2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)最新文献_第4页

Utilizing Subject-Specific Discriminative EEG Features for Classification of Motor Imagery Directions 利用被试特异性判别脑电特征进行运动意象方向分类

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923216

Kavitha P. Thomas, Neethu Robinson, A. P. Vinod

{"title":"Utilizing Subject-Specific Discriminative EEG Features for Classification of Motor Imagery Directions","authors":"Kavitha P. Thomas, Neethu Robinson, A. P. Vinod","doi":"10.1109/ICAwST.2019.8923216","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923216","url":null,"abstract":"Electroencephalogram (EEG)-based BrainComputer Interface (BCI) technology needs efficient algorithms to find distinct EEG patterns/features to realize applications with distinct high-dimensional control signals. This paper proposes a novel feature extraction methodology for separating EEG patterns associated right hand motor imagery performed towards left and right directions. The most discriminative subject-specific feature set is chosen based on Fisher’s ratio of absolute phase values of EEG in 6 low frequency sub bands. Using this, the proposed BCI system is capable of providing better classification results than state-ofthe-art methodology with fixed channels, fusing absolute phase and spatial features from selected subject-specific discriminative channels. Experimental analysis shows that though parietal lobe is vital in providing distinguishable features, the channel set that provide maximum accuracy, is highly subject-specific. Hence, subject-specific BCI that can decode finer parameters of imagined movement are feasible and further research to understand the activations elicited in parietal lobe can contribute towards robust BCI systems.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125132092","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

Improve the generalization of the cross-task emotion classifier using EEG based on feature selection and SVR 改进了基于特征选择和支持向量回归的脑电跨任务情绪分类器的泛化能力

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923256

Shuang Liu, Wenyi Wu, Siyu Zhai, Xiaoya Liu, Yufeng Ke, X. An, Dong Ming

{"title":"Improve the generalization of the cross-task emotion classifier using EEG based on feature selection and SVR","authors":"Shuang Liu, Wenyi Wu, Siyu Zhai, Xiaoya Liu, Yufeng Ke, X. An, Dong Ming","doi":"10.1109/ICAwST.2019.8923256","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923256","url":null,"abstract":"Emotion is a state that comprehensively represents human feeling, thought and behavior. In our daily life, emotion has played an increasingly important role, and emotion recognition has become a research focus. What’ more, the application has a broader perspective at home and abroad. Most existing studies identified emotion under specific tasks, but emotion classifiers are required to recognize emotion under any conditions in practice. Therefore, cross-task emotion recognition is a necessary step to move from the laboratory to the practical use. In this work, we designed three different induced tasks, picture-induced, music-induced and video-induced tasks. 13 (8 females and 5 males) participants were recruited and evoked to be positive, neutral and negative states respectively. The results using support vector regression highlighted that the correlation coefficient was higher for inter-task classification in video-induced and music-induced tasks, while deteriorated significantly in cross-task classification. Combining recursive feature screening and support vector regression to optimize features, the optimal feature set had better performance than all features employed, obtaining above 0.8 for correlation coefficient. These results indicated that SVR could achieve a better performance of cross-task emotion recognition, partly because it avoided the problem of emotion intensity mismatch in different tasks.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"328 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301418","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: 1

Performance Comparison of Machine Learning Algorithms to Predict Labor Complications and Birth Defects Based On Stress 基于压力预测分娩并发症和出生缺陷的机器学习算法性能比较

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923370

V. Madhusri, G. Kesavkrishna, R. Marimuthu, R. Sathyanarayanan

{"title":"Performance Comparison of Machine Learning Algorithms to Predict Labor Complications and Birth Defects Based On Stress","authors":"V. Madhusri, G. Kesavkrishna, R. Marimuthu, R. Sathyanarayanan","doi":"10.1109/ICAwST.2019.8923370","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923370","url":null,"abstract":"Stress affects physical as well as the mental health of the people and it follows that the stress is one of the major reasons behind the complications during pregnancy like hypertension. Hence it is necessary to ascertain the effects of stress on the health of the mother as well as the baby to find possible complications during pregnancy and delivery. It may also be useful to predict and avoid birth defects since there have been many instances where stress related complications have been known leading to cognitive disorders in the child. The goal of this study is to design and develop a prediction model for stress-based complications during pregnancy, based on the Physical, Social, Environmental and Biological factors. For this the dataset was generated using personalized interview-based survey administered to women who have undergone pregnancy and delivery in the past. The questions were based on the factors mentioned above. The data generated is used to check the correctness of the hypothesis and to evaluate the performance of the proposed stress prediction model using different machine learning algorithms like Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and Decision Tree (DT). The experimental results proved that the proposed model achieved an accuracy of 90% when Naive Bayes algorithm was used. The other algorithms produced lesser results but stillclose.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123705492","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: 1

Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis 基于深度模糊推理模型的知识获取及其在医学诊断中的应用

