Pasupa, Kitsuchart

Preferred name

Pasupa, Kitsuchart

Main Affiliation

School of Information Technology

kitsuchart.pa@kmitl.ac.th

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Towards Sentic-Aware Multimodal Models for Cyberbullying Detection in Thai Memes
(2026-01-01)
Weradechtaweewon, Nattawat
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Boondamnoen, Mongkol
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Pasupa, Kitsuchart
Cyberbullying has become an increasingly urgent issue in online communities. Memes, a popular form of online expression, often blend text and imagery in emotionally charged, sarcastic, or offensive ways–posing unique challenges for automatic harmful content detection. This work explores sentic-aware multimodal models for cyberbullying detection in Thai memes, with a focus on integrating affective commonsense knowledge through SenticNet-based features that emphasize conceptual reasoning and structured emotion representation. To enable this, we propose ThaiSenticNet 7, a resource adapted for the Thai language by translating from SenticNet 7, which supports the generation of sentic features. We investigate three representations–sentic vectors, sentic spectrograms, and sentic mel-spectrograms–and their integration with various sequential models to form sentic embeddings. These embeddings are fused with textual and visual information, extracted via a fine-tuned WangchanBERTa and a Swin Transformer, respectively, forming a unified multimodal pipeline. Experiments on a curated Thai meme dataset show that incorporating sentic features significantly enhances classification performance, with the best configuration–combining all three modalities–achieving an F<inf>1</inf>-score of 0.8044. Notably, the mel-spectrogram transformation proves particularly effective, suggesting that frequency-domain encoding helps capture subtle affective transitions in text-derived emotional signals. Our findings highlight the value of affective knowledge and multimodal modeling in tackling harmful content in memes.
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Efficient distributed SNP selection by a Modified Binary Flower Pollination Algorithm
(2020-07-01)
Rathasamuth, Wanthanee
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Pasupa, Kitsuchart
Porcine Single Nucleotide Polymorphisms (SNPs-certain pieces of nucleotide in a DNA sequence) can be indirectly associated with traits of an individual pig, like its meat quality or resistance to common diseases. It is most desirable to obtain a smallest number of most significant SNPs in genomics research, and several computer classification algorithms have been used to do so. For instance, for breed classification, one needs to obtain a set of a much smaller number of significant SNPs than that of the entire SNP data set. This study attempted to find such significant porcine SNPs by using computational feature selection and classification methods. In a preliminary trial, a binary flower pollination algorithm (BFPA) was used and shown not to able to reduce the number of selected SNPs to a sufficiently low number. Therefore, to achieve our objective, we developed a vertically distributed feature selection method incorporating a modified BFPA and a support vector machine classifier for selecting significant porcine SNPs. The developed method was evaluated and compared against four baseline methods. It provided the smallest average number of significant SNPs (128.40) that resulted in 94.57% classification accuracy. This and other findings in this study may directly benefit researchers in the bioinformatics field in their effort to map SNPs.
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Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches
(2021-08-01)
Tungjitnob, Suchat
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Pasupa, Kitsuchart
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Suntisrivaraporn, Boontawee
Nowadays, the banking industry has moved from traditional branch services into mobile banking applications or apps. Using customer segmentation, banks can obtain more insights and better understand their customers' lifestyle and their behavior. In this work, we described a method to classify mobile app user click behavior into two groups, i.e. SME and Non-SME users. This task enabled the bank to identify anonymous users and offer them the right services and products. We extracted hand-crafted features from click log data and evaluated them with the Extreme Gradient Boosting algorithm (XGBoost). We also converted these logs into images, which captured temporal information. These image representations reduced the need for feature engineering, were easier to visualize and trained with a Convolutional Neural Network (CNN). We used ResNet-18 with the image dataset and achieved 71.69% accuracy on average, which outperformed XGBoost, which only achieved 61.70% accuracy. We also evaluated a semi-supervised learning model with our converted image data. Our semi-supervised method achieved 73.12% accuracy, using just half of the labeled images, combined with unlabeled images. Our method showed that these converted images were able to train with a semi-supervised algorithm that performed better than CNN with fewer labeled images. Our work also led to a better understanding of mobile banking user behavior and a novel way of developing a customer segmentation classifier.
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Analysis of Dust Exposure Impact on Cardiovascular Diseases Risk Prediction in Bangkok, Thailand
(2021-01-21)
Sonsanit, Jakkrit
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Sirigate, Watcharin
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Pasupa, Kitsuchart
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Wiseschinda, Varut
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Kunanusont, Chaiyos
Cardiovascular disease (CVD) is a leading cause of death for people around the world. Prediction of CVD risk in advance is one of the most useful and effective tools to prevent and control your risk of developing CVD. Air pollution is a threat to health problems worldwide due to the development of the economy and society. The past studies found that air pollution was one factor that can cause CVD. Additionally, for the Thai population, pollution was one of the factors contributing to premature death. In this work, we aim to predict CVD risk using the patient data set from Bangkok Hospital in Bangkok alone with several algorithms and increase the accuracy using a combination of health and pollution. The prediction based on health data alone with the average AUC scores 0.89\pm 0.03, while the prediction with added air pollution data with the average AUC scores 0.91\pm 0.03 an average 0.02 increase than based on health data results alone. We found that considering pollution data can improve the overall performance of the model to predict CVD risk.
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Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-output Regression
(2024-01-01)
Wu, Zipeng
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Loo, Chu Kiong
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Pasupa, Kitsuchart
