2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)最新文献

{"title":"Collaborative Filtering Algorithm Based on Trust and Information Entropy","authors":"Anqi Kang","doi":"10.1109/ICIIBMS.2018.8549962","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549962","url":null,"abstract":"In order to improve the accuracy of similarity, an improved collaborative filtering algorithm based on trust and information entropy is proposed in this paper. Firstly, the direct trust between the users is determined by the user's rating to explore the potential trust relationship of the users. The time decay function is introduced to realize the dynamic portrayal of the user's interest decays over time. Secondly, the direct trust and the indirect trust are combined to obtain the overall trust which is weighted with the Pearson similarity to obtain the trust similarity. Then, the information entropy theory is introduced to calculate the similarity based on weighted information entropy. At last, the trust similarity and the similarity based on weighted information entropy are weighted to obtain the similarity combing trust and information entropy which is used to predicted the rating of the target user and create the recommendation. The simulation shows that the improved algorithm has a higher accuracy of recommendation and can provide more accurate and reliable recommendation service.","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":"130708856","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}

{"title":"The Design of Diabetic Retinopathy Classifier Based on Parameter Optimization SVM","authors":"Jiangxue Han, Wenping Jiang, Cuixia Dai, Hongyan Ma","doi":"10.1109/ICIIBMS.2018.8549947","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549947","url":null,"abstract":"Diabetic retinopathy is a kind of disease which can seriously damage eyesight. Early diagnosis and regular treatment can effectively reduce visual deterioration. Artificial judgment of fundus images is time-consuming and easy to misdiagnose. Machine learning is an algorithm which automatically analyzes rules from data and uses rules to predict unknown data. Support Vector Machine (SVM) is one of the most important methods of machine learning. SVM is a classifier with learning ability. It is broadly applied to image recognition and image processing. Based on machine learning, a parametric optimized SVM classifier for diabetic retinopathy is proposed. Firstly, the classifier uses PCA and KPCA method to extract the prominent features of the image without artificial recognizing the features of the image, eliminates the specific feature extraction method, reduces the algorithm complexity, increases the generalization ability of the algorithm, and greatly improves the image processing speed. Secondly, grid search and genetic algorithm are used to optimize the parameters, avoid the problem of slow operation speed and low classification accuracy due to the large amount of data or the unsuitable selection of kernel parameters. Finally, a combinatorial optimization algorithm of KPCA and grid search is created. Meanwhile, the designed experiments verify that this combination optimization algorithm can make the classifier achieve the best classification state. The experimental results show that the classification accuracy of this combinatorial optimization algorithm reaches 98.33%, which can realize the automatic classification of diabetic retinopathy more accurately and rapidly.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"36 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":"125291732","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}

{"title":"Accuracy Verification of Knife Tip Position Estimation for Liver Surgery Support System","authors":"M. Koeda, Daiki Yano, Mayuko Doi, Katsuhiko Onishi, H. Noborio","doi":"10.1109/ICIIBMS.2018.8549997","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549997","url":null,"abstract":"Our surgical support system can issue a warning when the surgical knife approaches that should not be cut, e.g. a large blood vessel. It can also navigate the knife-tip to appropriately resect a tumor. Our system estimates the position and orientation of the surgical knife and the target organ using two different distance sensors during surgery. The distance between the knife-tip and the blood vessels inside the organ is measured. In this paper, we report the verification of accuracy of the knife-tip position estimation. The experimental results show that the position estimation error of the knife-tip is 0.3 mm and the standard deviation is 0.3 mm.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"19 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":"125361636","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}

{"title":"Fatigue Classification of Ocular Indicators using Support Vector Machine","authors":"M. A. Puspasari, H. Iridiastadi, I. Z. Sutalaksana, A. Sjafruddin","doi":"10.1109/ICIIBMS.2018.8549999","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549999","url":null,"abstract":"Fatigue is one of major causes of road accident. Time on task is one of factors that worsen fatigue, however, previous literatures limited their study into short simulated driving. Moreover, drivers in Indonesia frequently experience long duration driving caused by high traffic density. This study aims to determine fatigue classification based on ocular indicators in long duration driving condition. Classification of fatigue was conducted using Support Vector Machine (SVM). Twelve subjects participated in this study, and they were told to drive for three straight hours by driving simulator. Results showed improvements of blink duration, blink rate, PERCLOS, and microsleep by the end of three hours driving. Deterioration of saccadic velocity, saccadic amplitude, and pupil diameter were also occurred by the end of three hours driving. Results from Spearman rho suggest blink duration, PERCLOS, and microsleep as parameters that significantly correlated to KSS score. Radial basis function (RBF) was used as Kernel function since it has the highest accuracy compared to linear functions. SVM model indicated validity of seven ocular indicators as fatigue classification, with accuracy above 80%.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"129 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":"128859615","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}

{"title":"Modified Local Ternary Pattern Based Face Recognition Using SVM","authors":"Pattarakamon Rangsee, K. Raja, V. R.","doi":"10.1109/ICIIBMS.2018.8549952","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549952","url":null,"abstract":"Face recognition (FR) has drawn considerable interest and attention in the area of pattern recognition. FR is still a challenging task in real time applications even though they are a number of face recognition algorithms which are available and work in various constrained environment. The paper proposes a FR algorithm using Modified Local Ternary Pattern (MLTP) with multi class Support Vector Machine (SVM) classifier. The MLTP features of the face images are classified by an Error-Correcting Output Code (ECOC) multiclass model with SVM. The proposed method is tested on six standard face databases. The experimental results have been demonstrated that the performance of MLTP with SVM can achieve higher recognition accuracy compared to the conventional methods.","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":"134506352","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}

{"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}

{"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}

{"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}

{"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}

{"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}