2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)最新文献_第2页

Multi-label classification of brain tumor mass spectrometry data In pursuit of tumor boundary detection method 多标签分类脑肿瘤质谱数据追求肿瘤边界检测方法

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279736

A. Sorokin, E. Zhvansky, K. Bocharov, I. Popov, Dmitry Zubtsov, A. Vorobiev, E. Nikolaev, V. Shurkhay, A. Potapov

引用次数: 6

Using mobile application as an instrument for ptosis diagnosis 使用手机应用程序作为上睑下垂诊断的工具

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279739

W. Kimpan, Pongpicha Sirivimonsattaya

引用次数: 0

Verification experiment for drone charging station using RTK-GPS RTK-GPS无人机充电站验证实验

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279762

Suriyon Tansuriyong, Motoki Kyan, Kaito Numata, Shuuya Taira, T. Anezaki

引用次数: 4

High-voltage driving circuit with on-chip ESD protection in CMOS technology 采用CMOS技术的片上ESD保护的高压驱动电路

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279758

Chun-Yu Lin, Yan-Lian Chiu

引用次数: 1

Experimental evaluation of an assist chair for sit-to-stand on speed of flipping up a seat of chair 一种坐立辅助椅对翻转椅座速度的实验评价

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279743

N. T. T. Suzuki, A. Suda, H. Onishi, T. Muromaki, T. Watanabe

{"title":"Experimental evaluation of an assist chair for sit-to-stand on speed of flipping up a seat of chair","authors":"N. T. T. Suzuki, A. Suda, H. Onishi, T. Muromaki, T. Watanabe","doi":"10.1109/ICIIBMS.2017.8279743","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279743","url":null,"abstract":"Assist chairs with simple mechanism to flip up a seat at the front edge to assist sit-to-stand (STS) is well known as commercial products developed from around 1980. The speed of flipping up the seat on the assist chairs would play a key role how to adjust assisting STS in individual cases, but there is almost no discussion how the speed of flipping up the seat affects the benefit of assisting STS. The aim of study is to investigate the optimised speed of flipping up the seat of the assist chairs. As a first step, a motorised assist chair with controllable speed of flipping up the seat was developed and tested with four healthy participants in three seat height conditions; High:520mm, Middle:420mm, and Low:320mm without and with flipping up the seat at maximum speed 30degree/second. Significant main effect of assisting was found on peak vGRF at Low seat height (p<0.005) by Wilcoxon rank test. There was no significant effect at Middle (p=0.126) and High seat height (p=0.507). With assisting at the Low seat height, the reduction of peak vGRF means that the flipping up the seat successfully supports trunk movement and knee function to lift up the hip from the seat. All participants felt the assisting was effective to reduce the hardness of STS at the Low seat height. With further studies to optimise the speed of flipping the seat, the motorised assist chair in this study would provide a proper assist for individual STS movement, with leaving certain part of physical load for keeping muscle functioning.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134332395","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}

引用次数: 2

Real-time object classification for autonomous vehicle using LIDAR 基于激光雷达的自动驾驶车辆实时目标分类

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279696

Masaru Yoshioka, N. Suganuma, Keisuke Yoneda, Mohammad Aldibaja

引用次数: 27

FF OCT with a swept source integrating a SLD and an AOTF 带扫描源的FF OCT集成了SLD和atf

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279689

Ting-Wei Chang, Hung-Chih Chiang, Chir-Weei Chang, Chy-Lin Wang, Yuan-Chin Lee

引用次数: 0

Optimal window lengths, features and subsets thereof for freezing of gait classification 冻结步态分类的最佳窗口长度、特征及其子集