Online multi-output regression is a crucial task in machine learning with applications in various domains such as environmental monitoring, energy efficiency prediction, and water quality prediction. This paper introduces CONNRC, a novel algorithm designed to address online multi-output regression challenges and provide accurate real-time predictions. CONNRC builds upon the k-nearest neighbor algorithm in an online manner and incorporates a relevant chain structure to effectively capture and utilize correlations among structured multi-outputs. The main contribution of this work lies in the potential of CONNRC to enhance the accuracy and efficiency of real-time predictions across diverse application domains. Through a comprehensive experimental evaluation on six real-world datasets, CONNRC is compared against five existing online regression algorithms. The consistent results highlight that CONNRC consistently outperforms the other algorithms in terms of average Mean Absolute Error, demonstrating its superior accuracy in multi-output regression tasks. However, the time performance of CONNRC requires further improvement, indicating an area for future research and optimization.
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MS-PatchTST: Leveraging Multi-Scale Temporal Features for Water Level Forecasting
(2025-01-01)
Zhang, Dong
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Pasupa, Kitsuchart
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Liu, Zongying
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Pan, Mingyang
Accurate water level forecasting is essential for navigation, enabling safe sailing, effective drought management, optimized route planning, and efficient port operations. However, traditional statistical approaches and conventional machine learning models often struggle to capture adaptive, multi-scale temporal features, thereby limiting forecasting accuracy. In recent years, patch-based forecasting methods have demonstrated strong capabilities in modeling consecutive temporal features. Building on this foundation, we propose Multi-Scale PatchTST (MS-PatchTST), a framework designed to enhance the perception of multi-scale information. The model incorporates a newly developed multi-scale parallel convolutional network (Multi-Scale ConvNet) to extract interaction features across different time scales. These features are then fused through a Transformer Encoder with relative positional encoding to capture temporal dependencies more effectively. Finally, the kernel mean squared error loss function is employed in place of the conventional mean squared error loss, improving the optimization process and enhancing overall training performance. Experiments on four real-world water level datasets demonstrate that MS-PatchTST consistently outperforms state-of-the-art baselines, achieving an average reduction of approximately 13% in both MAE and SMAPE compared with PatchTST.
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HANCaps: A Two-Channel Deep Learning Framework for Fake News Detection in Thai
(2024-01-01)
Maity, Krishanu
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Bhattacharya, Shaubhik
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Phosit, Salisa
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Kongsamlit, Sawarod
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Saha, Sriparna
The rapid advancement of internet technology, widespread smartphone usage, and the rise of social media platforms have drastically transformed the global communication landscape. These developments have resulted in both positive and negative consequences. On the one hand, they have facilitated the dissemination of information, connecting individuals across vast distances and fostering diverse perspectives. On the other hand, the ease of access to online platforms has led to the proliferation of misinformation, often in the form of fake news. Detecting and combatting fake news has become crucial to mitigate its adverse effects on society. This paper presents an investigation into fake news detection in the Thai language. It addresses current limitations in this domain by proposing a novel two-channel deep learning model named HANCaps, which integrates BERT and FastText embeddings with a hierarchical attention network and capsule network. The HANCaps model utilizes the BERT language model as one channel input, while the other channel incorporates pre-trained FastText embeddings. The proposed model undergoes evaluation using a benchmark Thai fake news dataset, and extensive experimentation demonstrates that HANCaps outperforms state-of-the-art methods by up to 3.28% in terms of F1 score, showcasing its superior performance.
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CowXNet: An automated cow estrus detection system
(2023-01-01)
Lodkaew, Thanawat
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Pasupa, Kitsuchart
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Loo, Chu Kiong
Estrus detection is essential for dairy farms to take cows for artificial insemination promptly. Conventional approaches for detecting estrus cows use electronic devices attached to cows to gather data for software analysis. However, electronic devices can be costly and make a cow moody and uncomfortable while moving. In a common approach, observers detect estrus cows by observing their behaviors. However, continuous observation can easily lead to errors due to the observer's fatigue. Therefore, we designed CowXNet, an automatic estrus detection system for cows, to assist farmers to detect estrus cows. CowXNet requires only a camera attached in a pen and a computer to analyze recorded videos. CowXNet analyzes the estrus behaviors of each cow in a pen and helps farmers to identify estrus cows. To develop and evaluate CowXNet efficiently and effectively, we collected data from Chokchai Farm, the biggest dairy farm in Asia (14.65483<sup>∘</sup>N, 101.34853<sup>∘</sup>E). CowXNet has four modules: (i) cow detection uses YOLOv4 to detect cows in recorded videos; (ii) body part detection uses a convolutional neural network to estimate locations of body parts of detected cows; (iii) estrus behavior detection uses body part coordinates to extract a set of discriminative features, and a classification algorithm to detect estrus behaviors, and (iv) behavior analysis module displays estrus behavior for analysis purposes. We evaluated CowXNet for two instances: module-independent evaluation and end-to-end framework evaluation. Overall, CowXNet was promising; it correctly detected estrus behavior interval of cows 83% of cases.
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Augmenting Differentiable Neural Computer with Read Network and Key-Value Memory
(2021-01-01)
Yadav, Alok
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Pasupa, Kitsuchart
A Differential Neural Computer (DNC) is a type of neural network architecture that can make use of external memory. A DNC model can represent complex data sequences and reason about them. However, training a complex DNC with large memory matrices was slow, hence this study focused on improving a DNC model to learn faster and perform bAbi question-Answering tasks more accurately. The attempted improvements were to use key-value pairs for memory locations instead of arrays and to obtain a read vector from the memory matrix with a neural network. Evaluation of the improved DNC models on the bAbi dataset showed that their compute time was 13% shorter and their error rate was 6.6% lower. To conclude, the attempted improvements were successful, and the training speed of the key-value memory model also became shorter.
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Preface
(2020-01-01)
Yang, Haiqin
;
Pasupa, Kitsuchart

Pasupa, Kitsuchart

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