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When are Latent Topics Useful for Text Mining?: Enriching Bag-of-Words Representations with Information Extraction in Thai News Articles
(2023-01-01)
Kanungsukkasem, Nont
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Chuangkrud, Piyawat
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Pitichotchokphokhin, Pimpitcha
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Damrongrat, Chaianun
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Leelanupab, Teerapong
The Bag-of-Words (BOW) model is simple but one of the successful representations of text documents. This model, however, suffers from the sparse matrix, in which most of the elements are zero. Topic modeling is an unsupervised learning method that can represent text documents in a low-dimensional space. Latent Dirichlet Allocation (LDA) is a topic modeling technique used for topic extraction and data exploration, with interpretable output. This paper presents a thorough study of potential benefits of applying LDA, as a feature extraction, to topic discovery and document classification in Thai news articles, comparing with TF–IDF and Word2Vec. We also studied how much of the top Thai terms extracted from LDA with the different numbers of topics can be interpretable and meaningful, and can be a representative of the corpus. Besides, a set of Topic Coherence measures were included in our study to estimate the degree of semantic similarity of extracted topics. To compare the performance and optimization time of classification of features from the different feature extraction methods, various types of classifiers, e.g., Logistic Regression, Random Forest, XGBoosting, etc., were experimented.
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Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images
(2023-01-01)
Pruthipanyasakul, Nareekarn
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Kanungsukkasem, Nont
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Urruty, Thierry
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Leelanupab, Teerapong
Integrating AI into medical diagnosis can provide a more accurate diagnosis when medical staff make treatment decisions. This paper studied on several deep neural networks, re-used with further training for a specific task in classifying the stenosis of a patient's coronary artery. From a 4DM-SPECT application, we collected polar map images that report, for example, myocardial perfusion, function and defect severity from cardiac emission computed tomography examination. We conducted a comparative study to identify the optimal combination of various state-of-the-art pre-trained models (i.e., VGG19, ResNet50, DenseNet121, and EfficientNetB0-B3) and eight different modalities of the myocardial perfusion images for classifying the stenosis of the coronary artery.
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A multi-sequences MRI deep framework study applied to glioma classfication
(2022-04-01)
Coupet, Matthieu
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Urruty, Thierry
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Leelanupab, Teerapong
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Naudin, Mathieu
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Bourdon, Pascal
Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology. Our proposed approach aims moreto create a computer-aided system that is capable of helping morethe expert diagnose the brain gliomas. moreWe propose a supervised learning regime based on a convolutional neural network based framework and transfer learning techniques. Our research morefocuses on the performance of different pre-trained deep learning models with respect to different MRI sequences. We highlight the best combinations of such model-MRI sequence couple for our specific task of classifying healthy brain against brain with glioma. moreWe also propose to visually analyze the extracted deep features for studying the existing relation of the MRI sequences and models. This interpretability analysis gives some hints for medical expert to understand the diagnosis made by the models. Our study is based on the well-known BraTS datasets including multi-sequence images and expert diagnosis.
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Keyword-Text Graph Representation for Short Text Classification
(2021-01-01)
Chuanakrud, Piyawat
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Leelanupab, Teerapong
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Damrongrat, Chaianun
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Kanunasukkasem, Nont
Short text classification is an essential task in Natural Language Processing. This task is widely applied to many applications, such as spam filtering, question-answering, artificial conversational agent, sentiment analysis, review mining, etc. Short texts usually encounter a great challenge for classification due to data sparseness as they do not provide sufficient contextual information. In this paper, we introduce Keyword-Text Graph Convolutional Networks (KwTGCN) for short text classification. We also propose a method to identify keywords by estimating word distribution over different categories. These category keywords are then used to build a special keyword-text graph of short text corpus. We employ Graph Convolutional Network (GCN) and our keyword-text graph to generate the representation of short text corpus based on the relations of document-keyword and document-word as well as the word co-occurrence. This document, word and keyword representation is further used as an input feature for the next layer of short text classification. The experimental results on multiple benchmark datasets show that our proposed model outperforms the state-of-the-art models for short text classification in multiple attempts.
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A Scalable Service Architecture with Request Queuing for Resource-Intensive Tasks
(2020-06-01)
Chaowvasin, Kasidis
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Sutanchaiyanonta, Pun
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Kanungsukkasem, Nont
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Leelanupab, Teerapong
Deploying Machine Learning (ML) prediction or Data Analytic (DA) process as a service in a Web API is not a trivial task. A number of settings and dependency requirements must be met to provide ML or DA successful solutions. In addition, an application that utilizes such an API needs to be always available to serve multiple users who can concurrently submit their requests. ML modeling or DA processing is a resource-intensive task, which can take a massive amount of time to process. Some tasks may take just a few minutes or hours while others may take several days to complete. In this paper, we design and develop a scalable architecture of API services for hosting ML models or DA functionalities in a production-grade deployment. The technologies of containerization and container orchestration, i.e., Docker and Kubernetes, have been employed to automate the deployment, scaling, and management of containerized ML or DA instances. To meet high-scale and high-availability requirements, the open-source message broker, i.e., RabbitMQ, is also used and containerized in Docker for scheduling multiple requests as task messages. These messages are then put into a task queue so that they will be processed later consecutively. Also, Nginx and Node.js with Express.js have been used and containerized as a web server and an API provider, respectively. We use a case-study of an intelligent system for processing documents about national research granting to validate our architecture.
