Pub Date : 2018-10-01DOI: 10.1109/ICIIBMS.2018.8550000
Y. Wan-jun, Zi Jing-Yan
In order to solve the shortcomings of existing building construction risk management methods in dealing with uncertainty, a construction risk management analysis method based on Bayesian network (BN) theory is proposed. Firstly, based on management experts decision-making methods, the main factors affecting construction risk management are determined, and the security risk Bayesian network topology is constructed. Then use Bayesian network forward reasoning to predict the probability of construction risk in different situations, and analyze the cause of risk by combining backward reasoning. Finally, a sensitivity analysis based on the mutual information index method was used to identify the sensitive risk factors. Combining with the historical data of domestic construction projects, this method is applied to the practice of construction project safety risk management. The results show that the construction risk probability is 3.36%; It is the biggest risk factor that the safety risk existing is not solved timely in construction.
{"title":"Research on Risk Management of Construction Safety based on Bayesian Network","authors":"Y. Wan-jun, Zi Jing-Yan","doi":"10.1109/ICIIBMS.2018.8550000","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8550000","url":null,"abstract":"In order to solve the shortcomings of existing building construction risk management methods in dealing with uncertainty, a construction risk management analysis method based on Bayesian network (BN) theory is proposed. Firstly, based on management experts decision-making methods, the main factors affecting construction risk management are determined, and the security risk Bayesian network topology is constructed. Then use Bayesian network forward reasoning to predict the probability of construction risk in different situations, and analyze the cause of risk by combining backward reasoning. Finally, a sensitivity analysis based on the mutual information index method was used to identify the sensitive risk factors. Combining with the historical data of domestic construction projects, this method is applied to the practice of construction project safety risk management. The results show that the construction risk probability is 3.36%; It is the biggest risk factor that the safety risk existing is not solved timely in construction.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"3 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":"130948862","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.2018.8549932
Mohd Saiful Hazam Majid, W. Khairunizam, A. Shahriman, I. Zunaidi, B. N. Sahyudi, M. Zuradzman
Rehabilitation is important treatment for post stroke patient to regain their muscle strength and motor coordination as well as to retrain their nervous system. Electromyography (EMG) has been used by researcher to enhance conventional rehabilitation method as a tool to monitor muscle electrical activity however EMG signal is very stochastic in nature and contains some noise. Special technique is yet to be researched in processing EMG signal to make it useful and effective both to researcher and to patient in general. Feature extraction is among the signal processing technique involved and the best method for specific EMG study needs to be applied. In this works, nine feature extractions techniques are applied to EMG signals recorder from subjects performing upper limb rehabilitation activity based on suggested movement sequence pattern. Three healthy subjects perform the experiment with three trials each and EMG data were recorded from their bicep and deltoid muscle. The applied features for every trials of each subject were analyzed statistically using student T-Test their significant of p-value. The results were then totaled up and compared between the nine features applied and Auto Regressive coefficient (AR) present the best result and consistent with each subjects' data. This feature will be used later in our future research work of Upper-limb Virtual Reality Rehabilitation.
{"title":"EMG Feature Extractions for Upper-Limb Functional Movement During Rehabilitation","authors":"Mohd Saiful Hazam Majid, W. Khairunizam, A. Shahriman, I. Zunaidi, B. N. Sahyudi, M. Zuradzman","doi":"10.1109/ICIIBMS.2018.8549932","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549932","url":null,"abstract":"Rehabilitation is important treatment for post stroke patient to regain their muscle strength and motor coordination as well as to retrain their nervous system. Electromyography (EMG) has been used by researcher to enhance conventional rehabilitation method as a tool to monitor muscle electrical activity however EMG signal is very stochastic in nature and contains some noise. Special technique is yet to be researched in processing EMG signal to make it useful and effective both to researcher and to patient in general. Feature extraction is among the signal processing technique involved and the best method for specific EMG study needs to be applied. In this works, nine feature extractions techniques are applied to EMG signals recorder from subjects performing upper limb rehabilitation activity based on suggested movement sequence pattern. Three healthy subjects perform the experiment with three trials each and EMG data were recorded from their bicep and deltoid muscle. The applied features for every trials of each subject were analyzed statistically using student T-Test their significant of p-value. The results were then totaled up and compared between the nine features applied and Auto Regressive coefficient (AR) present the best result and consistent with each subjects' data. This feature will be used later in our future research work of Upper-limb Virtual Reality Rehabilitation.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"3 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":"130956703","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.2018.8549949
Sumin Jin, Yungcheo l Byun, Sangyong Byun
Applications and services using brain waves have high possibilities in the near future. Especially, deep learning for pattern recognition is highly applicable in the area. In this research, we propose a method to recognize human behaviors using human bio-signal, that is, brain waves. EEG brain wave data is collected using a headset device and is used for training and testing CNN and LSTM which are considered as successful deep neural networks nowadays. From the experiment, we could get positive recognition rates and applicability for various kinds of applications using our proposed methods.
