Hanskunatai, Anantaporn

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Hanskunatai, Anantaporn
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Hanskunatai, Anantapom
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anantaporn.ha@kmitl.ac.th

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    A new streaming learning for stream chunk data classification based on incremental learning and adaptive boosting algorithm
    (2018-01-01)
    Claypo, Niphat
    ;
    Hanskunatai, Anantaporn
    ;
    Jaiyen, Saichon
    Currently, stream data classification is a challenge task to discover new useful knowledge from massive and dynamic data in big data era. This paper proposes a streaming learning method based on the incremental learning using a new adaptive boosting algorithm for stream data. The proposed adaptive boosting consists of a new method for updating distribution weight and the new weight voting. This learning method concentrates on learning from sequential chunks of data stream. The distribution weight updating method uses error of previous hypothesis to update the weight. The learning method uses only one data chunk to create a new hypothesis at a time and after learning, the learned data chunk can be thrown away and can learn the new data chunk without using the previous learned data. The experimental results show that the accuracy of the proposed method is higher than other methods in all datasets.
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    Inspection System for Glass Bottle Defect Classification based on Deep Neural Network
    (2023-01-01)
    Claypo, Niphat
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    The problem of defects in glass bottles is a significant issue in glass bottle manufacturing. There are various types of defects that can occur, including cracks, scratches, and blisters. Detecting these defects is crucial for ensuring the quality of glass bottle production. The inspection system must be able to accurately detect and automatically determine that the defects in a bottle affect its appearance and functionality. Defective bottles must be identified and removed from the production line to maintain product quality. This paper proposed glass bottle defect classification using Convolutional Neural Network with Long Short-Term Memory (CNNLSTM) and instant base classification. CNNLSTM is used for feature extraction to create a representation of the class data. The instant base classification predicts anomalies based on the similarity of representations of class data. The convolutional layer of the CNNLSTM method incorporates a transfer learning algorithm, using pre-trained models such as ResNet50, AlexNet, MobileNetV3, and VGG16. In this experiment, the results were compared with ResNet50, AlexNet, MobileNetV3, VGG16, ADA, Image threshold, and Edge detection methods. The experimental results demonstrate the effectiveness of the proposed method, achieving high classification accuracies of 77% on the body dataset, 95% on the neck dataset, and an impressive 98% on the rotating dataset.
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    Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models
    (2023-01-01)
    Thananukhun, Kit
    ;
    Jaiyen, Saichon
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Hanskunatai, Anantaporn
    Question-Answering (QA) models are part of Natural Language Processing (NLP) field used for ensuring questions match the answers appropriately. QA consists of several steps, one of which is called Question Classification, which is to classify the context of communication. In this step, it categorizes group of questions based on what users need to know in order to combine answers within the same category and respond accurately. It helps saving us time to search for answers as well. In this paper, we present a question classification model for Thai Conversational Chatbot using Artificial Neural Network and Multilingual Bidirectional Encoder Representations from Transformer (BERT) models using BERT-base multilingual cased combined with Multilayer Perceptron (MLP). The method yields the highest accuracy of 92.57%, compared to the BERT-base multilingual cased combined with other classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Decision Trees (DTs) with the accuracy scores of 88.57%, 80.00%, 78.57% and 60.29%, respectively. In addition, we also compare the performance of our proposed BERT model with another well-known Thai word embedding model, called Thai2Vec, which also combines with other classification models including MLP, SVM, NB, KNN and DTs, and their results of accuracies are: 85.71%, 85.71%, 75.71%, 75.71% and 58.86%, respectively. From the experiments, the BERT model combined with MLP can achieve the highest performance in term of accuracy among other methods.
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    FAST DEFECT DETECTION FOR GLASS BOTTLE USING AUTOENCODER AND ERROR THRESHOLD
    (2024-06-01)
    Hanskunatai, Anantaporn
    ;
    Jaiyen, Saichon
    ;
    Claypo, Niphat
    Glass bottle defect detection is an important part of quality control process in any glass manufacturing industry. The bottles must be inspected before packaging. Machine vision for glass bottle defect detection is the technology and method to inspect and analyze the defects for images automatically. Machine vision requires high-ability method to detect the defect and reject the bottle with the defect quickly. In this paper, defect detection framework for glass bottle defect detection tasks using autoencoders and error threshold is proposed. The fast detection method, a small autoencoder neural network architecture was designed with only good bottle images to train an autoencoder neural network. The decoded images are representations of normal bottle images and calculate threshold errors value. Defect detection is done by comparing the error between the normal background image and the encoded images to a threshold error from the training set. The performance of our method was compared to several other methods: VGG16, MobileNetV3, ADA, edge detection and image threshold. The experimental results show that our method yields 80% of accuracy on the body dataset and 92% of accuracy on the neck dataset. The average training time of our method is faster than that of all other neural network-based methods. From the experimental results, we can conclude that our defect detection framework outperforms other approaches both in accuracy and training time for defect detection on the side wall of a glass bottle.
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    Thai Voice Recognition for Controlling Electrical appliances Using Long Short-Term Memory
    (2020-04-01)
    Saheaw, Wuttichai
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    Human speech possesses characteristics in each of the word that can be recognized and learned by computers. In this research, It is being proposed the use of the Deep Learning Model to predict speech turn-on and turn-off various electrical appliances, by using the sound conversion method that has been through the process to get the value of sound waves and applied toward training process in different ways. As the sound has more than 1 syllable and having characteristics of similar words that might difficult to predict. This research is based on Convolutional Neural Network (CNN) for comparison with the use of Long Short-Term Memory (LSTM), which is part of the Recurrent Neural Network (RNN) and Thai language Speech Dataset turn-on and turn-off by the 7 types of electrical appliances, the process of reducing noise and silence of the front and back of the audio files by 14 classes in total. The experimental results signify that the proposed Long Short-Term Memory can achieve the best accuracy.
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    FACE SPOOFING DETECTION BASED ON DEEP FEATURE EXTRACTION AND INSTANCE-BASED CLASSIFICATION
    (2023-02-01)
    Claypo, Niphat
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    Face recognition is an important task in smart home security for detecting a face or monitoring a person in a live video and verifying the identity of an authentic user. However, there have been spoofing face methods that can trick a face recognition algorithm into wrongly verifying the identity of the person. In this paper, we propose a new hybrid framework for spoofing face detection based on Convolutional Neural Network and Long Short-Term Memory (CNNLSTM) and instance-based learning algorithm. In addition, a new dataset called FSA-CCTV is proposed, which contains face images from CCTV video clips with many types of spoofing attacks. The performance of our method was compared to several other anti-spoofing methods: CNN and RI-LBP, SLRNN, HSV+YCbCr, ResNet50, YCbCr+SVM and YCbCr+KNN. The experimental results show that our method yielded 93.2% of Accuracy, 96.8% of Recall, 94% of Precision, 94.8% of F<inf>1</inf>-score and 0.93 of AUC on the FSA-CCTV dataset. From the experimental results we can conclude that the proposed algorithm outperforms other approaches and yielded the most stable classification accuracy on the proposed dataset.