Pasupa, Kitsuchart

Preferred name

Pasupa, Kitsuchart

Main Affiliation

School of Information Technology

kitsuchart.pa@kmitl.ac.th

Now showing 1 - 10 of 13

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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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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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Weighted error-output recurrent Xavier echo state network for concept drift handling in water level prediction
(2024-11-01)
Liu, Zongying
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Zhang, Wenru
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Pan, Mingyang
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Loo, Chu Kiong
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Pasupa, Kitsuchart
Water level holds utmost significance in maritime domains. Precise water level predictions furnish indispensable insights for safe maritime navigation, guiding ships and vessels through passages, harbors, and waterways. This paper introduces a novel approach: the Weighted Error-Output Recurrent Xavier Echo State Network with Adaptive Forgetting Factor (WER-XESN-AFF). One of the contributions of this study is the introduction of the Xavier weights selection method, which replaces random weight selection from the Echo State Network (ESN). This method not only enhances forecasting performance but also reduces uncertainty in predictions. Additionally, two modified concept drift detectors, the Early Drift Detection Method and the Adaptive Forgetting Factor, are employed to address concept drift challenges. Another notable contribution is the introduction of a novel weighted error-output recurrent multi-step algorithm. This algorithm successfully overcomes the error accumulation problem by using past forecast errors to update current output weights. This study performs extensive experiments to evaluate the effectiveness of our approach in multi-step prediction in synthetic and real datasets. It compares the performance between the conventional randomization-based models and the ESN with the new weights selection approach and also tests the ability of concept drift detectors and the weighted error-output multi-step algorithm. Empirical findings and statistical analyses demonstrate that our proposed methods achieve expected effects, and the proposed model has better prediction ability than baselines. A significant improvement rate of 75.39% in Mean Squared Error is evident within the Jiujiang water level dataset when contrasting the performance of WER-XESN-AFF against the baseline model R-ESN across the 1–5 period.
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Highlight Detection in Podcasts: A Multimodal Deep Learning Approach
(2025-01-01)
Phuengpanyaloet, Wongsapat
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Boonruengkhao, Nonpipat
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Anchutin, Viktor
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Pasupa, Kitsuchart
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Loo, Chu Kiong
Podcasts have become a pervasive form of digital media, offering diverse content that often spans long hours. However, the vast volume of podcast episodes can make it challenging for listeners to locate the most engaging segments. Speech Emotion Recognition (SER) has witnessed remarkable advancements with the integration of deep learning techniques. This work proposes utilizing deep learning techniques employed in SER to discern emotional cues within podcasts, thereby enabling the detection of highlights. The task is framed as a binary classification problem, where the positive class contains examples of speech segments with high emotional activation. Transfer learning techniques from computer vision and speech recognition domains are applied, utilizing pre-trained models such as ConvNeXt, Vision Transformer, and wav2vec 2.0, which are compared with a baseline Convolutional Neural Network-Transformer hybrid. Additionally, multimodal models are introduced that learn from two distinct modalities: log mel-spectrograms and high-dimensional vector embeddings, both extracted from the raw audio data. The two modalities are combined using (i) a Simple Concatenated and (ii) CentralNet models. Experimental results demonstrate the effectiveness of combining two modalities over a single modality, achieving F<inf>1</inf>-scores of 0.6111 and 0.6270 for the Simple Concatenated and CentralNet models, respectively.
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Revisiting Echo State Networks for Continuous Gesture Recognition
(2022-01-01)
Yadav, Alok
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Pasupa, Kitsuchart
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Loo, Chu Kiong
Smartphones are equipped with Inertial Measurement Units (IMUs) that can capture user gesture data. Continuous gesture recognition is essential as it can be utilized and enhance human-computer interaction. Echo State Networks (ESNs) and Long Short-Term Memory (LSTM) models are well suited to performing this task. They have been successfully applied to the task in previous research, with LSTMs outperforming ESNs while having a considerably longer training time. However, the application of ESNs to continuous gesture recognition has not been fully explored as only the leaky integrator ESN has been used without hyperparameter optimization. In this study, we attempt to improve the ESN performance on the continuous gesture recognition task by experimenting with different model architectures and hyperparameter tuning. The performance of ESN models is significantly enhanced in terms of F1-score to 0.88, which is higher than the previously best performance of 0.87 using an LSTM model on continuous gesture recognition. The significant improvement is in training time, which is approximately 13 seconds for the ESN model compared to 89 seconds for the LSTM model in past research.
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DIAGNOSIS OF METABOLIC SYNDROME USING MACHINE LEARNING, STATISTICAL AND RISK QUANTIFICATION TECHNIQUES: A SYSTEMATIC LITERATURE REVIEW
(2021-01-01)
Kakudi, Habeebah Adamu
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Loo, Chu Kiong
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Moy, Foong Ming
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Kau, Lim Chee
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Pasupa, Kitsuchart
