Phankokkruad, Manop
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Phankokkruad, Manop
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manop.ph@kmitl.ac.th
14 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, 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, 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, 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, Ensemble Transfer Learning for Lung Cancer Detection(2021-07-23)Lung cancer is the most leading cause of death. One of the significant screening problems is the difficulty in diagnosing it at an early stage. Consequently, this is a better way to study the ensemble of the transfer learning model to improve their accuracy and performance for lung cancer detection. This study has proposed the three CNN models for detecting lung cancer using VGG16, ResNet50V2, and DenseNet201 architecture based on transfer learning the proposed models enhance to classify lung cancer into five different classes the three transfer learning of CNN architectures were used to train, test, and validate based on the image dataset the results reveal that the proposed models have performed the classification task for detecting lung cancer the models achieved an accuracy level of VGG16, ResNet50V2, and DenseNet201 were 62%, 90%, and 89%, respectively. Finally, the ensemble of the three proposed CNN models is created and validated the final proposed ensemble model achieved 91% validation accuracy that performed better than the other existing models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving Classification Efficiency Based on Combination of Extreme Gradient Boosting and Deep Transfer Learning(2023-01-01); Wacharawichanant, SiriratThe leading causes of blindness and low vision are ocular disease. Ocular disease such as glaucoma, cataract, diabetic retinopathy, and macular degeneration, which are diseases in which the risk of vision loss. Unfortunately, some ocular diseases have no symptoms until the late stages. Therefore, early-stage diagnosis of ocular disease is the best way to prevent vision loss. This work proposed the classification models for ocular disease classification using XGBoost in combination with deep transfer learning of CNN as the feature extractor. In the model training process, we used the pre-trained model including Xception and ResNet50 based on the transfer learning technique to extract different features. The proposed model was used to classify ocular disease into eight patterns. The XGBoost in combination with the ResNet50 model achieved an accuracy level of 87.82%, precision of 88.15%, sensitivity of 87.82%, and F1 score of 87.82%. The XGBoost in combination with the Xception model acquired an accuracy level of 87.02%, precision of 87.35%, sensitivity of 87.02%, and F1 score of 87.02%. By considering the F1 score, XGBoost in combination with transfer learning of CNN models gave a high score. Therefore, all evaluation parameters clearly indicate the high performance of the ocular disease classification model. The conclusion presents that the proposed method acquires more excellent performance than individual deep learning models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance Analysis and Comparison of Cerebral Stroke Prediction Models on Imbalanced Datasets(2022-01-01); Wacharawichanant, SiriratA cerebral stroke is an interrupt blood flow to the brain leading cause of death. A number of risk factors increase the risk of stroke occurence because of lifestyle. Machine learning is effective techniques can be applied in prediction of stroke. The different kind of algorithms give the various accuracy and performance in the prediction. This study has proposed the four machine learning algorithm for classifiers to predict of cerebral stroke. The proposed model with various classifier has considered the risk factors such as age, hypertension, heart disease, average glucose level, BMI, and smoking status as feature attributes to predict cerebral stroke. This study conducted on two stroke datasets, and improve the imbalanced of between classes by using SMOTE. The result shows that XGBoost provided the highest accuracy of around 98.08% and 96.73% by comparing to the other machine learning algorithms. In addition, this study evaluates the models by analyzing the statistical parameters include accuracy, precision, sensitivity, F1 score, and AUC. The evaluation reveals that the XGBoost, Random Forest, AdaBoost and KNN classifier achieved the average AUC value of 0.851, 0.868, 0.670 and 0.851, respectively. All models provided the high confidence values, whereas the model with XGBoost classifier gave the highest performance.