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923443

Y. Mori, Hirosato Seki, M. Inuiguchi

{"title":"Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis","authors":"Y. Mori, Hirosato Seki, M. Inuiguchi","doi":"10.1109/ICAwST.2019.8923443","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923443","url":null,"abstract":"In this paper, we reduce the number of fuzzy rules in the fuzzy inference model and acquire knowledge as fuzzy rules. The number of input items used for the inference model is reduced by randomly selecting the number of input items in each layer. Therefore, it turns out that the number of rules in the whole of this model can be reduced more than that of rules in an inference model that uses all the original input items at one time. However, in the previous model by Zhang, although the consequent part of the fuzzy rule was learned, the antecedent part was not learned. Since we need to deal with the situation where there is no prior knowledge in the problem to apply and it will be necessary to acquire knowledge from data, it is required to learn the antecedent part. In this paper, we propose a learning method for the antecedent fuzzy sets in fuzzy rules in order to obtain relationship between input and output of the learning data from the actual data. Then, as an example, the proposed method is applied to medical diagnosis of diabetes, the accuracy of the previous method is compared with that of the proposed method.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129767287","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

Implementation of multi-modal interface for VR application VR应用的多模态接口实现

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923551

Yushi Machidori, Ko Takayama, Kaoru Sugita

引用次数: 2

Deep Learning for Automatic Identification of Nodule Morphology Features and Prediction of Lung Cancer 基于深度学习的肺癌结节形态特征自动识别及预测

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923147

Weilun Wang, G. Chakraborty

{"title":"Deep Learning for Automatic Identification of Nodule Morphology Features and Prediction of Lung Cancer","authors":"Weilun Wang, G. Chakraborty","doi":"10.1109/ICAwST.2019.8923147","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923147","url":null,"abstract":"Lung Cancer is the most common and deadly cancer in the world. Correct prognosis affects the survival rate of patient. The most important symptom for early diagnosis is nodules images in CT scan. Diagnosis performed in hospital is divided into 2 steps : (1) Firstly, detect nodules from CT scan. (2) Secondly, evaluate the morphological features of nodules and give the diagnostic results.In this work, we proposed an automatic lung cancer prognosis system. The system has 3 steps : (1) In the first step, we trained two models, one based on convolutional neural network (CNN), and the other recurrent neural network (RNN), to detect nodules in CT scan. (2) In the second step, convolutional neural networks (CNN) are trained to evaluate the value of nine morphological features of nodules. (3) In the final step, logistic regression between values of features and cancer probability is trained using XGBoost model. In addition, we give an analysis of which features are important for cancer prediction. Overall, we achieved 82.39% accuracy for lung cancer prediction. By logistic regression analysis, we find that features of diameter, spiculation and lobulation are useful for reducing false positive.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132202834","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: 7

Imbalanced Twitter Sentiment Analysis using Minority Oversampling 使用少数派过采样的不平衡Twitter情绪分析

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923218

Kushankur Ghosh, Arghasree Banerjee, Sankhadeep Chatterjee, S. Sen

引用次数: 16

Equilibrium Selective Role Coordination for Autonomous Driving 自动驾驶的均衡选择角色协调

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923170

N. Iwahashi

{"title":"Equilibrium Selective Role Coordination for Autonomous Driving","authors":"N. Iwahashi","doi":"10.1109/ICAwST.2019.8923170","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923170","url":null,"abstract":"Role coordination is crucial in multi-agent collaboration because the collaboration may fail if the roles played by agents are inconsistent. In this paper, we present a role coordination method, Equilibrium Selective Role Coordination (ESRC), for decentralized continuous mutual action control in autonomous driving. In ESRC, the roles of agents are represented by game-theoretic equilibrium points that the agents try to achieve. ESRC comprises three hierarchical functions: (1) action due to given dynamics and constraints, (2) prediction of mutual actions, and (3) selection of roles. Corresponding to this functional hierarchy, three-layered mutual belief hierarchy is adopted. Each agent acts to achieve equilibrium with other agents while selecting an equilibrium point as an appropriate role assignment adaptively and online to reduce risk. The results of simulation experiments conducted demonstrate that our proposed method could produce appropriate actions even in complicated situations where several possible collisions needed to be considered. ESRC can be used to model a wide range of decentralized multi-agent based phenomena, such as human-robot physical interactions, dialogues, economic activities, artificial muscles, and neural information dynamics.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128205007","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: 0

Using Smart Insoles and RGB Camera for Identifying Stationary Human Targets 使用智能鞋垫和RGB相机识别静止的人体目标

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923255

Sevendi Eldrige Rifki Poluan, Yan-Ann Chen

引用次数: 1

Investigating Parallelization of Cross-language Plagiarism Detection System Using The Winnowing Algorithm in Cloud Based Implementation 基于云实现的跨语言剽窃检测系统并行化研究

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923539

A. A. P. Ratna, F. A. Ekadiyanto, Ihsan Ibrahim, Diyanatul Husna, Fathimah Rahimullah

引用次数: 4