Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549939
Yi-Cheng Liu, Ting-Wei Chang, Hung-Chih Chiang, Chir-Weei Chang, Yuan-Chin Lee
Demodex mite is believed to cause skin diseases such as rosacea, demodex folliculitis and demodex-aggravated perioral dermatitis. Its conventional diagnostic methods are skin scrape tests and superficial biopsies, which are invasive and painful to patients.
{"title":"Rapid, Noninvasive Diagnosis of Demodex Mite by Using a Low Cost, Portable, Full-Field OCT","authors":"Yi-Cheng Liu, Ting-Wei Chang, Hung-Chih Chiang, Chir-Weei Chang, Yuan-Chin Lee","doi":"10.1109/ICIIBMS.2018.8549939","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549939","url":null,"abstract":"Demodex mite is believed to cause skin diseases such as rosacea, demodex folliculitis and demodex-aggravated perioral dermatitis. Its conventional diagnostic methods are skin scrape tests and superficial biopsies, which are invasive and painful to patients.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"35 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132870878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549994
Hnin Pwint Myu Wai, Phyu Phyu Tar, P. Thwe
Today, web is a huge repository of information which needs for accurate automated classifiers for Web pages. Classification of Web page is essential to many tasks in Web information retrieval such as maintaining, web directories and focused crawling. So, this system proposes as the web page classification system based on semantic logic. For semantic, this system uses the ontology that stores each concept of each word. For classification, this system proposes the enhanced C4.5 decision tree and Naive Bayesian (NB) classifiers. In the original C4.5 classification algorithm, the traditional entropy measure is unable to measure the appropriateness of nodes when the class labels are the same. By using semantic technology, this system can effectively support to classify web pages into each category. To show the effectiveness, this system is tested by using HTML documents in the computer science domain.
{"title":"Ontology Based Web Page Classification System by Using Enhanced C4.5 and Naïve Bayesian Classifiers","authors":"Hnin Pwint Myu Wai, Phyu Phyu Tar, P. Thwe","doi":"10.1109/ICIIBMS.2018.8549994","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549994","url":null,"abstract":"Today, web is a huge repository of information which needs for accurate automated classifiers for Web pages. Classification of Web page is essential to many tasks in Web information retrieval such as maintaining, web directories and focused crawling. So, this system proposes as the web page classification system based on semantic logic. For semantic, this system uses the ontology that stores each concept of each word. For classification, this system proposes the enhanced C4.5 decision tree and Naive Bayesian (NB) classifiers. In the original C4.5 classification algorithm, the traditional entropy measure is unable to measure the appropriateness of nodes when the class labels are the same. By using semantic technology, this system can effectively support to classify web pages into each category. To show the effectiveness, this system is tested by using HTML documents in the computer science domain.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"55 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131965835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8550004
Yuan Wanjun, Wu Yuan
Blockchain technology is actually a distributed database technology. It adopts a series of security technologies such as P2P network technology, asymmetric encryption technology and smart contract technology to ensure the security and reliability of transactions. It has advantages of decentralization, anonymity and traceability. In view of the problems existing in the current centralized network transaction mode, this paper creatively proposes the design scheme of “network trading system based on blockchain technology”, and proposes a safe and feasible network transaction system model to ensure the transaction in network data transmission, data processing and other aspects of security. In-depth research and elaboration of some key technologies and principles in the network trading system. Comprehensive application of P2P network technology, asymmetric encryption technology, consensus mechanism, smart contract and other technologies to solve the security problem of network transaction systems. The specific implementation process is given for the transaction system from the aspects of demand analysis, transaction process, interface design, data model design and storage scalability design. Finally, it summarizes the application of blockchain technology in network transactions and points out the future research direction. (Abstract)
