Phankokkruad, Manop
Loading...
Preferred name
Phankokkruad, Manop
Main Affiliation
Email
manop.ph@kmitl.ac.th
22 results
Now showing 1 - 10 of 22
- Some of the metrics are blocked by yourconsent settings
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, Subcellular Protein Patterns Classification Using Extreme Gradient Boosting with Deep Transfer Learning as Feature Extractor(2024-01-01); Wacharawichanant, SiriratProteins are essential structural and functional components of human cells. Understanding and identifying proteins can provide valuable insights into their structure, function and role in human body. Subcellular proteins provide the expression that characterizes the many proteins and their conditions across cell types. This work proposed a classification model for subcellular protein patterns using XGBoost with transfer learning of CNN as the feature extractor. In the model training process, we used ResNet50, VGG16, Xception, and MobileNet as the pre-trained models based on the transfer learning technique to extract different features. The proposed model was used to classify subcellular proteins into 28 patterns. The XGBoost with ResNet50, VGG16, MobileNet, and Xception model achieved an accuracy level of 92.20% 92.77%, 91.63%, and 91.44%, respectively. The XGBoost with ResNet50, VGG16, MobileNet, and Xception model obtained an F1 score of 0.9179, 0.9233, 0.9131, and 0.9152, respectively. Considering the F1 score, All XGBoost with transfer learning of CNN models gave a high score. Therefore, all evaluation parameters clearly demonstrate the high performance of the subcellular protein pattern classification 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, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Morphology and Properties of Poly(Lactic Acid) and Ethylene-Methyl Acrylate Copolymer Blends with Organoclay(2017-10-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Comparison of Extreme Gradient Boosting and Convolutional Neural Network-Long Short-Term Memory for Service Demand Forecasting(2019-01-01); Wacharawichanant, SiriratThe accurate demand forecasting gives powerful insights on amount of the available resources should be allocated for the operation and can improve the effectiveness of the management to assure the completeness of the services. This work studied to forecast the number of patients, that applied for service demand forecasting. We proposed the comparison between XGBoost model and CNN-LSTM model for service demand forecasting. We created the XGBoost model and configured the optimal parameters by using grid search optimization techniques. In the same way, the CNN-LSTM model was constructed and adjusted the layers in the network structure. Later, the model has configured the optimal parameters obtained from the grid search optimization techniques. Both models were applied to two different datasets for forecasting the number of patients in the future. The results indicated that two models had skillful, and made the reliable forecasting in two datasets. This work evaluated and measured the model performance by calculating RMSE, and sMAPE, which was acceptable in all case study. The CNN-LSTM model gave better efficiency forecasting than XGBoost model. In contrast, XGBoost model operated faster, took a lower performing time, and lower CPU consumption than the CNN-LSTM model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Morphology and mechanical properties of poly(Lactic acid) with propylene-ethylene copolymer and α-cellulose(2019-01-01)