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Ocean surface current prediction is at the core of various marine operational routines, including disaster monitoring, oil-spill backtracking, sea navigation and search-and-rescue operations. More accurate prediction can yield significant improvement to the overall system. Most existing short-term prediction methods applied numerical models based on physical processes. In this paper, we propose an...
In any power plants, it is crucial to perform a preventive maintenance to avoid unexpected breakdown of machinery, e.g., circulating water pump, using data collected from various sensors. There have been prior attempts using just traditional prediction techniques. In this paper, we propose a two-stage model that employs a technique from time series analysis to predict when the machine tends to be...
Conventional clustering algorithms based on the assumption that a data point can be assigned to only a single cluster. In spite of, there are several types of data that a data point belongs to multiple categories and causes ground-truth clusters overlap. To handle this situation, several algorithms are proposed and referred as “overlapping clustering”. One of state-of-the-art partition-based overlapping...
A Character-level Convolutional Neural Network (Char-CNN) is an efficient text categorization method. It can be used in categorization task without a word segmentation step, which is necessary by traditional method for Thai. Currently, the existing model of Char-CNN uses a fixed input length and requires cutting off exceeding characters, which may lead to a missing of important content. In this paper,...
Multi-Label classification aims to classify an example that can belong to many classes. Although One-versus-All (OVA) is the most common approach, our prior work has shown that the proposed One-versus-One (OVO) always gives higher prediction accuracy than OVA. However, OVO requires an extremely high computational cost when there are a large number of labels. In this paper, we apply our OVO SVMs on...
Curriculum analysis is attracting widespread interest in educational field. There are two main approaches: (i) human-based and (ii) text-based assessments. Although an evaluation by teachers and learners are widely used, it is inconvenient and time-consuming. Also, the results absolutely rely on individual attitude. The text-based approach aims to directly evaluate the course syllabus; however, there...
Electroencephalography (EEG) is a user interface for communicating with patients, especially for tasks such as classifying Left/Right hand to represent YES/NO. Although there were many proposed classification techniques, none of them considered the non-stationary characteristic of brainwaves; thus, they cannot really be employed in real-world situations. In this paper, we aim to tackle the non-stationary...
In conventional algorithms a data point can be assigned to only a single cluster. However, in reality there are several types of data that a data point belongs to multiple categories and causes ground-truth clusters overlap. In this circumstance, conventional clustering cannot work effectively. To handle this problem, several algorithms are proposed and referred as “overlapping clustering”. One of...
Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. In this paper, we aim...
Sentiment analysis is very important for social listening, especially, when there are millions of Twitter users in Thailand nowadays. Almost all prior works are based on classical classification techniques, e.g., SVM, Naïve Bayes, etc. Recently, the deep learning techniques have shown promising accuracy in this domain on English tweet corpus. In this paper, we propose the first study that applies...
Electroencephalogram (EEG) has been used in the domain of emotion recognition, especially during the experience from music stimulus. A number of works have been submitted with promising results in emotion prediction tasks. Unfortunately, the majority of literature did not sufficiently take into account a non-stationary characteristic of EEG signals which could differ in each recording session, and...
Multi-label classification is a supervised learning, where one example can belong to several classes. In the case of Support Vector Machine (SVM), One-versus-All (OVA) is the most common approach to tackle this problem. However, the accuracy is very limited due to extremely imbalanced training set. It is interesting that there have only very few works that applied One-versus-One (OVO) in the multi-label...
Nowadays, Telecommunication service providers produce a huge volume of calling data records (CDR) each day. A clear understanding of their customers is a key success of any company. To analyze the behaviors and relationships between customers, social network analysis (SNA) is usually employed to detect influencers and communities along with calling behaviors (profiles). Unfortunately, the graph of...
It is important to detect breast cancers as early as possible, which are commonly diagnosed as a mass region on mammograms. Deep Convolutional networks (ConvNets) have been specially designed for various computer vision tasks. In image classification, it contains many layers to automatically extract image features and employs the softmax function at the last layer to predict a probability. Although...
In this research, electroencephalography (EEG) is used as an interface to communicate between patients and doctors. The signals from two electrodes (C3 and C4) are captured and used to classify Left/Right hand imagery representing YES/NO answers of the patients. In online applications, the training model mostly cannot be applied to the testing sessions due to a variation of the signals. Although some...
Twitter data has been becoming more interesting in social science study since it can effectively reflect a nature of human behavior. Unfortunately, it is complicated to analyze Twitter data, and the existing tools are not suitable for this domain. In this paper, we present a system that is tailored to analyze Twitter data for the social science research. The system comprises four main functions including:...
A summary judgement of Thai Supreme Court's case is necessary for legal research. Unfortunately, among thousands of decisions, judges traditionally spend a couple of months to manually create just one of them. In this paper, we present a system called “JudgeDoll, ” which automatically extracts the gist information and provides a legal summary of the full judgement. This system can help the judge to...
Hydro and Agro Informatics Institute (HAII) has installed more than 800 telemetry stations across Thailand to collect water level data for operation tasks and researches, e.g., flooding prevention system. To have an accurate result, it is crucial to control the quality of data by detecting and filtering out anomalies. In our previous work, a data quality management system to capture various types...
Hierarchical classification has been becoming a popular research topic nowadays, particularly on the web as text categorization. For a large web corpus, there can be a hierarchy with hundreds of thousands of topics, so it is common to handle this task using a flat classification approach, inducing a binary classifier only for the leaf-node classes. However, it always suffers from such low prediction...
Electroencephalograph (EEG) data is a recording of brain electrical activities, which is commonly used in emotion prediction. To obtain promising accuracy, it is important to perform a suitable data preprocessing; however, different works employed different procedures and features. In this paper, we aim to investigate various feature extraction techniques for EEG signals. To obtain the best choice,...
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