{"title":"Research on Network Trading System Using Blockchain Technology","authors":"Yuan Wanjun, Wu Yuan","doi":"10.1109/ICIIBMS.2018.8550004","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8550004","url":null,"abstract":"Blockchain technology is actually a distributed database technology. It adopts a series of security technologies such as P2P network technology, asymmetric encryption technology and smart contract technology to ensure the security and reliability of transactions. It has advantages of decentralization, anonymity and traceability. In view of the problems existing in the current centralized network transaction mode, this paper creatively proposes the design scheme of “network trading system based on blockchain technology”, and proposes a safe and feasible network transaction system model to ensure the transaction in network data transmission, data processing and other aspects of security. In-depth research and elaboration of some key technologies and principles in the network trading system. Comprehensive application of P2P network technology, asymmetric encryption technology, consensus mechanism, smart contract and other technologies to solve the security problem of network transaction systems. The specific implementation process is given for the transaction system from the aspects of demand analysis, transaction process, interface design, data model design and storage scalability design. Finally, it summarizes the application of blockchain technology in network transactions and points out the future research direction. (Abstract)","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122412685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549966
Hiroki Ito, K. Oiwa, A. Nozawa
Psychophysiological states have been evaluated using facial skin temperature, measured by infrared thermography. However, it is necessary to extract facial skin temperature manually, which is a technical problem in thermal image analysis. The objective of this study is to establish a technique for face tracking on thermal images. In this study, face tracking on thermal images was attempted using background subtraction and temporal analysis. As a result, the face region could be tracked with high precision, except during left-to-right horizontal movement.
{"title":"Face Tracking based on Temperature Distribution of Thermal Images for Real-Time Psychophysiological States Evaluation using Facial Skin Temperature","authors":"Hiroki Ito, K. Oiwa, A. Nozawa","doi":"10.1109/ICIIBMS.2018.8549966","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549966","url":null,"abstract":"Psychophysiological states have been evaluated using facial skin temperature, measured by infrared thermography. However, it is necessary to extract facial skin temperature manually, which is a technical problem in thermal image analysis. The objective of this study is to establish a technique for face tracking on thermal images. In this study, face tracking on thermal images was attempted using background subtraction and temporal analysis. As a result, the face region could be tracked with high precision, except during left-to-right horizontal movement.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121989149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8550011
Akerke Altaikyzy, Haiyan Fan, Yingqiu Xie
Bacterial infections have been clinically treated with variety of antibiotics. The excessive use of antibiotic nowadays, however, has caused severe drug resistance by the emergence of bacteria strains. The improper management of antibiotic usage has led to the contamination of the environment that threatens people's life. Therefore, it becomes critical to develop novel antibacterial agents that will reduce the risk of drug resistance to its minimum. As the nanomaterial and nanotechnology find their ways to anchor on nearly every aspect of our daily life, the antibacterial properties of nanoparticles have been studied and have shown promising effect while treating different strains of bacteria. Metal containing nanomaterials, though very effective, may potentially accumulate in human body and become cytotoxic. Recently, carbon nano dots derived from natural product have shown comparable antibacterial effect but are low cytotoxicity to human cells and cost. In this study, bactericidal effect of dates-derived carbon nanoparticles (CNPs) on survival of different gram-negative or gram-positive strains was tested. Dates-derived CNPs exhibited strong antibacterial effect against both gram-positive and gram-negative bacteria. Impressively, complete inhibition in the growth of all bacterial strains used in this research was achieved using as prepared CNPs. Moreover, the as prepared CNP was discovered as a great sensor to detect the pollution in ocean water. In deed, an enzyme kit was developed for the ocean water pollution detection. Thus CNP has great potential as biosensor both in medicine and pollution detection of ocean water.
