Papers by Virach Sornlertlamvanich
European Journal of Combinatorics, 2019
This study proposes a data labelling scheme for bed position classification task. The labelling s... more This study proposes a data labelling scheme for bed position classification task. The labelling scheme provides a set of bed position for the purpose of preventing the bed fall and bedsore injuries which seriously imperil the aging people health. Most of the elderly fall down when they attempt to get out of bed with unassisted bed exit. Also, there is a high possibility of rolling out of bed when an elderly lies close to the edge of the bed. In addition, a bedridden person, who cannot reposition by him/herself, has a high risk of bedsores. Repositioning in every two hours alleviates the prolonged pressure over on the body. We collected the data from a specific set of bed sensor and classified the signal into five positions on the bed, which are off-bed, sitting, lying center, lying left, and lying right. These five positions are the fundamental information for developing a model to capture the movement of the elderly on the bed. The precaution strategy is then able to be designed for the bed fall and bedsore prevention. The data of the five different positions are manually annotated by observing the synchronized video through a specially designed workbench. The combination of the positions of off-bed, sitting, and lying is used to detect a bed exit situation, and the combination of the positions in the lying state, i.e. lying center, lying left, and lying right, is used to detect the rolling out of bed situation. Moreover, to notify for reposition assisting in the bedridden, the three lying positions are used to calculate the time of the abiding position.
European Journal of Combinatorics, 2020
In an extremely fast development of technology era, we are now living in the age of Industry 4.0,... more In an extremely fast development of technology era, we are now living in the age of Industry 4.0, the age of realizing Cyber Physical System (CPS). The virtual space being realized by digital space concept will completely merge with our physical dimension in a very near future. Every smart ecosystem could make us more convenient to live. However, this technology could be a severe weapon which is able to damage our life, our assets, organization security, and national sovereignty and could affect the extinction of human kind. We strongly realize this concern and are proposing one of the solutions to secure our life in the next smart world, the Holistic Framework of Using Machine Learning for an Effective Incoming Cyber Threats Detection. We present an effective holistic framework which is easy to understand, easy to follow, and easy to implement a system to protect our digital space in an initial state. This approach describes all steps with the significant modules (I-D-A-R: Idea-Dataset-Algorithm-Result Framework with B-L-P-A: Brain-Learning-Planning-Action concept) and explains all major concern issues for developers. As a result of the I-D-A-R framework, we provide an important key success factor of each state. Finally, a comparison of detection accuracy between using Multinomial Naïve Bayes, Support Vector Machine (SVM) and Deep Learning algorithm, and the application of the feature engineering techniques between Principle Component Analysis (PCA) and Standard Deviation successfully show that we can reduce the computation time by using the proper algorithm that matches with each dataset characteristics while all prediction results still promising.
This research aimed to fine-tune image classification with deep learning techniques to verify the... more This research aimed to fine-tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent.
International Conference on Computational Linguistics, Dec 1, 2016
Complaint classification aims at using information to deliver greater insights to enhance user ex... more Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or services. Categorized information can help us quickly collect emerging problems in order to provide a support needed. Indeed, the response to the complaint without the delay will grant users highest satisfaction. In this paper, we aim to deliver a novel approach which can clarify the complaints precisely with the aim to classify each complaint into nine predefined classes i.e. accessibility, company brand, competitors, facilities, process, product feature, staff quality, timing respectively and others. Given the idea that one word usually conveys ambiguity and it has to be interpreted by its context, the word embedding technique is used to provide word features while applying deep learning techniques for classifying a type of complaints. The dataset we use contains 8,439 complaints of one company.
The COVID-19 pandemic has given a tremendous impact on the economy, changed the way of living of ... more The COVID-19 pandemic has given a tremendous impact on the economy, changed the way of living of the whole world. Many lives are lost, the labor markets are affected and the people lifestyle are changed in order to limit the impact of COVID-19 pandemic. As of now, it seems that the COVID vaccine is the only solution for the world to be safe again. In the United Kingdom and the United States, many studies have been conducted on the sentiment analysis where the emotions of participants before vaccination and after vaccination are observed. The first batch of vaccines has been launched at the end of 2020 while some developed countries started early vaccination campaigns, and others are still in the process of ordering vaccines and remained unvaccinated until early 2021. The vaccinations are prioritized on the high-risk groups, such as medical workers and the elderly population. Vaccination for people under age 18 are still not available in the initial stage. Despite the executions of vaccinations, there are various opinions on whether the COVID-19 vaccines are safe, and a number of the population remain skeptical of taking the vaccine. In this research, we analyze tweets to understand public perception on the COVID-19 vaccine by classifying the sentiments and attitudes towards the vaccination and the available types of vaccine [1]. Social media is an appropriate source of research to analyze public attitudes towards COVID-19 vaccine and what they feel about the various brands of the vaccine in the market. For this research, tweets written in two languages, English and Japanese, have been collected. In Japan, some related surveys on public willingness for vaccinations and the sentiment analysis are already conducted. This study randomly surveys on the users’ tweets about COVID-19 vaccination and their emotions expressed in their tweets. Due to the certain vaccination accidents, people in various countries become more concerned about the side effects and safety of the vaccine due to local deaths of various circumstances and unknown causes. In an attempt to help assess and understand public sentiment towards the initial stage of the vaccination campaign, sentiment analysis tools are utilized. It can discover that there are different sentiment patterns observed in different regions and time points as well as in different vaccine brands. It is expected that the text categorization process will be conducted using NLTK’s dedicated Twitter corpus. In social media data, users enter multiple punctuation marks, acronyms and emotions to express their emotions. TextBlob tool will be used for analysis, which computationally identifies and classifies text into three emotions: positive, negative or neutral. TextBlob is used because it processes data by including various letters, symbols, etc. in its dictionary. In this method, each word in the dictionary is based on whether it is positive or negative, while adding an emotional analysis of commonly used expressions. In this way, people’s attitudes towards vaccines in the UK, the US and Japan can be analyzed. The accuracy of the method is 73.3% in English and 71.9% in Japanese. The results show that the British and Americans are more neutral and positive about vaccines, while the Japanese are more pessimistic about vaccines [2].