{"title":"Analysis of Brain Waves for Detecting Behaviors","authors":"Sumin Jin, Yungcheo l Byun, Sangyong Byun","doi":"10.1109/ICIIBMS.2018.8549949","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549949","url":null,"abstract":"Applications and services using brain waves have high possibilities in the near future. Especially, deep learning for pattern recognition is highly applicable in the area. In this research, we propose a method to recognize human behaviors using human bio-signal, that is, brain waves. EEG brain wave data is collected using a headset device and is used for training and testing CNN and LSTM which are considered as successful deep neural networks nowadays. From the experiment, we could get positive recognition rates and applicability for various kinds of applications using our proposed methods.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"38 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":"131236829","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.2018.8550005
Md. Tohidul Islam, B.M. Nafiz Karim Siddique, S. Rahman, T. Jabid
In our paper we tried to classify food images using convolutional neural network. Convolutional neural network extracts spatial features from images so it is very efficient to use convolutional neural network for image clasification problem. Recently people are sharing food images in social media and writing review on food. So there is a lot of food image in the social media but some image may not be labeled. It will be very helpful for restaurants if they can advertise their food to those people who is looking similar kind of foods they offer. Food classification system can help social media platform to identify food. Food classification system can enable an opportunity for social media platform to offer advertisement service for restaurants and beverage companies to their targeted users. It will be financially beneficial for both social media platform and beverage companies. Food classification is very difficult task because there is high variance in same category of food images. We developed a convolutional neural network model to classify food images in food-11 dataset. We also used a pre-trained Inception V3 convolutional neural network model to classify food images.
{"title":"Food Image Classification with Convolutional Neural Network","authors":"Md. Tohidul Islam, B.M. Nafiz Karim Siddique, S. Rahman, T. Jabid","doi":"10.1109/ICIIBMS.2018.8550005","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8550005","url":null,"abstract":"In our paper we tried to classify food images using convolutional neural network. Convolutional neural network extracts spatial features from images so it is very efficient to use convolutional neural network for image clasification problem. Recently people are sharing food images in social media and writing review on food. So there is a lot of food image in the social media but some image may not be labeled. It will be very helpful for restaurants if they can advertise their food to those people who is looking similar kind of foods they offer. Food classification system can help social media platform to identify food. Food classification system can enable an opportunity for social media platform to offer advertisement service for restaurants and beverage companies to their targeted users. It will be financially beneficial for both social media platform and beverage companies. Food classification is very difficult task because there is high variance in same category of food images. We developed a convolutional neural network model to classify food images in food-11 dataset. We also used a pre-trained Inception V3 convolutional neural network model to classify food images.","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":"133819931","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.2018.8549953
Yang Zheng, Hong Fu, Bin Li, W. Lo, Bin Li
Strabismus is a common vision disorder that affects around 4% of the population, bringing about unpleasant influences on people's health and quality of life. The cover test is one of the exams for detecting this pathology, which is considered as the golden standard method. However, the subjectivity of the ophthalmologist conducting the cover test could lead to uncertainties and limitations to the result of strabismus assessment. Nowadays computer-aid methods have been used to assist ophthalmological diagnosis and therapy, whereas the development and use of the high-tech is not a general reality within the sub-specialty of strabismus. In this study, an automatic stimulus module controlled by the micro-control-unit is used to generate the cover action of the occluder and the imaging devices are used to simultaneously monitor and record the movement of the eyes. With the proposed system and algorithm, the presence and type of strabismus can be generated automatically, which makes the diagnosis of strabismus objective, automatic and highly efficient.
