2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)最新文献_第5页

Parallel Implementation of Chaos Neural Networks for an Embedded GPU 基于嵌入式GPU的混沌神经网络并行实现

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923383

Zhongda Liu, Takeshi Murakami, Satoshi Kawamura, H. Yoshida

引用次数: 5

Effective feature extraction from driving data for detection of danger awareness 从驾驶数据中有效提取特征以检测危险意识

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923343

Kotaro Nakano, B. Chakraborty

{"title":"Effective feature extraction from driving data for detection of danger awareness","authors":"Kotaro Nakano, B. Chakraborty","doi":"10.1109/ICAwST.2019.8923343","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923343","url":null,"abstract":"In recent years, the importance of driver’s support system is increasing as a solution for dealing with car related accidents. These driving support systems are equipped with functions for avoiding various hazards when the driver drives the vehicle, reducing the risk of causing an accident. In this research, we focus on the time series data of the driving behaviour of the driver, and based on these data, experiments aiming at development of the dangerous driving detection system due to cognitive distraction of the driver have been conducted. The driving behaviour data have been collected from driving simulator which contain driver’s actions mainly steering, accelerator and foot brake operations. It has been observed that the driving behaviour of each driver changes while driving in the state of distraction from while driving attentively and by analyzing these changes, the driver’s distraction from the normal state can be detected. The objective of this paper is to find the effective features for detection of distracted driving of specific driver in real time (specific short intervals). From the collected data of driving behaviour of multiple subjects, static feature based driving model and dynamic feature based driving model for individual drivers and all drivers for attentive driving and distracted driving have been developed. It can be shown from the results that distracted driving can be identified for individual in real time with stable accuracy using dynamic feature based models.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"1 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":"130161170","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

Misinformation Containment in OSNs leveraging Community Structure 利用社区结构的osn中的错误信息遏制

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923277

A. Ghoshal, Nabanita Das, Soham Das

引用次数: 7

Utilization of Oversampling for multiclass sentiment analysis on Amazon Review Dataset 利用过采样方法对亚马逊评论数据集进行多类情感分析

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923260

Anirban Mukherjee, S. Mukhopadhyay, P. Panigrahi, Saptarsi Goswami

引用次数: 14

A Study of Social Media Reviews Effects on the Success of Crowdfunding Projects 社交媒体评论对众筹项目成功的影响研究

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923411

Long-Sheng Chen, Ying-Jung Chuang

引用次数: 1

Method for left atrial appendage segmentation using heart CT images 利用心脏CT图像分割左心耳的方法

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923258

Itaru Takayashiki, A. Doi, Toru Kato, H. Takahashi, Shoto Sekimura, M. Hozawa, Y. Morino

引用次数: 1

A New Approach to Classify Knee Osteoarthritis Severity from Radiographic Images based on CNN-LSTM Method 基于CNN-LSTM方法的膝骨关节炎放射图像严重程度分类新方法

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923284

Rima Tri Wahyuningrum, L. Anifah, I. K. E. Purnama, M. Purnomo

{"title":"A New Approach to Classify Knee Osteoarthritis Severity from Radiographic Images based on CNN-LSTM Method","authors":"Rima Tri Wahyuningrum, L. Anifah, I. K. E. Purnama, M. Purnomo","doi":"10.1109/ICAwST.2019.8923284","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923284","url":null,"abstract":"This paper introduces a new approach to quantify knee osteoarthritis (OA) severity using radiographic (X-ray) images. Our new approach combines preprocessing, Convolutional Neural Network (CNN) as a feature extraction method, followed by Long Short-Term Memory (LSTM) as a classification method. Preprocessing is conducted by manually cropping on the knee joint with dimensions of 400 x 100 pixels. The public dataset used to evaluate our approach is the Osteoarthritis Initiative (OAI) with very promising results from the existing approach where this dataset has information about the KL grade assessment for both knees (right and left). OAI is a multicenter and prospective observational study of knee OA. The purpose of this dataset is to develop public domain research resources to facilitate scientific evaluation of biomarkers for OA as a potential replacement endpoint for disease development. We have experimented by using three-fold cross-validation, where the first 2/3 data becomes the training data, while the last 1/3 data work as the testing data. Those groups data are being rotated with no overlap. Obtained results demonstrate that the mean accuracy is 75.28 %, and the mean loss function using cross-entropy is 0.09. These results outperform the deep learning methods that have been implemented before.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"4 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":"125286001","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: 27

Non-reference Quality Assessment Model using Deep learning for Omnidirectional Images 基于深度学习的全向图像非参考质量评估模型

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923442

Tung Q. Truong, Huyen T. T. Tran, T. Thang

{"title":"Non-reference Quality Assessment Model using Deep learning for Omnidirectional Images","authors":"Tung Q. Truong, Huyen T. T. Tran, T. Thang","doi":"10.1109/ICAwST.2019.8923442","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923442","url":null,"abstract":"Image quality assessment (IQA) has been a popular research topic in image processing. However, most studies until now have been focusing on traditional images and only a few focused on omnidirectional images. Unlike in the case of traditional images, the users can only view a part of 360-degree images at a time, and thus tend to focus more on specific regions of the image. This makes predicting quality scores for omnidirectional images a challenging task since most existing models for traditional images usually treat all regions of the image equally. In this paper, we propose an omnidirectional image quality assessment model based on deep learning. This model focuses on learning the features of the middle region of input images. The model first automatically predicts the quality scores for patches sampled from the input image. The quality score of the image will then be calculated by weighted averaging of the patch quality scores based on their positions. Experimental results show that the proposed model provides very promising accuracy for predicting quality scores of omnidirectional images.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"204 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":"114660687","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: 10

Interactive Evolutionary Computation System Using Multiple Users’ Gaze Information Considering User’s Partial Evaluation Participation 考虑用户部分评价参与的多用户注视信息交互进化计算系统

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923513

H. Takenouchi, Masataka Tokumaru

{"title":"Interactive Evolutionary Computation System Using Multiple Users’ Gaze Information Considering User’s Partial Evaluation Participation","authors":"H. Takenouchi, Masataka Tokumaru","doi":"10.1109/ICAwST.2019.8923513","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923513","url":null,"abstract":"We investigate an interactive evolutionary computation using multiple users’ gaze information when users partially participate in each design evaluation. In the previous study, we confirmed the effectiveness of the proposed system from a viewpoint of real system operation. However, the fluctuation of the users during solution candidate evaluation was not considered. In the actual operation of the proposed system, users may change during the process due to user interchange. Therefore, in this study, we verify the effectiveness of the proposed system when varying the users participating in each evaluation for each generation. In the experiment, we employ two types of situations as assumed real environments. The first situation changes the number of users evaluating the designs at each generation. The second situation employs various users from the predefined population to evaluate the designs at each generation. The experimental results show that, despite the change in the number of users during the solution candidate evaluation, the proposed system can generate coordination to satisfy many users.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"104 34","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120827114","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

Sifting for Deeper Insights from Public Opinion: Towards Crowdsourcing and Big Data for Project Improvement 从民意中筛选更深入的见解:面向众包和大数据的项目改进

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923438

Jean Marie Tshimula, M. M. Njuguna, Thierry Roger Bayala, Mbuyi Mukendi Didier, Achraf Essemlali, Hugues Kanda, Numfor Solange Ayuni

引用次数: 1