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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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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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Morphology and properties of poly(lactic acid)/ethylene-octene copolymer blends with different organoclay types
(2020-01-01)
Wacharawichanant, Sirirat
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Sriwattana, Attachai
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Yaisoon, Kulaya
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Phankokkruad, Manop
This work studied the morphology, mechanical and thermal properties of poly (lactic acid) (PLA)/ethylene-octene copolymer (EOC) (80/20) blends with different organoclay types. Herein, EOC was introduced to toughening PLA by melt blending and organoclay was used to improve compatibility and tensile properties of the blends. The two organoclay types were nanoclay surface modified with aminopropyltriethoxysilane 0.5-5 wt% and octadecylamine 15-35% (Clay-ASO) and nanoclay surface modified with dimethyl dialkyl (C14-C18) amine 35-45 wt% (Clay-DDA). The organoclay contents were 3, 5 and 7 phr. Scanning electron microscope (SEM) observation results revealed PLA/EOC blends demonstrated a two-phase separation of dispersed EOC phase and PLA matrix phase. The addition of organoclay significantly improved the compatibility between PLA and EOC phases due to EOC droplet size decreased dominantly in PLA matrix, so organoclay could act as an effective compatibilizer. The incorporation of organoclay increased significantly tensile strength of PLA/EOC/organoclay composites while Young’s modulus increased with 5 phr of organoclay. The thermal stability of PLA/EOC blends did not change when compared with neat PLA, and when added Clay-ASO in the blends could improve the thermal stability of the PLA/EOC blends.
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Improvement of poly(Lactic acid) properties by ethylene-octene copolymer and organoclay
(2020-01-01)
Wacharawichanant, Sirirat
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Hanjai, Paweena
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Khongaio, Sanya
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Phankokkruad, Manop
The work studied the morphological, mechanical and thermal properties of poly(lactic acid) (PLA)/ethylene-octene copolymer (EOC) blends before and after adding the montmorillonite clay surface modified with 25-30% of octadecylamine (clay-ODA). The PLA/EOC blends and composites were prepared by melt mixing in an internal mixer. The EOC contents were 5, 10, 20, 30 wt% and clay-ODA contents were 1 and 3 phr. The morphology analysis showed that the addition of clay-ODA could improve the miscibility of PLA and EOC phases due to the domain size of dispersed EOC phase decreased with increasing clay-ODA content. X-ray diffraction revealed the formation of intercalated/exfoliated structure in PLA/clay-ODA and PLA blend composites. The mechanical properties showed that the impact strength of PLA/EOC blends dramatically increased with increasing EOC content up to 10 wt%. The strain at break of PLA blends increased with increasing EOC content. Moreover, the incorporation of clay-ODA increased significantly Young’s modulus of PLA and PLA/EOC blends with increasing clay-ODA content. The thermal stability of PLA/EOC blends improved with the addition of a small amount of clay-ODA.

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