2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)最新文献_第2页

Message from the Steering Committee Chairs 指导委员会主席致辞

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/ccem.2018.00006

Shou-Yu Lee, Siwei Zhou, Dongcheng Li, Xuelin Li

引用次数: 0

Evaluation of Performance of Cloud Based Neural Network Models on Arrhythmia Classification 基于云的神经网络模型在心律失常分类中的性能评价

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00013

Uttam R, Supreeth Arabi, A. Mantri, Surabhi Rakhecha

引用次数: 0

Failure Prediction Model for Predictive Maintenance 预测性维修故障预测模型

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00019

KamalaKanta Mishra, Sachin Kumar Manjhi

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引用次数: 7

A Comparative Study of Feature Selection Methods for Classification of Chest X-Ray Image as Normal or Abnormal Inside AWS ECS Cluster AWS ECS聚类胸片正常与异常分类特征选择方法的比较研究

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00011

Vaibhav Sanjay Lalka, Srinivasa Rao Kundeti, Vinod Kumar, V. J.

{"title":"A Comparative Study of Feature Selection Methods for Classification of Chest X-Ray Image as Normal or Abnormal Inside AWS ECS Cluster","authors":"Vaibhav Sanjay Lalka, Srinivasa Rao Kundeti, Vinod Kumar, V. J.","doi":"10.1109/CCEM.2018.00011","DOIUrl":"https://doi.org/10.1109/CCEM.2018.00011","url":null,"abstract":"Machine learning algorithms are used to discover complex nonlinear relationships in biomedical data. However, sophisticated learning models becomes computationally unfeasible when dimension of the data increases. One of the solution to overcome this problem is to use feature selection methods. Feature selection methods finds the optimal feature subset and the subset performance is evaluated using some evaluation criteria, these methods are categorized as Filter, Wrapper, Embedded and Hybrid approaches. Even though these methods reduces the dimension of the data, the execution time of training increases as the dataset size increases. And also nowadays the preferred place for storage of data is cloud. Thus, the first step before applying machine learning algorithms is to copy the data to our local machine. This might take lot of time, if the size of data is huge. So to overcome such problems, here we propose a pipeline that runs on the AWS cloud based distributed architecture capable of doing feature selection, training and classifying. Here, we define an evaluation criteria that measures the performance of feature subsets based on the classification accuracy and size of the feature subset. The experiments were carried out on two chest X-ray datasets (Shenzhen and NIH) clinically tested as normal or abnormal. We achieved the classification accuracy of 84.24% for Shenzhen dataset and 79.55% for NIH dataset for classifying the chest X-ray image as normal or abnormal reducing the feature subset size to more than 50% with hybrid approach of feature selection and using defined evaluation criteria.","PeriodicalId":156315,"journal":{"name":"2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125845263","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

Dynamic Prioritization and Execution of API Tests Based on Customer Usage Pattern for SaaS Applications 基于客户使用模式的SaaS应用程序API测试的动态优先级和执行

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00026

S. R. De Reanzi, Vinoth Rajiah, P. R. J. Thangiah

引用次数: 1

Bodhisattva - Rapid Deployment of AI on Containers 菩萨-在容器上快速部署AI

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00025

S. Rao, Pradyumna S, Subramaniam Kalambur, D. Sitaram

引用次数: 2

False Positive Analysis of Software Vulnerabilities Using Machine Learning 利用机器学习对软件漏洞进行误报分析

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00010

Sumanth Gowda, Divyesh L Prajapati, Ranjit Singh, Swanand S. Gadre

引用次数: 4

Energy Management of Cloud Data Center Using Neural Networks 基于神经网络的云数据中心能源管理

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00022

N. Uv, Kishore Kumar G Pillai

引用次数: 3

Orchestration Based Hybrid or Multi Clouds and Interoperability Standardization 基于混合或多云的业务流程和互操作性标准化

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00018

D. Sitaram, Sudheendra Harwalkar, Chetna Sureka, Harsh Garg, Manusarvathra Dinesh, M. Kejriwal, Shikhar Gupta, V. Kapoor

引用次数: 3