positioned closely together by applying the hierarchical clustering. The result revealed that the Smith-Waterman algorithms could measure the similarity between files effectively. Also, this work could analyze the relativity of the persons, classifies the person who positioned closely together, and the person between nearest related members of the group. Finally, this work represented the amount of time that person duplicated the files. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative study of text-to-speech synthesis techniques for mobile linguistic translation process(2014-03-30) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, COVID-19 Pneumonia detection in chest X-ray images using transfer learning of convolutional neural networks(2020-07-24)The COVID-19 pandemic is the defining global health crisis and the greatest challenge. Since its emergence and has spread rising daily worldwide. The early diagnosis of COVID-19 can improve a chance of survival and can support clinical treatment. An automatically COVID-19 pneumonia detection will support the medical diagnosis to examine the chest X-ray image. For this reason, this work intent to develop the CNN model by the process of transfer learning. The models will be created from the three pre-trained models include Xception, VGG16, and Inception-Resnet-V2 model. Then, these newly proposed models will be applied to detect COVID-19 pneumonia from the X-ray image dataset. The proposed models enhance to diagnose the chest X-ray images of patients who have pneumonia by COVID-19. Since the existing COVID-19 X-ray dataset has a small number of images containing 323 images, this work uses the data augmentation techniques to increase the virtual number of images. The results reveal that the proposed models have performed the classification task for detecting pneumonia. The proposed model achieved an accuracy level of Xception, VGG16, and Inception-Resnet-V2 is 97.19%, 95.42% and 93.87%, respectively. It reveals that the CNN model with the Xception has higher accuracy than VGG16 and Inception-ResNet-V2 model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identification, counting, and sizing of dispersed phase droplet of scanning electron microscopy micrograph using digital image processing(2012-12-01); Wacharawichanant, SiriratThe identification of dispersed phase droplet in scanning electron microscopy (SEM) image is the heart of the process polymer blends, especially for the development of the polymeric materials and improvement of the polymer properties. Manual identification is the hard work and inaccurate method. To solve this problem, a digital image processing (DIP) method based on Hough transform is proposed for automatically identifies the dispersed phase droplet in SEM images. By combining the characteristics of SEM images and the DIP method, this method performed hierarchical Hough transform on the circular droplet to detect the object boundary in the SEM images. The DIP method has been experimented on variety of SEM images and very promising results have been achieved given more accuracy. Experimental results show that the proposed method with high adaptability is more accurate and rapidly than the traditional method. © 2012 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cost-Sensitive Extreme Gradient Boosting for Imbalanced Classification of Breast Cancer Diagnosis(2020-08-01)The clinical information can enhance the doctors for predicting and diagnosing the diseases also making the right decisions. Breast cancer is the most dangerous disease, early diagnosis can improve a chance of survival and can support clinical treatment. Detecting breast cancer takes a lot of time and it is hard to classification. However, the problem of the classification occurs when there is an unequal distribution of classes the dataset. This is caused by the low performance in the traditional machine learning models. For this reason, this work proposed the cost-sensitive XGBoost model, which is an improved version of the XGBoost model in conjunction with cost-sensitive learning. The models were applied to classify the four breast cancer datasets that contained the imbalanced data. In the experiment, this work determined the best parameters on each dataset by the hyperparameters optimization technique before configuring the models. The results indicated that the cost-sensitive XGBoost model had been skillful, and could improve classification accuracy in four datasets. In addition, this work evaluated the model performance by accuracy, ROC AUC, and k-Fold cross-validation to ensure that the new models is accurate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving the Sound Classification Accuracy Using CNN-LSTM and MFCC with Audio Augmentation for Diagnosing Respiratory Disease(2025-01-01); Wacharawichanant, SiriratAudio is vital information data for understanding various situations. A multitude of sound features can be explained by analysis through the audio signals. Numerous classification methods have been developed to study audio classification. This work studies the improvement of audio classification for the diagnosis of respiratory disease through the integration of audio data augmentation and CNN in conjunction with LSTM (CNN-LSTM). Furthermore, this paper focuses on audio data augmentation and feature extraction in the deep learning approach. This study proposed the CNN-LSTM model to diagnose respiratory disease by learning from the different audio datasets. The results reveal that the CNN-LSTM model attained an accuracy of 81.48%, precision of 0.8340, sensitivity of 0.6948, and F1-score of 0.7225. Considering the achieved F1-score, the CNN-LSTM model demonstrates a high level of diagnotic accuracy. Therefore, all evaluation evaluation parameters collectively indicate the robust performance of the proposed disease classification model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improvement of poly(Lactic acid) properties by ethylene-octene copolymer and organoclay(2020-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Morphology and properties of poly(lactic acid)/ethylene-octene copolymer blends with different organoclay types(2020-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An application of convolutional neural network-long short-term memory model for service demand forecasting(2019-07-01); Wacharawichanant, SiriratThe medical services are very important requirement for being healthy human. In order to ensure the availability of resources for the medicine needed, the most hospital makes an service demand estimation by forecasting a number of patients to provide the sufficient medical services. Therefore, the accurately forecast a number of patients would be valuable knowledge for managing. This work proposed the CNN-LSTM model, which was a combination of CNN and LSTM, to forecast the number of patients who used hospital services. The CNN model was used to interpret, and extract the features from the input data. Then, it was provided this information to the LSTM model for interpreting and making a forecast. The CNN-LSTM models were applied to forecast on the two datasets. The results indicated that CNN-LSTM model made reliable forecasting. This work measured the model performnace by calculating RMSE and MAE value. The result showed RMSE and MAE of the models were very low in all experiments. Forecasting the number of patients can help the hospital to estimate the service demand, make a better policy for managing the medical resources on demand, and improve the efficiency of medical services for the future.