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Improving the Sound Classification Accuracy Using CNN-LSTM and MFCC with Audio Augmentation for Diagnosing Respiratory Disease
(2025-01-01)
Phankokkruad, Manop
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Wacharawichanant, Sirirat
Audio 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.
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Subcellular Protein Patterns Classification Using Extreme Gradient Boosting with Deep Transfer Learning as Feature Extractor
(2024-01-01)
Phankokkruad, Manop
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Wacharawichanant, Sirirat
Proteins 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.
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Morphology and Properties of Poly(Lactic Acid) and Polybutylene Succinate and Ethylene-co-Methyl Acrylate-co-Glycidyl Methacrylate Ternary Blends
(2023-01-01)
Wacharawichanant, Sirirat
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Netphong, Patcharin
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Pipatbannakit, Thakoon
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Phankokkruad, Manop
This work studied the morphological, mechanical, and thermal properties of poly(lactic acid) (PLA)/polybutylene succinate (PBS)/ethylene-co-methyl acrylate-co-glycidyl methacrylate (EMA-GMA) ternary blends. The polymer blends were prepared in an internal mixer and then molded into films by compression molding. The results showed that the PLA/PBS blends had immiscible morphology in which the PBS phase was dispersed in PLA matrix as spherical shape. The PBS droplet size increased with increasing PBS content. The fractured surface of PLA/PBS/EMA-GMA blends displayed more small crack of plastic deformations than that of PLA/PBS blends and the fracture behavior of PLA/PBA blends was changed to more ductile fracture when added with EMA-GMA. The addition of EMA-GMA in polymer blends improved the compatibility of two phases. The results of the mechanical properties showed that PBS and EMA-GMA addition improved the strain at break of PLA/PBS and PLA/PBS/EMA-GMA blends, respectively. The incorporation of EMA-GMA had no effect on the melting temperature and degradation temperature of PLA blends. The PLA/PBS/EMA-GMA blends had lower percent crystallinity than that of PLA/PBS blends and most of percent crystallinity decreased with increasing EMA-GMA loading due to its more amorphous structure than PLA and PBS.
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Improving Classification Efficiency Based on Combination of Extreme Gradient Boosting and Deep Transfer Learning
(2023-01-01)
Phankokkruad, Manop
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Wacharawichanant, Sirirat
The 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.
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Mechanical, Thermal and Morphological Properties of Poly(Lactic Acid) and Poly(Butylene Adipate-co-Terephthalate) Blends with Organoclay
(2022-01-01)
Wacharawichanant, Sirirat
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Wongpan, Krittaphorn
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Aksornnam, Khunpat
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Phankokkruad, Manop
This work studied the effect of nanoclay surface modified with 25-30 wt% of methyl dihydroxyethyl hydrogenated tallow ammonium (Clay-DHA) on morphological, mechanical and thermal properties of poly(lactic acid) (PLA) and poly(butylene adipate-co-terephthalate) (PBAT) blends. The PLA/PBAT (75/25 w/w) blends without and with Clay-DHA were melt mixed by an internal mixer and molded by compression method. The morphological analysis observed the phase separation of PLA/PBAT blends due to minor PBAT phase dispersed as spherical shape in PLA phase, indicating a poor interfacial adhesion between PLA and PBAT phases. The incorporation of Clay-DHA could improve the compatibility of polymer blends. The tensile testing found that the addition of Clay-DHA 1 and 3 phr increased Young’s modulus of PLA/PBAT blends. The addition of Clay-DHA decreased the strain at break of PLA/PBAT blends. The thermal degradation of PLA/PBAT blends and composites showed the similar thermal degradation process step. The addition of Clay-DHA was no effect on thermal stability and thermal properties of PLA/PBAT blends.
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Performance Analysis and Comparison of Cerebral Stroke Prediction Models on Imbalanced Datasets
(2022-01-01)
Phankokkruad, Manop
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Wacharawichanant, Sirirat
A 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.
