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

Pronunciation Training on Isolated Kannada Words Using "Kannada Kali" - A Cloud Based Smart Phone Application 使用基于云的智能手机应用程序“Kannada Kali”对孤立的卡纳达语单词进行发音训练

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00017

Savitha Murthy, Ankit Anand, Avinash Kumar, Ajaykumar S. Cholin, Ankita Shetty, Aditya D. Bhat, Akshay Venkatesh, Lingaraj Kothiwale, D. Sitaram, Viraj Kumar

{"title":"Pronunciation Training on Isolated Kannada Words Using \"Kannada Kali\" - A Cloud Based Smart Phone Application","authors":"Savitha Murthy, Ankit Anand, Avinash Kumar, Ajaykumar S. Cholin, Ankita Shetty, Aditya D. Bhat, Akshay Venkatesh, Lingaraj Kothiwale, D. Sitaram, Viraj Kumar","doi":"10.1109/CCEM.2018.00017","DOIUrl":"https://doi.org/10.1109/CCEM.2018.00017","url":null,"abstract":"Automated feedback on pronunciation system on a smart phone is useful for a student trying to learn a new language at his or her own pace. The objective of our re-search is to implement a pronunciation training system with minimal language specific data. Our proposed system consists of an Android application as a front-end, and a pronunciation evaluation and mispronunciation detection framework as the back-end hosted on a cloud. We conduct our experiments on spoken isolated words in Kannada. Our pronunciation evaluation(for spoken word) implementation on the cloud involves training a classifier with features from Dynamic Time Warping (DTW) with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequencies (LSF) and, without directly on LSF (without DTW). We study the performance of different machine learning algorithms for pronunciation rating. We propose a novel semi-supervised approach for detecting mispronounced segments of a word using Self Organizing Maps (SOM) that are also deployed on the cloud. Our implementation of SOM learns the features of an automatically segmented reference speech. The trained SOM is then used to determine the deviations in the learner's pronunciation. We evaluate our system on 1169 Kannada audio samples from students around 18 to 25 years of age. The Kannada words considered are taken from textbooks of first and second grade (considering learners as beginners who do not know Kannada) and include 2 to 5 syllable words. We report accuracy on binary classification and multi-class classification for different classifiers. The mispronounced segments detected using SOM correlate with the human ratings. Our approach of pronunciation evaluation and mispronunciation detection is based on minimal data and does not require a speech recognition system.","PeriodicalId":156315,"journal":{"name":"2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"6 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":"121280703","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

Cloud Threat Defense – A Threat Protection and Security Compliance Solution 云威胁防御-威胁防护和安全合规解决方案

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00024

D. Bharadwaj, Anamika Bhattacharya, Manivannan Chakkaravarthy

引用次数: 24

Big Data Science in Building Medical Data Classifier Using Naïve Bayes Model 使用Naïve贝叶斯模型构建医疗数据分类器的大数据科学

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00020

Kevin D'souza, Z. Ansari

{"title":"Big Data Science in Building Medical Data Classifier Using Naïve Bayes Model","authors":"Kevin D'souza, Z. Ansari","doi":"10.1109/CCEM.2018.00020","DOIUrl":"https://doi.org/10.1109/CCEM.2018.00020","url":null,"abstract":"currently, maintenance of clinical databases has become a crucial task in the medical field. The patient data consisting of various features and diagnostics related to disease should be entered with the utmost care to provide quality services. As the data stored in medical databases may contain missing values and redundant data, mining of the medical data becomes cumbersome. As it can affect the results of mining, it is essential to have good data preparation and data reduction before applying data mining algorithms. Prediction of disease becomes quick and easier if data is precise and consistent and free from noise. One of the key specialty of Naive Bayes classifiers is that they are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Evaluation of closed-form expression can be achieved by Maximum-likelihood training. Which requires linear time, rather than by expensive iterative approximation as used for many other types of classifiers. This research uses data science approach to diognize the medical data. In this article, a study has been conducted by using naïve Bayes classifier to classify the medical data. The suitability of the classifier and the accuracy of the classifier are measured using different performance criteria. This study is useful for researchers and developers in understanding and using a classification technique in medical diagnosis.","PeriodicalId":156315,"journal":{"name":"2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"173 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":"114680621","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

Cross Border Data Flow Governance in Storage Cloud Leveraging Deep Learning Techniques 利用深度学习技术的存储云跨境数据流治理

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00012

Briti Gangopadhyay, Vishal Jetla, Sandeep R. Patil, H. Pancha, K. Gildea, Carl Zetie

引用次数: 0

A System and Method for Calculating Estimated Price of any Cloud Provisioning Template 计算任何云供应模板估计价格的系统和方法