Metabolic syndrome (MetS), known to substantially lower the quality of life is associated with the increased incidence of non-communicable diseases (NCDs) such as type II diabetes mellitus, cardiovascular diseases and cancer. Evidence suggests that MetS accounts for the highest global mortality rate. For the early and accurate diagnosis of MetS, various statistical and ML techniques have been developed to support its clinical diagnosis. We performed a systematic review to investigate the various statistical and machine learning techniques (ML) that have been used to support the clinical diagnoses of MetS from the earliest studies to December 2020. Published literature relating to statistical and ML techniques for the diagnosis of MetS were identified by searching five major scientific databases: PubMed, Science Direct, IEEE Xplore, ACM digital library, and SpringerLink. Fifty-seven primary studies that met the inclusion criteria were obtained after screening titles, abstracts and full text. Three main types of techniques were identified: statistical (n=10), ML (n=44), and risk quantification (n=3). Standardized Z-score is the only statistical technique identified while the ML techniques include principal component analysis, confirmatory factory analysis, artificial neural networks, multiple logistics regression, decision trees, support vector machines, random forests, and Bayesian networks. The areal similarity degree risk quantification, framingham risk score and simScore were the three risk quantification techniques identified. Evidence suggests that evaluated ML techniques, with accuracy ranging from 75.5% to 98.9%, can more accurately diagnose MetS than both statistical and risk quantification techniques. The standardised Z-score is the most frequent statistical technique identified. However, highlighted proof based on performance measures indicate that the decision tree and artificial neural network ML techniques have the highest predictive performance for the prediction of MetS. Evidence suggests that more accurate diagnosis of MetS is required to evaluate the predictive performance of the statistical and ML techniques.
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Optimizing echo state networks for continuous gesture recognition in mobile devices: A comparative study
(2024-03-15)
Yadav, Alok
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Pasupa, Kitsuchart
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Loo, Chu Kiong
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Liu, Xiaofeng
Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F<inf>1</inf>-score: 0.89), leaving out a fraction of data from all users to use in testing (F<inf>1</inf>-score: 0.96), and training and testing using LOOCV on a single user (F<inf>1</inf>-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F<inf>1</inf>-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.
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Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling
(2020-02-01)
Liu, Zongying
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Loo, Chu Kiong
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Pasupa, Kitsuchart
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Seera, Manjeevan
This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELM<inf>ALD</inf>). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELM<inf>ALD</inf> with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.
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An Interpretable Multi-target Regression Method for Hierarchical Load Forecasting
(2023-01-01)
Wu, Zipeng
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Loo, Chu Kiong
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Pasupa, Kitsuchart
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Xu, Licheng
Accurate energy load forecasting provides good decision support for energy management. Current energy load forecasts focus more on forecast accuracy without exploring the similar patterns and correlations of energy load demand between regions. Our proposed interpretable hybrid multi-target regression approach provides more explanatory abilities for each region’s energy load prediction. After combining the correlation between forecast targets and hierarchical forecast information, our model achieves a high forecast accuracy in that the mean square error is reduced by three quarters compared to LightGBM’s independent prediction for each region on the GEFCom 2017 dataset.
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Self-attention hierarchical kernel reservoir state network for inland water level prediction
(2026-01-15)
Liu, Zongying
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Xu, Xiaohan
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Pasupa, Kitsuchart
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Loo, Chu Kiong
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Wei, Yang
Waterway transportation sustainably facilitates global trade through eco-efficient cargo movement, where accurate water level forecasting is critical for ensuring navigational safety and operational continuity. To develop a highly accurate prediction model, it is essential to consider the periodic characteristics of water level data, which often emerge in real-world datasets. This study introduces a novel reservoir state structure based on reservoir computing theory, the Self-attention Hierarchical Kernel Reservoir State Network (SHK-RSN). It employs three primary mechanisms. First, a hierarchical feature extraction method groups training data and extracts high-dimensional features from these groups using the kernel trick in a hierarchical manner. Second, a self-attention weight selection approach is introduced to replace the random weights in the Hierarchical Kernel Reservoir State Network (HK-RSN), improving the rationale for hidden neuron connections and enhancing the interpretability of weight selection. Third, a novel reservoir state structure is proposed to capture periodic information and extract temporal features across periods, enabling the model to capture richer temporal information and identify relationships among periods. Experiments are conducted on one artificial and five real-world time series datasets, with forecast performance evaluated over 1–7 steps. Our proposed model, SHK-RSN, is compared with models based on randomization, the kernel trick, and deep learning. The experimental results demonstrate that SHK-RSN exhibits superior forecasting ability relative to the baselines. It achieves the best Symmetric Mean Absolute Percentage Error (SMAPE) across all datasets in the 1–7 period average among baseline methods, demonstrating a relative improvement of 25.7% to 46.9% over the conventional Echo State Network.

Pasupa, Kitsuchart

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