Kitsuchart Pasupa

Kitsuchart Pasupa
King Mongkut's Institute of Technology Ladkrabang · Faculty of Information Technology

BEng, MSc(Eng), PhD

About

126
Publications
38,235
Reads
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735
Citations
Introduction
He is an associate professor at King Mongkut's Institute of Technology Ladkrabang, Thailand. Previously, he was a research fellow working in the School of Electronics and Computer Science, University of Southampton and also a visiting research fellow at the University College London, Xerox Research Centre Europe, Helsinki University of Technology, University of Leoben, and Hokkaido University. Prior to this, he was a research associate and a PhD student at the University of Sheffield.
Additional affiliations
March 2018 - present
King Mongkut's Institute of Technology Ladkrabang
Position
  • Professor (Associate)
June 2014 - February 2018
King Mongkut's Institute of Technology Ladkrabang
Position
  • Professor (Assistant)
June 2011 - May 2014
King Mongkut's Institute of Technology Ladkrabang
Position
  • Lecturer
Education
October 2004 - November 2007
The University of Sheffield
Field of study
  • Automatic Control & Systems Engineering
October 2003 - September 2004
The University of Sheffield
Field of study
  • Control Systems
June 1999 - March 2003
Sirindhorn International Institute of Technology, Thammasat University, Thailand
Field of study
  • Electrical Engineering