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00023

Arijit Roy, Ajoy Acharyya

引用次数: 0

Ontology Based Resource Management for IoT Deployed with SDDC 基于本体的SDDC部署物联网资源管理

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00015

Koundinya Koorapati, Prem Kumar Ramesh, S. Veeraswamy

{"title":"Ontology Based Resource Management for IoT Deployed with SDDC","authors":"Koundinya Koorapati, Prem Kumar Ramesh, S. Veeraswamy","doi":"10.1109/CCEM.2018.00015","DOIUrl":"https://doi.org/10.1109/CCEM.2018.00015","url":null,"abstract":"Resource management is a challenging issue for data centers catering to the emergent paradigm of Internet of Things (IoT). Virtualization and cloud technologies have given rise to Software-Defined Data Center (SDDC) where the main data center infrastructure viz. compute, networking, storage and security are virtualized. The provisioning and operation of the SDDC infrastructure is completely automated by software and each of the infrastructure is delivered as a service. Despite the proliferation of SDDC, the adoption of SDDC with IoT is still at its very beginning. Especially there are a lack of intelligent resource management techniques covering the end-to-end IoT and IT fabric. In an attempt to bridge this gap, we present a resource modelling framework based on semantic technologies using ontologies. To that end, the key contribution of this paper lies in being one of the first attempts in the modelling the IoT ecosystem comprising of IoT and SDDC using semantic based approaches and derive a cohesive/unified ontology. The usefulness of the cohesive ontology is demonstrated with solutions to certain problems pertinent in the Operational Technology (OT)/Information Technology (IT) convergence space and resource allocation.","PeriodicalId":156315,"journal":{"name":"2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"37 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":"131477019","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

Resource Allocation for Heterogeneous Cloud Computing Using Weighted Fair-Share Queues 基于加权公平共享队列的异构云计算资源分配

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00014

K. N. Kumar, Reshmi Mitra

{"title":"Resource Allocation for Heterogeneous Cloud Computing Using Weighted Fair-Share Queues","authors":"K. N. Kumar, Reshmi Mitra","doi":"10.1109/CCEM.2018.00014","DOIUrl":"https://doi.org/10.1109/CCEM.2018.00014","url":null,"abstract":"On-demand resource provisioning is based on automated approaches for resource pooling and elasticity on the cloud service provider (CSP) side. The infrastructure services must be adapted dynamically to accommodate customer demands and yet, operate within offerings of the CSP. Although multiple approaches for homogeneous clouds are available, more realistic platforms based on heterogeneous resources and virtual machines (VMs) present unique challenges. Our resource management algorithm allocates memory, network and computational resources to heterogeneous VMs, in order to provide customized fine-grained control for the scalable capacity planning of data centers. Weighted fair-share (WFS) queues: high, medium and low are used to classify the incoming jobs in buckets of appropriate length based on priority. The highest priority jobs with aggressive deadlines are allowed to progress at a similar pace using round-robin scheduling, while lowest priority jobs are allocated on Low Queue with First-Come-First-Serve (FCFS) scheduling. The proposed algorithm performs better on throughput related metrics: number of instructions executed (30% more), turn-around and waiting times (on an average of about 10% less) w.r.t. standard policies such as shortest job first (SJF) and FCFS.","PeriodicalId":156315,"journal":{"name":"2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"74 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":"121592705","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

An Artificial Intelligence and Cloud Based Collaborative Platform for Plant Disease Identification, Tracking and Forecasting for Farmers 基于人工智能和云的植物病害识别、跟踪和预测协同平台

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00016

K. Singh

{"title":"An Artificial Intelligence and Cloud Based Collaborative Platform for Plant Disease Identification, Tracking and Forecasting for Farmers","authors":"K. Singh","doi":"10.1109/CCEM.2018.00016","DOIUrl":"https://doi.org/10.1109/CCEM.2018.00016","url":null,"abstract":"Plant diseases are a major threat to farmers, consumers, environment and the global economy. In India alone, 35% of field crops are lost to pathogens and pests causing losses to farmers. Indiscriminate use of pesticides is also a serious health concern as many are toxic and biomagnified. These adverse effects can be avoided by early disease detection, crop surveillance and targeted treatments. Most diseases are diagnosed by agricultural experts by examining external symptoms. However, farmers have limited access to experts. Our project is the first integrated and collaborative platform for automated disease diagnosis, tracking and forecasting. Farmers can instantly and accurately identify diseases and get solutions with a mobile app by photographing affected plant parts. Real-time diagnosis is enabled using the latest Artificial Intelligence (AI) algorithms for Cloud-based image processing. The AI model continuously learns from user uploaded images and expert suggestions to enhance its accuracy. Farmers can also interact with local experts through the platform. For preventive measures, disease density maps with spread forecasting are rendered from a Cloud based repository of geo-tagged images and micro-climactic factors. A web interface allows experts to perform disease analytics with geographical visualizations. In our experiments, the AI model (CNN) was trained with large disease datasets, created with plant images self-collected from many farms over 7 months. Test images were diagnosed using the automated CNN model and the results were validated by plant pathologists. Over 95% disease identification accuracy was achieved. Our solution is a novel, scalable and accessible tool for disease management of diverse agricultural crop plants and can be deployed as a Cloud based service for farmers and experts for ecologically sustainable crop production.","PeriodicalId":156315,"journal":{"name":"2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"2 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":"133843507","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: 31

Artificial Intelligence Based Storage Management Architecture 基于人工智能的存储管理体系结构

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/CCEM.2018.00027

Rishika Kedia, Anisha Lunawat

引用次数: 3

Title Page i 第1页

2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2018-11-01 DOI: 10.1109/ccem.2018.00001

引用次数: 0