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

Evaluation of Performance on LPWA Network Realizes for Multi-wavelength Cognitive V2X Wireless System 多波长认知V2X无线系统LPWA网络实现的性能评估

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923135

Akira Sakuraba, Y. Shibata, Toshihiro Tamura

{"title":"Evaluation of Performance on LPWA Network Realizes for Multi-wavelength Cognitive V2X Wireless System","authors":"Akira Sakuraba, Y. Shibata, Toshihiro Tamura","doi":"10.1109/ICAwST.2019.8923135","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923135","url":null,"abstract":"Vehicle-to-everything (V2X) communication is a base technology for realizing future mobility. By vehicle onboard sensors and their output data analysis, dangerous road areas can be identified such as icy road. V2X communication allows to exchange them among vehicles instantly. This paper introduces a cognitive V2X wireless system which exchanges road surface state information among running vehicles and roadside communication units (RSUs). This approach is based on the combination of multiple standard wireless links which have different characteristics. Our system has two different standards of wireless LAN links to deliver road state information by bulk messaging, and one long-range LPWA link in order to exchange network information and node location before the vehicle moves into wireless LAN range. We designed the V2X communication which is independent from public packet wireless network in order to provide highly availability in case of disaster, not only road surface information providing in normal time. We have measured network performance on LPWA link in field experiment, our proposed method and setup can deliver message for other node which is located at about 1,000 m distance.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"12 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":"127167365","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

Classification of Sleep Disorder from Single Lead Non-overlapping of ECG-apnea based Non-Linear Analysis using Ensemble Approach 基于集成非线性分析的单导联非重叠心电图呼吸暂停睡眠障碍分类

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923415

Iman Fahruzi, I. Purnama, H. Takahashi, M. Purnomo

{"title":"Classification of Sleep Disorder from Single Lead Non-overlapping of ECG-apnea based Non-Linear Analysis using Ensemble Approach","authors":"Iman Fahruzi, I. Purnama, H. Takahashi, M. Purnomo","doi":"10.1109/ICAwST.2019.8923415","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923415","url":null,"abstract":"The most significant determinant of quality of life is sleep quality, with better sleep resulting in a healthier and longer life. Polysomnography, or PSG, is a standardized system to get the medical records from multi-lead ECG recordings. However, PSG is a complicated, expensive and time-consuming procedure. Other alternatives include home sleep centre (HSC) development as a tool for early diagnosis and prevention of sleep disorders while keeping high accuracy. HSC uses low-cost equipment by utilizing single-lead ECG and accompanying applications. ECG is one of the media used in diagnosing and analysis of medical information related to sleep disorders. This study aims to develop a computerized sleep diagnosis application to help experts classify symptoms by investigation and evaluation of QRS morphological, time-frequency characteristics, and nonlinear analysis from single-lead ECG recordings. The classification of non-overlapping of ECG-apnea based non-linear analysis using an ensemble approach. The ensemble learning model approach, using the Boosted Tree test, yielded an accuracy of 94.7%, prediction speed of 120 obs/s and training time of 2.374 s. The QRS morphological characteristic and improved non-overlapping ECG recordings provided satisfactory diagnostic performance in sleep disorder classification for HSC usage.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"28 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":"125131162","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

Computer-Aided Diagnosis for Lung Lesion in Companion Animals from X-ray Images Using Deep Learning Techniques 基于深度学习技术的伴侣动物x射线图像肺部病变计算机辅助诊断

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923126

Pattaramanee Arsomngern, Nichakorn Numcharoenpinij, Jitpinun Piriyataravet, W. Teerapan, Woranich Hinthong, P. Phunchongharn

{"title":"Computer-Aided Diagnosis for Lung Lesion in Companion Animals from X-ray Images Using Deep Learning Techniques","authors":"Pattaramanee Arsomngern, Nichakorn Numcharoenpinij, Jitpinun Piriyataravet, W. Teerapan, Woranich Hinthong, P. Phunchongharn","doi":"10.1109/ICAwST.2019.8923126","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923126","url":null,"abstract":"X-ray radiography in animals has the difficulty of interpretation due to a variety of animals. This leads to image misinterpretation for a non-specialist veterinarian in some clinics that has no radiologist. Based on statistics of veterinary specialists in the US in 2018, the role of radiologist currently faces a shortage problem, especially in the fields of veterinary, which has only 4.2% from all of the other veterinarians. In this paper, we proposed an animal X-ray diagnosis application, namely Pet-X, focusing on the lung lesion problem which has difficulty in interpreting and need to be inspected in many respiratory and cardiovascular related cases. Pet-X automatically learns the sets of dogs and cats thoracic radiograph images, consisting of two positions which are in lateral and ventrodorsal position, pre-processes the images and generates the lung lesion diagnosis model using deep learning techniques (i.e., Convolutional neural networks). The diagnosis model is used to detect the possibility of abnormal lungs, and classify the abnormality in to any three lesion types of abnormal lungs (i.e., Alveolar, Interstitial and Bronchial). The proposed model could achieve a sensitivity 76%, specificity 83.3%, and accuracy 79.6% for lung lesion detection, and a sensitivity 81%, specificity 63.67%, and accuracy 72.3% for abnormal lung classification. Moreover, our application applied the class activation mapping technique to locate the abnormal regions in the images. Finally, Pet-X could assist the veterinarian and radiologist users to diagnose lung lesion in companion animals from X-ray images.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"30 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":"122953212","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: 8