{"title":"An Automatic Stimulus and Synchronous Tracking System for Strabismus Assessment based on Cover Test","authors":"Yang Zheng, Hong Fu, Bin Li, W. Lo, Bin Li","doi":"10.1109/ICIIBMS.2018.8549953","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549953","url":null,"abstract":"Strabismus is a common vision disorder that affects around 4% of the population, bringing about unpleasant influences on people's health and quality of life. The cover test is one of the exams for detecting this pathology, which is considered as the golden standard method. However, the subjectivity of the ophthalmologist conducting the cover test could lead to uncertainties and limitations to the result of strabismus assessment. Nowadays computer-aid methods have been used to assist ophthalmological diagnosis and therapy, whereas the development and use of the high-tech is not a general reality within the sub-specialty of strabismus. In this study, an automatic stimulus module controlled by the micro-control-unit is used to generate the cover action of the occluder and the imaging devices are used to simultaneously monitor and record the movement of the eyes. With the proposed system and algorithm, the presence and type of strabismus can be generated automatically, which makes the diagnosis of strabismus objective, automatic and highly efficient.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"16 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":"133333563","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.2018.8550015
Kah Keng Wong, A. Banham
Huntingtin-interacting protein 1 (HIP1R) is an endocytic protein involved in endocytosis of surface receptors by regulating actin polymerization. We have previously shown that HIP1R was expressed in lymphoid B cells and diffuse large B-cell lymphoma (DLBCL) associated with better survival. Herein, we examined the expression profile of HIP1R in different immune cell populations and its potential functions in DLBCL. By utilizing a validated anti-HIP1R monoclonal antibody (clone 44), we examined whether the following immune cells in human reactive tonsils expressed HIP1R through double immunostaining: T cells (CD3+), macrophages (CD68+), mantle zone (MZ) B cells (PAX5+), germinal centre (GC) B cells (BCL6+) and plasma cells (IRF4/MUM1+). HIP1R was strongly expressed in PAX5+ MZ B cells, moderately expressed in BCL6+ GC B cells, but absent in CD3+ T cells, CD68+ macrophages and IRF4/MUM1+ plasma cells. In particular, we observed that HIP1R was absent in IRF4/MUM1+ plasma cells residing within the GC or non-GC interfollicular regions, suggesting that IRF4/MUM1 might downregulate HIP1R expression in activated B cells. We have previously shown that HIP1R expression is directly suppressed by the transcription factor FOXP1 in activated B-cell-like diffuse large B-cell lymphoma (ABC-DLBCL) cells, however FOXP1 is absent in normal plasma cells, suggesting the presence of other regulators. Our previous immunostaining results in a series of DLBCL patient cases (n=155) showed a significant inverse correlation between HIP1R and IRF4/MUM1 (Pearson r = −0.495; p < 0.001). Indeed, knockdown of IRF4/MUM1 expression in the ABC-DLBCL cell line OCI-LY3 by two independent IRF4 siRNA constructs increased HIP1R expression at both transcript and protein levels. In terms of functional relevance, the bioinformatics approach Gene Set Enrichment Analysis (GSEA) was adopted to examine gene sets positively-associated with HIP1R transcript expression profile in three independent gene expression profiling (GEP) datasets of DLBCL patient cases derived from Gene Expression Omnibus database i.e. GSE10846 (n=233), GSE23501 (n=63), and GSE19246 (n=59). Our GSEA results showed that the gene set �Rho GTPase Activator Activity� (GO ID:0005100) was significantly positively-associated with HIP1R expression profile across all three GEP datasets GSE10846 (p = 0.0016), GSE23501 (p < 0.0001) and GSE19246 (p = 0.0167). These results suggest that HIP1R is involved in the activation of Rho GTPase signaling pathway, which has been documented to inhibit migration of DLBCL cells, and HIP1R expression is suppressed by transcription factors involved in B-cell activation including FOXP1 and IRF4/MUM1.