Publications

Publications (126)
Article
Full-text available
Extreme learning machine (ELM) is a powerful classification method and is very competitive among existing classification methods. It is speedy at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require the comparison of facial images of two individuals simultaneously and decide whether the...
Article
Full-text available
Background: Many people use social media in their daily life for entertainment, business, personal communication and catching up with friends. In social media marketing, sentiment analysis is one of the most popular research topics because it can be employed to perform brand or market research monitoring and to keep an eye on the competitors. Machi...
Preprint
Full-text available
Extreme Learning Machine is a powerful classification method very competitive existing classification methods. It is extremely fast at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require comparison of facial images of two individuals at the same time and decide whether the two faces ide...
Article
Full-text available
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...
Article
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, va...
Preprint
div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image,...
Preprint
Full-text available
We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, whic...
Article
Full-text available
Evaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorit...
Conference Paper
Full-text available
Payment or fund transfer transactions can be annotated by users when they are made through a mobile banking app, for example, SCB Easy app—a mobile banking app by Siam Commercial Bank—allows users to annotate transactions with 40 character texts. The AI\(^2\) framework was used to identify user intentions with the transactions, so that the bank can...
Conference Paper
Abstract—Text-independent speaker verification is a task of verifying a speaker identity from a characteristic of voice. We proposed the combined deep Convolutional Neural Network (CNN) consisting of (i) the first CNN trained to achieve gender classification which is then used to create a gender-like embedding and (ii) the last CNN trained with one...
Article
Full-text available
Information of Red Blood Cell (RBC) morphology, obtained by analysing RBC images, is regularly requested by veterinarians to diagnose anaemic dogs. Machine learning techniques have been exploited to speed up the image classification. Recently, many researchers used deep learning techniques for classification; however, a large quantity of labelled d...
Preprint
Full-text available
In late 2019, the first case of COVID-19 was confirmed in Wuhan, China. The number of cases has been rapidly growing since then. Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19. However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply. Because...
Conference Paper
Full-text available
In late 2019, the first case of COVID-19 was confirmed in Wuhan, China. The number of cases has been rapidly growing since then. Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19. However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply. Because...
Conference Paper
Customer segmentation is an essential process that leads a bank to gain more insight and better understand their customers. In the past, this process requires analyses of data, both customer demographic and offline financial transactions. However, from the advancement of mobile technology, mobile banking has become more accessible than before. With...
Conference Paper
Unbalanced data is widespread in practice and presents challenges which have been widely studied in classical machine learning. A classification algorithm trained with unbalanced data is likely to be biased towards the majority class and thus show inferior performance on the minority class. To improve the performance of deep neural network (DNN) mo...
Conference Paper
Lip movement can be used as an alternative approach for biometric authentication. We describe a novel method for lip password authentication, using end-to-end 3D convolution and bidirectional long-short term memory. By employing triplet loss to train deep neural networks and learn lip motions, representation of each class is more compact and isolat...
Conference Paper
CDMC-International Cybersecurity Data Mining Competition (http://www.csmining.org) is a world unique data-analytic competition sitting in the trans-disciplinary area of artificial intelligence and cybersecurity. In this paper, we summarize CDMC’19—the 10th cybersecurity data mining competition, which was held in Sydney Australia—together with a cou...
Article
With the development of industry and technology, the development of the environment and cities has drawn lots of attention. Time series prediction plays a vital role in protecting the environment and improving the level of intelligence and technology in cities, for example prediction of air pollution, water levels, palm oil prices, financial data a...
Article
Full-text available
Background: The number of porcine Single Nucleotide Polymorphisms (SNPs) used in genetic association studies is very large, suitable for statistical testing. However, in breed classification problem, one needs to have a much smaller porcine-classifying SNPs (PCSNPs) set that could accurately classify pigs into different breeds. This study attempte...
Conference Paper
In principle, a porcine Single Nucleotide Polymorphism (SNP--a specific piece of nucleotide in a DNA sequence) can be associated with a trait 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 genomic research and several computer classification...
Article
Full-text available
Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained para...
Article
Full-text available
Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. In the past decade, many appro...
Article
Full-text available
A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time. Extreme learning machine (ELM) is one of the fastest learning algorithms based on a non-iterative method for a single hidden layer feedforward neural network (SLFN) model. ELM uses a ran...
Preprint
Full-text available
Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. In the past decade, many appro...
Book
The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 pape...
Book
The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually.The 187 full papers presented were carefully reviewed and selected from 618 submi...
Book
The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 pape...
Book
The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 187 full papers presented were carefully reviewed and selected from 618 subm...
Book
The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 187 full papers presented were carefully reviewed and selected from 618 subm...
Conference Paper
Full-text available
This paper proposes a novel algorithm called Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with a kernel filter and a modified Drift Detector Mechanism (Meta-RKOS-ELM\(_\mathrm{ALD}\)-DDM). The algorithm aims to tackle a well-known concept drift problem in time series prediction by utilising the modified concept drift d...
Conference Paper
Heat detection of cattle in video is essential for dairy farm. A cow should be inseminated within a certain period of time in order for it to breed successfully. After it has given birth to a calf, it produces milk. This paper proposes the use of a set of discriminative features to detect cattle in heat, where the features were extracted from the b...
Article
Full-text available