2022 International Electronics Symposium (IES), Aug 9, 2022
Artificial Intelligence, Mar 25, 2020
International journal of smart computing and artificial intelligence, 2022
In this paper, we present a novel finger character recognition method in sign language using dime... more In this paper, we present a novel finger character recognition method in sign language using dimension reduction finger character feature knowledge base for similarity measure. A sign language communication is crucial method for deaf or hearing-impaired people. One of the most important problems is that very few people can understand a sign language. Essentially, there is not enough image data set for finger character learning. In addition to aligning a corpus of images of finger character, * it is necessary to realize an automatic recognition system for finger characters in a sign language. We construct a knowledge base for finger character features and apply it to realize a novel finger character recognition. Our method enables finger character recognition by similarity measure between the input finger character features and a knowledge base. The experimental results show that our approach efficiently utilizes the knowledge base generated from a small amount of finger character images. We also present our prototype system and experimental evaluation.
Journal of ICT Research and Applications, Aug 31, 2016
Social media are a powerful communication tool in our era of digital information. The large amoun... more Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove. Since classification is an important part of text mining, many techniques have been proposed to classify this kind of information. We developed an effective technique of social media text classification by semi-supervised learning utilizing an online news source consisting of well-formed text. The computer first automatically extracts news categories, well-categorized by publishers, as classes for topic classification. A bag of words taken from news articles provides the initial keywords related to their category in the form of word vectors. The principal task is to retrieve a set of new productive keywords. Term Frequency-Inverse Document Frequency weighting (TF-IDF) and Word Article Matrix (WAM) are used as main methods. A modification of WAM is recomputed until it becomes the most effective model for social media text classification. The key success factor was enhancing our model with effective keywords from social media. A promising result of 99.50% accuracy was achieved, with more than 98.5% of Precision, Recall, and F-measure after updating the model three times.
Journal of Sensors, Jan 31, 2020
Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to... more Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to the edge of a bed. These mishaps have a high possibility of serious injuries, such as bruises, soreness, and bone fractures. Moreover, a lack of repositioning the body of a bedridden elderly person may cause bedsores. To avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. In this study, we propose a bed position classification method based on the sensor signals collected from only four sensors that are embedded in a panel (composed of two piezoelectric sensors and two pressure sensors). It is installed under the mattress on the bed. The bed positions considered are classified into five different classes, i.e., off-bed, sitting, lying center, lying left, and lying right. To collect the training dataset, three elderly patients were asked for consent to participate in the experiment. In our approach, a neural network combined with a Bayesian network is adopted to classify the bed positions and put a constraint on the possible sequences of the bed positions. The results from both the neural network and Bayesian network are combined by the weighted arithmetic mean. The experimental results have a maximum accuracy of position classification of 97.06% when the proportion of coefficients for the neural network and the Bayesian network is 0.3 and 0.7, respectively.
IOS Press eBooks, Jan 23, 2023
This paper describes about project "Data Sensorium" launched at the Asia AI Institute of Musashin... more This paper describes about project "Data Sensorium" launched at the Asia AI Institute of Musashino University. Data Sensorium is a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.
IOS Press eBooks, Jan 14, 2022
Computer programming is popularized in 21 st century education in terms of allowing intensive log... more Computer programming is popularized in 21 st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system.
2022 7th International Conference on Business and Industrial Research (ICBIR)
Frontiers in Artificial Intelligence and Applications
Through technology, it is essential to seamlessly bridge the divide between diverse speaking comm... more Through technology, it is essential to seamlessly bridge the divide between diverse speaking communities (including the signer (the sign language speaker) community). In order to realize communication that successfully conveys emotions, it is necessary to recognize not only verbal information but also non-verbal information. In the case of signers, there are two main types of behavior: verbal behavior and emotional behavior. This paper presents a sign language recognition method by similarity measure with emotional expression specific to signers. We focus on recognizing the sign language conveying verbal information itself and on recognizing emotional expression. Our method recognizes sign language by time-series similarity measure on a small amount of model data, and at the same time, recognizes emotion expression specific to signers. Our method extracts time-series features of the body, arms, and hands from sign language videos and recognizes them by measuring the similarity of th...
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For the biomedical ontologies, Concept Similarity Measures (CSMs) become important in order to fi... more For the biomedical ontologies, Concept Similarity Measures (CSMs) become important in order to find similar treatments between diseases. For the ontology primitive concepts, they do not have enough definitions because they are partially defined in the ontology so one way to find the similarity between primitive concepts is to apply textual similarity methods between concept names. But existing textual similarity methods cannot give correct similarity degrees for all concept pairs. In this paper, we propose a new primitive concept name similarity measure based on natural language processing to get a better result in concept similarity measure in terms of noun phrase construction analysis. We conduct experiments on the standard clinical ontology SNOMED CT and make the comparison between our proposed method and existing two approaches against human expert results in order to prove our proposed similarity measure give correct and nearest similarity degree between primitive concepts.
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Papers by Virach Sornlertlamvanich