{"title":"Effects of Carbon Nanoparticles on Bacteria and Application Potential as Biosensors of Pollution Detection of Ocean Water Carbon Nanoparticles as Antibiotics and Biosensors of Ocean Water","authors":"Akerke Altaikyzy, Haiyan Fan, Yingqiu Xie","doi":"10.1109/ICIIBMS.2018.8550011","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8550011","url":null,"abstract":"Bacterial infections have been clinically treated with variety of antibiotics. The excessive use of antibiotic nowadays, however, has caused severe drug resistance by the emergence of bacteria strains. The improper management of antibiotic usage has led to the contamination of the environment that threatens people's life. Therefore, it becomes critical to develop novel antibacterial agents that will reduce the risk of drug resistance to its minimum. As the nanomaterial and nanotechnology find their ways to anchor on nearly every aspect of our daily life, the antibacterial properties of nanoparticles have been studied and have shown promising effect while treating different strains of bacteria. Metal containing nanomaterials, though very effective, may potentially accumulate in human body and become cytotoxic. Recently, carbon nano dots derived from natural product have shown comparable antibacterial effect but are low cytotoxicity to human cells and cost. In this study, bactericidal effect of dates-derived carbon nanoparticles (CNPs) on survival of different gram-negative or gram-positive strains was tested. Dates-derived CNPs exhibited strong antibacterial effect against both gram-positive and gram-negative bacteria. Impressively, complete inhibition in the growth of all bacterial strains used in this research was achieved using as prepared CNPs. Moreover, the as prepared CNP was discovered as a great sensor to detect the pollution in ocean water. In deed, an enzyme kit was developed for the ocean water pollution detection. Thus CNP has great potential as biosensor both in medicine and pollution detection of ocean water.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131483418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8550030
Pariwat Prathanrat, Chantri Polprasert
In this paper, we employ machine learning to predict the performance of Jupyter notebook on JupyterHub. We show that the notebook's CPU profile, the notebook's RAM profile, number of users and average delay between cells are crucial features that impact the performance of the machine learning models to accurately predict the performance of Jupyter notebook in term of the response time. We characterize the performance of our model to predict the notebook's response time in terms of the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results show that the random forest model yields strongest performance to predict the performance of Jupyter notebook with MAPE equal to 9.849% and MAE equal to 13.768 seconds. with r-square equal to 0.93.
在本文中，我们使用机器学习来预测Jupyter笔记本在JupyterHub上的性能。我们表明，笔记本电脑的CPU配置文件，笔记本电脑的RAM配置文件，用户数量和单元之间的平均延迟是影响机器学习模型性能的关键特征，以准确预测Jupyter笔记本电脑在响应时间方面的性能。我们根据平均绝对误差(MAE)和平均绝对百分比误差(MAPE)来描述模型的性能，以预测笔记本电脑的响应时间。结果表明，随机森林模型在预测Jupyter笔记本性能时，MAPE = 9.849%， MAE = 13.768秒，效果最好。r方等于0.93。
{"title":"Performance Prediction of Jupyter Notebook in JupyterHub using Machine Learning","authors":"Pariwat Prathanrat, Chantri Polprasert","doi":"10.1109/ICIIBMS.2018.8550030","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8550030","url":null,"abstract":"In this paper, we employ machine learning to predict the performance of Jupyter notebook on JupyterHub. We show that the notebook's CPU profile, the notebook's RAM profile, number of users and average delay between cells are crucial features that impact the performance of the machine learning models to accurately predict the performance of Jupyter notebook in term of the response time. We characterize the performance of our model to predict the notebook's response time in terms of the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results show that the random forest model yields strongest performance to predict the performance of Jupyter notebook with MAPE equal to 9.849% and MAE equal to 13.768 seconds. with r-square equal to 0.93.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"70 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130655135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549989
Chan-Uk Yeom, Keun-Chang Kwak
This paper is concerned with the prediction of full load electrical power output of a base load operated Combined Cycle Power Plant (CCPP) based on Takai-Sugeno-Kang (TSK)-based Extreme Learning Machine (ELM). Here TSK-based ELM is designed by a systematic approach to producing automatic fuzzy if-then rules, while the conventional ELM is designed without knowledge information. The design of TSK-ELM consists of two main steps. In the first step, an initial randomly partition matrix is generated and cluster centers for random clustering are estimated. These centers are used to determine the premise part of fuzzy rules. Next, the linear parameters of the TSK fuzzy type in consequent part are estimated using the Least Squares Estimate (LSE) method. The experiments were performed on prediction of electrical power in CCPP by the presented TSK-ELM. The input variables include hourly average ambient variables temperature, ambient pressure, relative humidity and exhaust vacuum. The output variable is used to predict the net hourly electrical energy output. The experimental results revealed that the presented TSK-ELM showed good performance in compared to the original ELM.
