2019 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献_第10页

Real World Road Platoons and Negative Obstacles 真实世界的道路排和负面障碍

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00123

H. Hexmoor, K. Ahmed

引用次数: 0

ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network 基于卷积神经网络的二维图像分类心电信号分析

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00180

Muhammad Wasimuddin, K. Elleithy, Abdel-shakour Abuzneid, M. Faezipour, O. Abuzaghleh

{"title":"ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network","authors":"Muhammad Wasimuddin, K. Elleithy, Abdel-shakour Abuzneid, M. Faezipour, O. Abuzaghleh","doi":"10.1109/CSCI49370.2019.00180","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00180","url":null,"abstract":"Cardiovascular diseases, listed as the underlying cause of death, accounted for approximately 836,546 deaths in the United States in 2018. Statistics show that almost one of every three deaths in the US is a result of heart disease. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. This is while quick and immediate action at the onset of such heart conditions can save many lives. To this end, ample research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. However, more accurate and near real-time techniques are still under investigation. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier. The proposed method classifies the ECG signal as normal or abnormal by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 97.47%.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128508444","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

Classifying DNS Servers Based on Response Message Matrix Using Machine Learning 基于响应消息矩阵的机器学习分类DNS服务器

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00291

K. Shima, Ryo Nakamura, Kazuya Okada, Tomohiro Ishihara, Daisuke Miyamoto, Y. Sekiya

引用次数: 3

Risk Prediction of Critical Vital Signs for ICU Patients Using Recurrent Neural Network 应用循环神经网络预测ICU患者危重生命体征风险

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00191

Daniel Chang, David Chang, M. Pourhomayoun

{"title":"Risk Prediction of Critical Vital Signs for ICU Patients Using Recurrent Neural Network","authors":"Daniel Chang, David Chang, M. Pourhomayoun","doi":"10.1109/CSCI49370.2019.00191","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00191","url":null,"abstract":"Monitoring vital signs for Intensive Care Unit (ICU) patients is an absolute necessity to help assess the general physical health. In this research, we use machine learning to make a classification forecast that uses continuous ICU vital signs measurements to predict whether the vital signs of the next hour would reach the critical value or not. With the early warning, nurses and doctors can prevent emergency situations that may cause organ dysfunction or even death before it is too late. In this study, the data includes vital sign measurements, laboratory test results, procedures, medications collected from over 40,000 patients. After data preprocessing, bias data balancing, feature extraction, and feature selection, we have a clean dataset with informative and discriminating features. Then, various machine learning algorithms including Random Forest, XGBoost, Artificial Neural Networks (ANN), and LSTM were developed to predict critical vital signs of ICU patients 1-hour beforehand. We particularly developed predictive models to predict Heart Rate, Blood Oxygen Level (SpO2), Mean Arterial Pressure (MAP), Respiratory Rate (RR), Systolic Blood Pressure (SBP). The results demonstrated the accuracy of the developed methods.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114139542","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: 20

Multi-Agent Coordination Profiles through State Space Perturbations 状态空间扰动下的多智能体协调曲线

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00051

Derrik E. Asher, Michael Garber-Barron, Sebastian S. Rodriguez, Erin G. Zaroukian, Nicholas R. Waytowich

{"title":"Multi-Agent Coordination Profiles through State Space Perturbations","authors":"Derrik E. Asher, Michael Garber-Barron, Sebastian S. Rodriguez, Erin G. Zaroukian, Nicholas R. Waytowich","doi":"10.1109/CSCI49370.2019.00051","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00051","url":null,"abstract":"The current work utilized a multi-agent reinforcement learning (MARL) algorithm embedded in a continuous predator-prey pursuit simulation environment to measure and evaluate coordination between cooperating agents. In this simulation environment, it is generally assumed that successful performance for cooperative agents necessarily results in the emergence of coordination, but a clear quantitative demonstration of coordination in this environment still does not exist. The current work focuses on 1) detecting emergent coordination between cooperating agents in a multi-agent predator-prey simulation environment, and 2) showing coordination profiles between cooperating agents extracted from systematic state perturbations. This work introduces a method for detecting and comparing the typically 'black-box' behavioral solutions that result from emergent coordination in multi-agent learning spatial tasks with a shared goal. Comparing coordination profiles can provide insights into overlapping patterns that define how agents learn to interact in cooperative multi-agent environments. Similarly, this approach provides an avenue for measuring and training agents to coordinate with humans. In this way, the present work looks towards understanding and creating artificial team-mates that will strive to coordinate optimally.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114150350","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: 6

Smart DC Wall Outlet with Load Voltage Detection 智能直流墙壁插座与负载电压检测

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00140

Benjamin Tan, P. Granieri, T. Taufik

{"title":"Smart DC Wall Outlet with Load Voltage Detection","authors":"Benjamin Tan, P. Granieri, T. Taufik","doi":"10.1109/CSCI49370.2019.00140","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00140","url":null,"abstract":"A standard home in the United States has access to the 120V AC power for use with home appliances. However, many home electronics are powered by DC electricity. This introduces energy loss in the conversion process. A residential DC electrical system will avoid such conversion loss by storing energy in batteries and supplying it to DC appliances. Unfortunately, there is no existing voltage standards for DC appliances, which makes it challenging to power DC appliances straight from a DC wall outlet. The Smart DC Wall outlet addresses this by automatically adjusting its output voltage to meet any required DC load voltage. A solution involving low-voltage detection algorithm within a DC-DC converter is presented in this paper. The proposed solution monitors trends in the output current and sets the output voltage accordingly. Simulation tests resulted in identification of the required output voltage of five out of seven test devices. Results also indicate the possibility of generalizing the turn on characteristics of DC devices with more refined algorithm to improve successful voltage identification by the Smart Wall outlet.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114579767","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

Static Malware Analysis Using Machine Learning Algorithms on APT1 Dataset with String and PE Header Features 基于机器学习算法的APT1数据集字符串和PE头特征静态恶意软件分析

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00022

Neil Balram, G. Hsieh, Christian McFall

引用次数: 8

Sensitivity Analysis of Internal Erosion Models for Dam Safety 大坝安全内冲模型敏感性分析

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00089

M. Neilsen, Chendi Cao

引用次数: 1

Data Integrity in Policy-Based Attribute Access Control in Social Network Cloud 社交网络云中基于策略属性访问控制中的数据完整性

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00011

Katanosh Morovat, B. Panda

引用次数: 0

Digital Radio Mondiale App Development Digital Radio Mondiale应用程序开发

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00200

S. Jarng, Y. Kwon, Dongsoo Joseph Jarng

引用次数: 0