{"title":"Expression profile of HIP1R in B-cell subsets and in silico prediction of its functions in diffuse large B-cell lymphoma","authors":"Kah Keng Wong, A. Banham","doi":"10.1109/ICIIBMS.2018.8550015","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8550015","url":null,"abstract":"Huntingtin-interacting protein 1 (HIP1R) is an endocytic protein involved in endocytosis of surface receptors by regulating actin polymerization. We have previously shown that HIP1R was expressed in lymphoid B cells and diffuse large B-cell lymphoma (DLBCL) associated with better survival. Herein, we examined the expression profile of HIP1R in different immune cell populations and its potential functions in DLBCL. By utilizing a validated anti-HIP1R monoclonal antibody (clone 44), we examined whether the following immune cells in human reactive tonsils expressed HIP1R through double immunostaining: T cells (CD3+), macrophages (CD68+), mantle zone (MZ) B cells (PAX5+), germinal centre (GC) B cells (BCL6+) and plasma cells (IRF4/MUM1+). HIP1R was strongly expressed in PAX5+ MZ B cells, moderately expressed in BCL6+ GC B cells, but absent in CD3+ T cells, CD68+ macrophages and IRF4/MUM1+ plasma cells. In particular, we observed that HIP1R was absent in IRF4/MUM1+ plasma cells residing within the GC or non-GC interfollicular regions, suggesting that IRF4/MUM1 might downregulate HIP1R expression in activated B cells. We have previously shown that HIP1R expression is directly suppressed by the transcription factor FOXP1 in activated B-cell-like diffuse large B-cell lymphoma (ABC-DLBCL) cells, however FOXP1 is absent in normal plasma cells, suggesting the presence of other regulators. Our previous immunostaining results in a series of DLBCL patient cases (n=155) showed a significant inverse correlation between HIP1R and IRF4/MUM1 (Pearson r = −0.495; p < 0.001). Indeed, knockdown of IRF4/MUM1 expression in the ABC-DLBCL cell line OCI-LY3 by two independent IRF4 siRNA constructs increased HIP1R expression at both transcript and protein levels. In terms of functional relevance, the bioinformatics approach Gene Set Enrichment Analysis (GSEA) was adopted to examine gene sets positively-associated with HIP1R transcript expression profile in three independent gene expression profiling (GEP) datasets of DLBCL patient cases derived from Gene Expression Omnibus database i.e. GSE10846 (n=233), GSE23501 (n=63), and GSE19246 (n=59). Our GSEA results showed that the gene set �Rho GTPase Activator Activity� (GO ID:0005100) was significantly positively-associated with HIP1R expression profile across all three GEP datasets GSE10846 (p = 0.0016), GSE23501 (p < 0.0001) and GSE19246 (p = 0.0167). These results suggest that HIP1R is involved in the activation of Rho GTPase signaling pathway, which has been documented to inhibit migration of DLBCL cells, and HIP1R expression is suppressed by transcription factors involved in B-cell activation including FOXP1 and IRF4/MUM1.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"15 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":"124471567","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.2018.8549981
Divya A, K. Raja, V. R.
The field of Face Recognition (FR) is still a thought-provoking problem, while in recent advances of Artificial Neural Networks (ANN) has shown improved performance in FR rate. In this paper, we propose face recognition based on windowing technique using Discrete Cosine Transform (DCT), average covariance and ANN. The novel concept of windowing technique is used to divide each image to $mathbf{4x4},mathbf{8X8}$ and $mathbf{16X16}$ size of windows. The DCT is applied on each window to obtain DCT co-efficients. The covariance matrix is computed on each DCT coefficient matrix and average value of each block is also computed to obtain final feature value. The computation of an average covariance reduces the original size of face image by around 97% i.e., the number of co-efficients in the final feature set is only around 3% of the original size of an image. The proposed method is very efficient in identifying with very less number of features. Network is created and trained the input dataset and target dataset to reach the desired output. The trained net is then tested to compute performance parameters of the network. The experiments are conducted on some popularly used face databases to illuminate the performance and the efficiency of the proposed algorithm. The experimental results are tabulated and are compared with the existing methods. It is observed that, the proposed model achieves better recognition accuracy for $mathbf{16X16}$ windowing and also with existing algorithms.