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279699

V. Mikos, C. Heng, A. Tay, N. S. Chia, K. Koh, D. Tan, W. Au

{"title":"Optimal window lengths, features and subsets thereof for freezing of gait classification","authors":"V. Mikos, C. Heng, A. Tay, N. S. Chia, K. Koh, D. Tan, W. Au","doi":"10.1109/ICIIBMS.2017.8279699","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279699","url":null,"abstract":"Freezing of gait (FoG) is a common gait impairment in Parkinson's disease that puts patients at risk of falls and deteriorates their quality of life. Relief is sought after by evaluating the possibility of wearable systems that detect FoG in real-time and provide gait-reinforcing biofeedback cues. The successful detection relies on the extraction of high quality features, which have to be computed from recent samples of an inertial measurement unit in order to ensure real-time applicability. Unfortunately, the amount of samples considered for a feature's computation, i.e. the data window length, has been subjected to widespread disagreement: With no thorough analysis available, employed window lengths differed by several seconds among implementations. We derive optimal window lengths for a broad number of features used throughout literature by using mutual information as an evaluation metric, and elaborate on a window length's significance in affecting classification performance. With conventional feature selection methods, feature subsets tailored to various machine learning algorithms are established. Relying on these feature subsets for FoG classification, whereby all features are extracted with optimal window lengths, F1-scores increase up to 17.1% for individual classifiers and up to 12.7% on average when compared to previously proposed feature sets that are extracted with sub-optimal window lengths.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114716633","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}

引用次数: 4

Primary screening of diabetic retinopathy based on integrating morphological operation and support vector machine 基于形态学操作和支持向量机的糖尿病视网膜病变初步筛查

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279750

Syna Sreng, Noppadol Maneerat, D. Isarakorn, K. Hamamoto, Ronakorn Panjaphongse

{"title":"Primary screening of diabetic retinopathy based on integrating morphological operation and support vector machine","authors":"Syna Sreng, Noppadol Maneerat, D. Isarakorn, K. Hamamoto, Ronakorn Panjaphongse","doi":"10.1109/ICIIBMS.2017.8279750","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279750","url":null,"abstract":"Diabetic retinopathy is one of the most frequent causes of blindness due to diabetes. Primary screening is essential due to prerequisite step toward the diagnosis of diabetic retinopathy in order to prevent vision loss or blindness. This paper presents the methods to discriminate between healthy images and diabetic retinopathy images on the retinal images. The proposed method involves three main steps. Initially, the image is preprocessed to remove small noises and enhance the contrast of the image. Secondly, Kirsch edge detection is utilized to detect the bright lesions. Subsequently, the red lesions are detected depending on top-hat morphological filtering methods. Then the bright and dark lesions are combined by using logical AND operator. In order to be left only pathological signs, the noises near the vicinity of the optic disc and blood vessels are further removed using blob analysis. Finally, morphological features are extracted and fed to the SVM classifier. The proposed method was evaluated with three datasets containing 229 images. It achieved the accuracy of 90%, sensitivity of 86.33% and specificity of 98.55% with the average computational time 8 seconds per image. The method is simple and fast, easy to implement and the result is promising.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115749277","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}

引用次数: 5

A hybrid of data mining and ensemble learning forecasting for recurrent ovarian cancer 基于数据挖掘和集成学习的复发性卵巢癌预测

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI: 10.1109/ICIIBMS.2017.8279723

Y. Lu, Chi-Jie Lu, Chi-Chang Chang, Yu-Wen Lin

{"title":"A hybrid of data mining and ensemble learning forecasting for recurrent ovarian cancer","authors":"Y. Lu, Chi-Jie Lu, Chi-Chang Chang, Yu-Wen Lin","doi":"10.1109/ICIIBMS.2017.8279723","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279723","url":null,"abstract":"This study applied advanced machine learning techniques and combined with ensemble learning, widely considered as the most successful method to produce objective to an inferential problem of recurrent ovarian cancer. In this study, five machine learning approaches including SVM(support vector machine), C5.0, ELM(extreme learning machine), MARS(Multivariate Adaptive Regression Splines) and RF(Random Forests) were considered to find important risk factors and to predict the recurrence-proneness for ovarian cancer. We use ensemble learning to improve the defect of classification accuracy used normal machine learning: first, selecting important risk factors by ensemble learning, then, using the five machine learning approaches to analyze again. The medical records and pathology were accessible by the Chung Shan Medical University Hospital Tumor Registry. The existing literature on recurrent ovarian cancer reveals that factors include Age, Histology, Grade, Pathologic T, Pathologic N, Pathologic M, Pathologic Stage, The International Federation of Gynecology and Obstetrics (FIGO), Surgical Margins, Performance status, CA125, Operation Optimal Debulking, Chemotherapy Guideline. There are totally 987 patients in the data set. In our study, C5.0 is the superior approach in predicting recurrence of ovarian cancer. Moreover, the classification accuracy of C5.0, MARS, RF and SVM indeed increases after using ensemble learning. Particularly the classification accuracy of C5.0 obviously improves by using ensemble learning with hybrid scheme.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126558523","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}

引用次数: 3