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Stages of Progression Classification of Alzheimer's Disease Using Deep Transfer Learning Models with Over-Sampling
(2022-01-01)
Phankokkruad, Manop
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Wacharawichanant, Sirirat
Alzheimer's disease is a chronic neurodegenerative disease that affected patients loss of memory, ability of thinking, reading, and cognitive decline. The Alzheimer's disease divided stages of progression into four general stages on the basis of their symptoms. The early diagnosis helps to slow down the disease and reduce the costs of treatment. Since Alzheimer's disease has four stages of progression, the classification problems are those where a stage must be predicted in the case of an unequal number of instances of each class. This study has proposed the classification the stages of progression of Alzheimer's disease using four transfer learning models such as VGG19, Xception, ResNet50, and MobileNetV2. The proposed models classify Alzheimer's disease into four stage of progression. The models gained an accuracy level of VGG19, Xception, ResNet50, and MobileNetV2 model of 77.73%, 82.46%, 76.28% and 79.29%, respectively. By considering the F1 score, the Xception, VGG19, and ResNet50, and MobileNetV2 models gave the high score of 0.7995, 0.8870, 0.8305, and 0.5993, respectively. Therefore, the VGG19 model is the best model by considering the F1 score that means the VGG19 model is the best model in overall performance. Finally, this study measures the AUC value that indicates the ability to classify between classes. The results show that AUC value of MobileNetV2, Xception, ResNet50, and VGG19 are 0.9290, 0.9539, 0.7937, and 0.8037, respectively. Therefore, the Xception model is the best model that has capable of distinguishing the stages of progression of the Alzheimer's disease.
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Ensemble Transfer Learning for Lung Cancer Detection
(2021-07-23)
Phankokkruad, Manop
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.
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Influence of cellulose fiber content on morphology and properties of poly(Lactic acid)/propylene-ethylene copolymer/cellulose composites
(2021-01-01)
Wacharawichanant, Sirirat
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Opasakornwong, Patteera
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Poohoi, Ratchadakorn
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Phankokkruad, Manop
This work studied the effects of medium-length fibrous cellulose (MFC) on the morphology, mechanical and thermal properties of poly(lactic acid) (PLA)/propylene-ethylene copolymer (PEC) (90/10) blends. The morphological analysis of PLA/MFC composites observed MFC fibers inserted in the PLA matrix and MFC appeared agglomeration when added high MFC loading. The phase morphology showed the two-phase separation of PLA/PEC blends. The presence of PEC reduced the agglomeration of MFC fibers in polymer matrix. The tensile stress and strain curves found that the ultimate stress of PLA was the highest value and the addition of MFC increased Young’s modulus of PLA/MFC and PLA/PEC/MFC composites. The PEC presence improved the strain at breaking point of PLA/PEC blends. The thermal properties found that the incorporation of MFC did not improve the thermal stability of PLA/MFC and PLA/PEC/MFC composites due to the PLA had degradation temperature higher than MFC.
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Evaluation of Deep Transfer Learning Models in Glaucoma Detection for Clinical Application
(2021-01-01)
Phankokkruad, Manop
The clinical information supports the doctors in diagnosing the diseases and making the right decisions. Glaucoma is the leading cause of irreversible blindness disease. Vision loss can be avoided by early stage detection and right treatment. This study has proposed the deep transfer learning of the CNN model for detecting the glaucoma using ResNet50V2, VGG16, InceptionV3, and Xception. The proposed models help in the diagnosis of the patients who have glaucoma. The model with CNN architecture was used to learn from training the Glaucoma image dataset. Since the existing dataset has a small number of images, this study 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 glaucoma. The proposed model achieved an accuracy level of VGG16, RestNet50V2, InceptionV3, and Xception are 97.27%, 94.53%, 95.31%, and 94.92%, respectively. Furthermore, this study evaluates the models by considering the clinical performance parameters include accuracy, precision, specificity, sensitivity, and F1 score. All models provide the high confidence values. The evaluation reveals that the deep transfer learning model with VGG16 architecture is the highest performance in tests. The VGG16 model achieved the average AUC-ROC value of 98.94%.

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