{"title":"A Design of TSK-Based ELM for Prediction of Electrical Power in Combined Cycle Power Plant","authors":"Chan-Uk Yeom, Keun-Chang Kwak","doi":"10.1109/ICIIBMS.2018.8549989","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549989","url":null,"abstract":"This paper is concerned with the prediction of full load electrical power output of a base load operated Combined Cycle Power Plant (CCPP) based on Takai-Sugeno-Kang (TSK)-based Extreme Learning Machine (ELM). Here TSK-based ELM is designed by a systematic approach to producing automatic fuzzy if-then rules, while the conventional ELM is designed without knowledge information. The design of TSK-ELM consists of two main steps. In the first step, an initial randomly partition matrix is generated and cluster centers for random clustering are estimated. These centers are used to determine the premise part of fuzzy rules. Next, the linear parameters of the TSK fuzzy type in consequent part are estimated using the Least Squares Estimate (LSE) method. The experiments were performed on prediction of electrical power in CCPP by the presented TSK-ELM. The input variables include hourly average ambient variables temperature, ambient pressure, relative humidity and exhaust vacuum. The output variable is used to predict the net hourly electrical energy output. The experimental results revealed that the presented TSK-ELM showed good performance in compared to the original ELM.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116162205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549921
Sehoon Yang, Sang-Joon Lee, Yungcheo l Byun
Gesture recognition has lots of applications in automation including home device control. Nowadays, a smartphone is a very common device which can be utilized to capture gesture information. In this paper, we propose a method to recognize gestures using machine learning, which uses gesture data collected from a gyroscope sensor in a smartphone. We implemented and tested to verify our method, and as a result, we found that the method showed an acceptable rate of recognition for home automation.
{"title":"Gesture Recognition for Home Automation Using Transfer Learning","authors":"Sehoon Yang, Sang-Joon Lee, Yungcheo l Byun","doi":"10.1109/ICIIBMS.2018.8549921","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549921","url":null,"abstract":"Gesture recognition has lots of applications in automation including home device control. Nowadays, a smartphone is a very common device which can be utilized to capture gesture information. In this paper, we propose a method to recognize gestures using machine learning, which uses gesture data collected from a gyroscope sensor in a smartphone. We implemented and tested to verify our method, and as a result, we found that the method showed an acceptable rate of recognition for home automation.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134507971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549957
Munikrishna D C, K. Raja, V. R.
Face recognition has become the new captivating field for scientists and researchers the world over. This paper, proposes an algorithm based on the convolution of the Pixel Difference Vector (PDV) and Local Binary Pattern (LBP) features. The features from the two techniques are convolved to generate a square matrix, which is then reshaped into a column vector. The column vectors of all the images that are present in the database are compared against the column vectors of the test image, making use of Euclidean Distance (ED). Following this, the location of the image in the database is obtained to detect the person and minimum distance between the specific image and the test image. The location is tracked so as to ensure precision. The results are used for matching, calculation of FAR, FRR and TSR. The model that has been proposed has been evaluated on the ORL database, JAFFE database, Indian Females database etc. The experimental results indicate that the systems proposed outperform the existing ones based on individual feature techniques and models employing multiple feature types.