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Discover Underlying Topics in Thai News Articles: A Comparative Study of Probabilistic and Matrix Factorization Approaches
(2020-06-01)
Pitichotchokphokhin, Pimpitcha
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Chuangkrud, Piyawat
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Kalakan, Kongkan
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Suntisrivaraporn, Boontawee
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Leelanupab, Teerapong
Topic modeling is an unsupervised learning approach, which can automatically discover the hidden thematic structure in text documents. For text mining, topic modeling is a language-independent technique that disregards grammar and word order. Apart from semantic and structural issues, Thai language is typically considered more complex than others. Due to the lack of word delimiter and a surfeit of composite words. Errors from word tokenization can create significant problems for any post processes of text, such as document retrieval, sentiment analysis, machine translation, etc., adversely decreasing the performance of text applications. Despite a strong correlation between word ordering and semantic meaning, topic modeling has been widely reported that it can extract latent information, aka. latent topic or latent semantic, encoded in documents. Although there were few previous research works on studying topic modeling in Thai language, they mostly focused on upstream processes of Natural Language Processing (NLP) in, for example, applying a refined stop-word list to, or adding N-gram on a single specific topic modeling method. To our knowledge, this paper is the first to explore different topic modeling approaches, i.e., Latent Dirichlet Allocation (LDA) and Nonnegative Metrix Factorization (NMF), in Thai Language to compare their coherence. We also employ and compare a set of state-of-the-art evaluation metrics based on Topic Coherence.
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An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences
(2020-01-01)
Coupet, Matthieu
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Urruty, Thierry
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Leelanupab, Teerapong
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Naudin, Mathieu
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Bourdon, Pascal
Gliomas are the most common central nervous system tumors. They represent 1.3% of cancers and are the 15th most common cancer for men and women. For the diagnosis of such pathology, doctors commonly use Magnetic Resonance Imaging (MRI) with different sequences. In this work, we propose a global framework using convolutional neural networks to create an intelligent assistant system for neurologists to diagnose the brain gliomas. Within this framework, we study the performance of different neural networks on four MRI modalities. This work allows us to highlight the most specific MRI sequences so that the presence of gliomas in brain tissue can be classified. We also visually analyze extracted features from the different modalities and networks with an aim to improve the interpretability and analysis of the performance obtained. We apply our study on the MRI sequences that are obtained from BraTS datasets.
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Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction
(2019-01-01)
Kanungsukkasem, Nont
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Leelanupab, Teerapong
News has been an important source for many financial time series predictions based on fundamental analysis. However, digesting a massive amount of news and data published on the Internet to predict a market can be burdensome. This paper introduces a topic model based on latent Dirichlet allocation (LDA) to discover features from a combination of text, especially news articles and financial time series, denoted as Financial LDA (FinLDA). The features from FinLDA are served as additional input features for any machine learning algorithm to improve the prediction of the financial time series. We provide posterior distributions used in Gibbs sampling for two variants of the FinLDA and propose a framework for applying the FinLDA in a text and data mining for financial time series prediction. The experimental results show that the features from the FinLDA empirically add value to the prediction and give better results than the comparative features including topic distributions from the common LDA.
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Learning and immediate retention of Japanese vocabulary using generated mnemonic keywords
(2017-08-29)
Leelanupab, Teerapong
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Anonthanasap, Orapin
This laboratory-based user study is designed to evaluate automated mnemonic keywords generation systems for Japanese vocabulary learning. We examine our successful methodology and in particular a new phonetic algorithm, named JemSoundex, for Japanese-to-English-Mnemonic phonetic matching, in a learning and immediate retention task. Our methodology retrieves and ranks candidate keywords by considering phonetic, orthographic and semantic similarities, as well as psycholinguistic power. Experimental results showed that keywords provided by JemSoundex improved learner performance in the task of a short-term vocabulary learning, in comparison with no keyword support and two traditional phonetic transcriptions (i.e., IPA and Soundex). This improvement was even more evident for difficult words having more syllables. Participants also rated keywords generated by our JemSoundex as more phonetically relevant and useful than those by other baselines.
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Evaluating a thermal icon for the enhancement of mobile feedback perception in noisy and bumpy environments
(2017-07-21)
Ketna, Monticha
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Leelanupab, Teerapong
The shortcoming of commonly used mobile feedbacks, e.g., audio and vibrotactile, is that they are often not suitable for some environments, such as in very loud and bumpy locations. The aim of this experiment is to evaluate how much thermal feedback increases user perception of mobile feedback in the situations where the audio and vibrotactile feedbacks may be suppressed due to noise and vibration. The two situations with interference, i.e., noise and vibration, were simulated. The perception of uni- A nd multimodal feedbacks, combined with a cold thermal icon, was tested as a means of a mobile device delivering notifications to users. Eleven stimuli were presented to subjects while six of those with the thermal icon were applied to the wrist of subjects. Experimental results showed that thermal feedback has potentials to be used solo or together with other feedbacks to enhance user perception in unsuitable situations. Furthermore, multimodal feedbacks, especially those with thermal stimulus, were reported to be more detectable than others. These insights indicate that thermal feedback may provide a salient alternative for user notification in modern mobile and wearable devices.

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