{"title":"Face Recognition Based on Windowing Technique Using DCT, Average Covariance and Artificial Neural Network","authors":"Divya A, K. Raja, V. R.","doi":"10.1109/ICIIBMS.2018.8549981","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549981","url":null,"abstract":"The field of Face Recognition (FR) is still a thought-provoking problem, while in recent advances of Artificial Neural Networks (ANN) has shown improved performance in FR rate. In this paper, we propose face recognition based on windowing technique using Discrete Cosine Transform (DCT), average covariance and ANN. The novel concept of windowing technique is used to divide each image to $mathbf{4x4},mathbf{8X8}$ and $mathbf{16X16}$ size of windows. The DCT is applied on each window to obtain DCT co-efficients. The covariance matrix is computed on each DCT coefficient matrix and average value of each block is also computed to obtain final feature value. The computation of an average covariance reduces the original size of face image by around 97% i.e., the number of co-efficients in the final feature set is only around 3% of the original size of an image. The proposed method is very efficient in identifying with very less number of features. Network is created and trained the input dataset and target dataset to reach the desired output. The trained net is then tested to compute performance parameters of the network. The experiments are conducted on some popularly used face databases to illuminate the performance and the efficiency of the proposed algorithm. The experimental results are tabulated and are compared with the existing methods. It is observed that, the proposed model achieves better recognition accuracy for $mathbf{16X16}$ windowing and also with existing algorithms.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"37 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":"129313240","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.2018.8549955
Mbaitiga Zacharie, Satoshi Fuji, Shimoji Minori
The development and impact of technology on our everyday lives cannot be compared with the world our ancestors lived in several decades ago. This is described as the world of technology (WoT). But despite all the advancements in technologies, understanding of the mechanisms of nature and the damages caused via natural disasters, such as earthquakes, landslides, and flooding to mention only a few, are still very far away. In the effort of saving lives during natural disasters, such as earthquakes, this study introduces a rapid human body detection using image processing from UAV camera. The skin color from a female student is first extracted in RGB then converted to HSV. Next, opening and closing morphological operations are performed eight times each to remove all noise present in the image. Experimental tests were performed both indoor and outdoor, where the female student presented an object close and far to the camera to check the detection capability in both cases. The experiment results show that close or far, the camera can clearly detect both a human body and any part of a human body. The results of the experiment proves the merit of the proposed method.
{"title":"Rapid Human Body Detection in Disaster Sites Using Image Processing from Unmanned Aerial Vehicle (UAV) Cameras","authors":"Mbaitiga Zacharie, Satoshi Fuji, Shimoji Minori","doi":"10.1109/ICIIBMS.2018.8549955","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549955","url":null,"abstract":"The development and impact of technology on our everyday lives cannot be compared with the world our ancestors lived in several decades ago. This is described as the world of technology (WoT). But despite all the advancements in technologies, understanding of the mechanisms of nature and the damages caused via natural disasters, such as earthquakes, landslides, and flooding to mention only a few, are still very far away. In the effort of saving lives during natural disasters, such as earthquakes, this study introduces a rapid human body detection using image processing from UAV camera. The skin color from a female student is first extracted in RGB then converted to HSV. Next, opening and closing morphological operations are performed eight times each to remove all noise present in the image. Experimental tests were performed both indoor and outdoor, where the female student presented an object close and far to the camera to check the detection capability in both cases. The experiment results show that close or far, the camera can clearly detect both a human body and any part of a human body. The results of the experiment proves the merit of the proposed method.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"69 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":"128359057","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.2018.8549960
Kyoungho Son, Yungcheo l Byun, Sang-Joon Lee
With the advance of machine learning and deep learning, lots of applications have been implemented so far. Prediction is one of them, which has been drawing lots of interests from researchers. In this paper, we implemented the method to predict visitors in a certain tourism place using machine learning. From our experiments, we could get some positive results showing its applicability in a real environment.
{"title":"Prediction of Visitors using Machine Learning","authors":"Kyoungho Son, Yungcheo l Byun, Sang-Joon Lee","doi":"10.1109/ICIIBMS.2018.8549960","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549960","url":null,"abstract":"With the advance of machine learning and deep learning, lots of applications have been implemented so far. Prediction is one of them, which has been drawing lots of interests from researchers. In this paper, we implemented the method to predict visitors in a certain tourism place using machine learning. From our experiments, we could get some positive results showing its applicability in a real environment.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"34 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133086709","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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