Novel Features for Diagnosis of Parkinson’s Disease From off-Line Archimedean Spiral Images 从离线阿基米德螺旋图像诊断帕金森病的新特征

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923159

J. D. Gupta, B. Chanda

引用次数: 5

iCAST 2019 Author Index iCAST 2019作者索引

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/icawst.2019.8923374

引用次数: 0

A Study on Security Trend based on News Analysis 基于新闻分析的安全趋势研究

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923373

Chia-Mei Chen, Dan-Wei Wen, Jun-Jie Fang, G. Lai, Yi-Hung Liu

引用次数: 0

Hierarchical MQTT with Edge Computation 具有边缘计算的分层MQTT

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923340

H. Chien, Guo-Hao Qiu, Ruo-Wei Hung, An-Tong Shih, Chunhua Su

引用次数: 1

Detecting Anomalous Events on Distributed Systems Using Convolutional Neural Networks 基于卷积神经网络的分布式系统异常事件检测

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923357

Purimpat Cheansunan, P. Phunchongharn

{"title":"Detecting Anomalous Events on Distributed Systems Using Convolutional Neural Networks","authors":"Purimpat Cheansunan, P. Phunchongharn","doi":"10.1109/ICAwST.2019.8923357","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923357","url":null,"abstract":"Detection of anomalous events is very crucial for the maintenance and performance tuning in long-running distributed systems. System logs contain the complete information of system operation that can be used for describing the situations of the computing nodes. However, log messages are unstructured and difficult to utilize. In this work, we propose a novel anomaly detection framework in a Hadoop Distributed File System (HDFS) that transforms the log messages to structured data and automatically monitors the system operation logs using Convolutional Neural Networks (CNN). We evaluate the performance of anomaly detection in terms of precision, recall, and f-measure. The proposed framework can provide with precision = 94.76 ± 0.81%, recall = 99.53 ± 0.23%, and f-measure = 97.09 ± 0.49%. To apply the proposed framework in the practical application, we also concern about the training time and prediction productivity. From our experimental results, our proposed framework outperforms the existing models (i.e., LSTM and Bi-LSTM) with higher recall, lower training time, and higher prediction productivity.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"38 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":"130292468","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

Reversible Data Hiding Scheme Based on Difference Expansion Using Shiftable Block Strategy for Enhancing Image Fidelity 基于可移动块差分扩展的图像保真度增强可逆数据隐藏方案

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923138

Chin-Feng Lee, Jau-Ji Shen, Yi-Jhen Wu, Somya Agrawal

引用次数: 5

Binary Owl Search Algorithm for Feature Subset Selection 特征子集选择的二进制猫头鹰搜索算法

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923486

A. K. Mandal, Rikta Sen, B. Chakraborty

{"title":"Binary Owl Search Algorithm for Feature Subset Selection","authors":"A. K. Mandal, Rikta Sen, B. Chakraborty","doi":"10.1109/ICAwST.2019.8923486","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923486","url":null,"abstract":"Feature subset selection is one of the essential preprocessing tasks for numerous classification problems. This is because good feature subset can reduce overfitting of data, enhance the accuracy, and lessen the training time of a classifier model. However, finding the optimum feature subset is computationally expensive when the number of features is relatively high. Therefore, stochastic approaches are often used in attaining good feature subset within a feasible time frame. In this paper, we propose a binary variant of recent stochastic optimization algorithm Owl Search Optimization (OSA) for optimum feature subset selection. In this approach, six different transfer functions of S-shaped and V-shaped families were employed for generating six different binary Owl Search Optimization (BOSA) models. The proposed mechanism then employed on eleven publicly available datasets and performances were compared with popular approaches including, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Harmony Search (HC). Results reveal that, for most of the datasets, BOSA-based approaches can produce optimal feature subset with reduced number of features and improved classification accuracy compared to other approaches.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"19 3 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":"123645302","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