We have proposed a square wave quadrature amplitude modulation (SW-QAM) scheme for visible light communication (VLC) using an image sensor in our previous work. Here, we propose a robust and unified system by using a neural decoding method. This method offers essential SW-QAM decoding capabilities, such as LED localization, light interference elimi...
Article
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-ELMALD). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is u...
Article
Full-text available
Most visible light communication (VLC) technologies use a light emitting diode (LED) as a data transmitter and a photodiode as a receiver. In this paper, we alternatively focus on the use of an image sensor or camera as a receiver due to its wide availability. However, the successful use of an image sensor mainly depends on the efficiency of the en...
Preprint
Full-text available
A panel of large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pig. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exist a much...
Article
A smart city connects physical, information technology, social, and business infrastructures together to leverage their collective intelligence. Feedback drives improvements in service, city development, and quality of life in the city. Therefore, sentiment analysis in real-time of opinions expressed in text form by residents in the city is absolut...
Conference Paper
White blood cell classification plays a significant role in helping a physician to diagnose disease. Using automated analyser machine can easily analyse, fast, and accurate but the machine is very costly. Alternatively, this task can be manually performed by a human who is an expert in the field. However, it is very laborious. Machine learning and...
Conference Paper
Single-nucleotide polymorphisms (SNPs) are important genetic variables that are very popular in Genome-wide association study at the present time. They are often used in studies related to genetic disorders. A distinctive trait of SNPs is that there are a lot of them since they are variables originated from various positions in a DNA sequence. Unfo...
Article
Full-text available
Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to Noncommunicable Diseases such as Type II Diabetes, Cardiovascular Diseases and Cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This study aims to diagnose the risk of MetS using a new non-clinical...
Article
Full-text available
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This framework enables an automatic hairstyle reco...
Conference Paper
This paper proposes a meta-cognitive recurrent multi-step-prediction model called Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (Meta-RRKOS-ELM-DDM). This model combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine (RKOS-ELM) with the...
Conference Paper
E-commerce provides convenience and flexibility for consumers; for example, they can inquire about the availability of a desired product and get immediate response, hence they can seamlessly search for any desired products. Every day, e-commerce sites are updated with thousands of new images and their associated metadata (textual information), caus...
Article
Full-text available
Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward n...
Article
This paper proposes a novel recurrent multi-step-ahead prediction model called Recurrent Kernel Extreme Reservoir Machine (RKERM) with Quantum Particle Swarm Optimization (QPSO). This model combines the strengths of Recurrent Kernel Extreme Learning Machine (RKELM) and modified Reservoir Computing to overcome the limitations of prediction horizon w...
Conference Paper
Single Nucleotide Polymorphism (SNP) is a variability of DNA sequence that connects to a unique trait of an organism. A good SNP selection can provide a good porcine breed that grows fast with high yield. SNP selection can be done by a computerized feature selection method and classification technique. At present, an effective classification model...
Article
Full-text available
Monocomponent image decomposition plays an important role in image analysis and related areas, such as image denoising, object detection, and texture segmentation. Existing image decomposition methods can extract monocomponents but their performances are insufficiently accurate because of interference and redundancy component problems caused by ina...
Conference Paper
Recently, image sentiment analysis has become more and more attractive to many researchers due to an increasing number of applications developed to understand images e.g. image retrieval systems and social networks. Many studies aim to improve the performance of the classifier by many approaches. This work aims to predict the emotional response of...
Conference Paper
The revolution of the digital age has resulted in e-commerce where consumers’ shopping is facilitated and flexible such as able to enquire about product availability and get instant response as well as able to search flexibly for products by using specific keywords, hence having an easy and precise search capability along with proper product catego...
Conference Paper
Metabolic syndrome (MetS) is a combination of interrelated risk factors associated with an increased risk of developing type II diabetes Mellitus (T2DM), stroke and cardiovascular diseases (CVD). The economic, social and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Cu...
Article
Full-text available
Eye movement data collection is very expensive and laborious. Moreover, there are usually missing values. Assuming that we are collecting eye movement data from a set of images viewed by different users, there is a possibility that we will not able to collect the data of every user from every image–one or more views may not be represented in the im...
Article
Full-text available
This work focuses on error analyzes from the Support Vector Machine (SVM) classification on Thai children stories at a sentence level. The construction of the Sentiment Term Tagging System (STTS) program allows the researchers to make observations and hypothesize around the areas where most anomalies occur. Three hypotheses, based on terms sentimen...
Conference Paper
Hinge loss is one-sided function which gives optimal solution than that of squared error (SE) loss function in case of classification. It allows data points which have a value greater than 1 and less than \(-1\) for positive and negative classes, respectively. These have zero contribution to hinge function. However, in the most classification tasks...
Conference Paper
Extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feed-forward neural network (SLFN) based on random input weights which usually requires large number of hidden nodes. Recently, novel constructive and destructive parsimonious (CP and DP)-ELM which provide the effectiveness generalization and compact hidden nodes ha...
Conference Paper
Extreme Learning Machine (ELM) is a universal approximation method that is extremely fast and easy to implement, but the weights of the model are normally randomly selected so they can lead to poor prediction performance. In this work, we applied Weighted Similarity Extreme Learning Machine in combination with Jaccard/Tanimoto (WELM-JT) and cluster...
Conference Paper
Many machine learning algorithms have been introduced to solve different types of problem. Recently, many of these algorithms have been applied to deep architecture model and showed very impressive performance. In general, deep architecture model suffers from over-fitting problem when there is a small number of training data. In this paper, we atte...