{"title":"Spatial Domain Face Recognition System Using Convolution of PDV and LBP","authors":"Munikrishna D C, K. Raja, V. R.","doi":"10.1109/ICIIBMS.2018.8549957","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549957","url":null,"abstract":"Face recognition has become the new captivating field for scientists and researchers the world over. This paper, proposes an algorithm based on the convolution of the Pixel Difference Vector (PDV) and Local Binary Pattern (LBP) features. The features from the two techniques are convolved to generate a square matrix, which is then reshaped into a column vector. The column vectors of all the images that are present in the database are compared against the column vectors of the test image, making use of Euclidean Distance (ED). Following this, the location of the image in the database is obtained to detect the person and minimum distance between the specific image and the test image. The location is tracked so as to ensure precision. The results are used for matching, calculation of FAR, FRR and TSR. The model that has been proposed has been evaluated on the ORL database, JAFFE database, Indian Females database etc. The experimental results indicate that the systems proposed outperform the existing ones based on individual feature techniques and models employing multiple feature types.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125498910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8549968
Evi Septiana Pane, Alfi Zuhriya Khoirunnisaa, A. Wibawa, M. Purnomo
One of the typical gaming disorder is cybersickness. Cybersickness is the condition that occurs during or after exposed by the virtual environment. The increasing of cybersickness symptoms in gamers can lead to the poor health condition. Prior studies in investigating cybersickness employ subjective self-reports questionnaire, i.e., simulator sickness questionnaire (SSQ). However, the objective measurement is required to determine the actual condition of subjects due to cybersickness severity level. Therefore, this paper proposed identification of cybersickness severity level using electroencephalograph (EEG) signals. From the EEG, we extract the best feature such as percentage change (PC) of power percentage (PP) in beta and theta frequency band from pre- to post-stimulation. We found a specific pattern of cybersickness that marked by the sudden decreasing of $mathbf{PP}beta$ during the recording between baseline segment (4 minutes) and the last part (4 minutes) of game playing. Unlike previous studies, this paper proposed the rules-based algorithm i.e. CN2 Rules Induction for identifying cybersickness severity level. This giving ease for medical-expert to determine appropriate diagnosis and treatment towards patients. The classification yields the best accuracy of 88.9% using the CN2 rule induction. It is outperforming other classifiers accuracies such as decision tree (72.2%) and SVM (83.3 %). According to the results, incorporating PC of the $mathbf{PP}beta$ feature with the rules-based algorithm is working well for identifying cybersickness severity level from EEG.
{"title":"Identifying Severity Level of Cybersickness from EEG signals using CN2 Rule Induction Algorithm","authors":"Evi Septiana Pane, Alfi Zuhriya Khoirunnisaa, A. Wibawa, M. Purnomo","doi":"10.1109/ICIIBMS.2018.8549968","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549968","url":null,"abstract":"One of the typical gaming disorder is cybersickness. Cybersickness is the condition that occurs during or after exposed by the virtual environment. The increasing of cybersickness symptoms in gamers can lead to the poor health condition. Prior studies in investigating cybersickness employ subjective self-reports questionnaire, i.e., simulator sickness questionnaire (SSQ). However, the objective measurement is required to determine the actual condition of subjects due to cybersickness severity level. Therefore, this paper proposed identification of cybersickness severity level using electroencephalograph (EEG) signals. From the EEG, we extract the best feature such as percentage change (PC) of power percentage (PP) in beta and theta frequency band from pre- to post-stimulation. We found a specific pattern of cybersickness that marked by the sudden decreasing of $mathbf{PP}beta$ during the recording between baseline segment (4 minutes) and the last part (4 minutes) of game playing. Unlike previous studies, this paper proposed the rules-based algorithm i.e. CN2 Rules Induction for identifying cybersickness severity level. This giving ease for medical-expert to determine appropriate diagnosis and treatment towards patients. The classification yields the best accuracy of 88.9% using the CN2 rule induction. It is outperforming other classifiers accuracies such as decision tree (72.2%) and SVM (83.3 %). According to the results, incorporating PC of the $mathbf{PP}beta$ feature with the rules-based algorithm is working well for identifying cybersickness severity level from EEG.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120894047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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